Police officer Essay

Inspector Goole has an air of mystery around him from the start. Mr. Birling is on the bench and knows all the police officers but has never seen Inspector Goole. The Inspector tells him that he is new â€Å"Only recently transferred.† This makes us wonder, is it a coincidence that he has just started working in Brumley at the same time as this investigation is going on? Or maybe he knew Eva Smith personally and wanted to find out more about what drove her to suicide. We don’t really know who he is and there is no evidence that he is a real Inspector so the audience are confused whether to believe him or not. We do not know enough about his character to trust him. He also has a very mysterious nature for example the way he talks to people. â€Å"He chooses his words carefully and speaks weightily.† He does this so that he makes sure that he is saying what he wants to say and means it. Everything he says has a rather matter of fact tome to it making the other characters think that he is always right and knows what he is talking about. Also he has a disconcerting habit of looking at the person that he is talking to. This makes them uneasy. It is like the Inspector is searching them internally and can see what they are thinking. The name of the Inspector â€Å"Inspector Goole† adds to the dramatic tension of the play. The name Goole is a homophone to the word ghoul which is often related to a ghost. A ghost is a disembodied spirit of a dead person i.e. it is not really there. Ghosts and ghouls are thought to be fictional to scare children. Therefore is the Inspector real or not? Is â€Å"Goole† his real name or is he just using the name to scare the Birlings? These questions remain unanswered throughout the play. Inspector Goole has a very awkward yet methodical way of questioning each member of the family. He concentrates on one person at a time and does not let them go until he is satisfied with the information that he has gained. When he is questioning one person he does not like to disturbed. He has a photograph in his pocket which he says is of Eva Smith and he says he found it in her lodgings. He only shows it to one person at a time and makes sure that no one else can see it. When he shows Mr. Birling the photograph Gerald and Eric are eager to see it as well but he blocks their view. When they ask him why they can’t see the photograph he replies â€Å"It’s the way I like to go to work. One person and one line of enquiry at a time. Otherwise there is a muddle.† This shows the methodical side to his questioning. Throughout his enquiries, the Inspector has remained in control. At times he has massively taken charge.† In social status, the Inspector is lower than the Birlings although throughout most of the play the Birlings are inferior to the Inspector as they do not know all the information like he does. What we do not know is how much the inspector already knows before arriving at the house. Whenever he asks a question, he already seems to know the otherwise he would not be questioning them. The information he has gathered before coming to the house is by reading the diary that Eva Smith left. Maybe some pages of the diary were missing, hence the lack of information in the Inspector. There is not really any evidence that what the inspector says is true apart from everyone admitting to what they did to Eva Smith. He doesn’t even show the Birlings so called diary. Therefore we aren’t sure if there even was a diary or if he made it all up. We are force to believe that he is telling the truth because how else would he have got that information? The audience answer to it â€Å"Anyhow I already knew.† He obviously has bits of the puzzle missing do not learn a great about Inspector Goole in the play. All they learn about is his character and personality; he is imposing and intimidating. We do not know any background information on him e.g. where he is from. All this does not matter because even though he is the main character the play is not about him it is about Eva Smith. The audience probably do feel a bit curious about the mysterious Inspector but their curiosity soon subsides as they try to keep up with the fast pace of the play. Before the Inspector goes he says â€Å"One Eva Smith has gone but there are millions and millions of Eva Smiths left with us.† From this, the family learn that they might have led someone to suicide but there are plenty more vulnerable people like Eva Smith left in the world that needs their help.  John Priestley was born on September 13th 1894. He grew up into a family who enjoyed debates. At a very young age he was caught up in debates with his friends. They all discussed politics from a socialist point of view. He expresses these ideas and beliefs in the play. In the play his socialist ideas are expressed through the characters and by exploiting some techniques and devices. Now I am going to write about the social and political climate of the day. Priestley wrote this play ‘An Inspector Calls’ in 1944, but the play was set in 1912. Priestley took into account the political and social climate of not just 1944 but also when the play was set in 1912. Because of the time in between the two periods Priestley could use historical events and facts for his own use in the play. An example of this is when Birling says, â€Å"I’m talking as a hard headed, practical man of business. And I say there isn’t a chance of war. â€Å"And in the Inspectors final speech when he talks about everyone being responsible for each other’s actions and the world is a whole and people shouldn’t be divided up into classes and social groups. â€Å"I tell you that the time will come when, if men will not learn that lesson, then they will be taught in fire and blood and anguish.† In both these quotes Priestley touches on the subject of war. This would have attracted the audience to what Birling was saying and they would have started to dislike him and his ideas, since the Second World War would have just finished the audience would have been upset about their lost ones and would have been interested in peoples opinions and views leading up to the start of World War One.

Real-Time Fraud Detection: How Stream Computing Can Help the Retail Banking Industry

Para os meus pais, porque â€Å"o valor das coisas nao esta no tempo que elas duram, mas na intensidade com que acontecem. Por isso existem momentos inesqueciveis, coisas inexplicaveis e pessoas incomparaveis† como voces! Obrigado por tudo, Filipe Abstract The Retail Banking Industry has been severely affected by fraud over the past few years. Indeed, despite all the research and systems available, fraudsters have been able to outsmart and deceive the banks and their customers. With this in mind, we intend to introduce a novel and multi-purpose technology known as Stream Computing, as the basis for a Fraud Detection solution.Indeed, we believe that this architecture will stimulate research, and more importantly organizations, to invest in Analytics and Statistical Fraud-Scoring to be used in conjunction with the already in-place preventive techniques. Therefore, in this research we explore different strategies to build a Streambased Fraud Detection solution, using advanced Dat a Mining Algorithms and Statistical Analysis, and show how they lead to increased accuracy in the detection of fraud by at least 78% in our reference dataset. We also discuss how a combination of these strategies can be embedded in a Stream-based application to detect fraud in real-time.From this perspective, our experiments lead to an average processing time of 111,702ms per transaction, while strategies to further improve the performance are discussed. Keywords: Fraud Detection, Stream Computing, Real-Time Analysis, Fraud, Data Mining, Retail Banking Industry, Data Preprocessing, Data Classi? cation, Behavior-based Models, Supervised Analysis, Semi-supervised Analysis Sammanfattning Privatbankerna har drabbats hart av bedragerier de senaste aren. Bedragare har lyckats kringga forskning och tillgangliga system och lura bankerna och deras kunder.Darfor vill vi infora en ny, polyvalent strommande datorteknik (Stream Computing) for att upptacka bedragerier. Vi tror att denna struktur kommer att stimulera forskningen, och framfor allt fa organisationerna att investera i analytisk och statistisk bedragerisparning som kan anvandas tillsammans med be? ntlig forebyggande teknik. Vi undersoker i var forskning olika strategier for att skapa en strommande losning som utnyttjar avancerade algoritmer for datautvinning och statistisk analys for att upptacka bedragerier, och visar att dessa okar traffsakerheten for att upptacka bedragerier med minst 78% i var referensbas.Vi diskuterar aven hur en kombination av dessa strategier kan baddas in i en strommande applikation for att upptacka bedragerier i realtid. Vara forsok ger en genomsnittlig bearbetningstid pa 111,702ms per transaktion, samtidigt som olika strategier for att fortsatta forbattra resultaten diskuteras. Acknowledgments â€Å"Silent gratitude isn’t much use to anyone† Gladys Bronwyn Stern When I wrote the ? rst words in this report I think I had no idea what a Master Thesis is about!I can’t blame myself though since I never wrote one before, but if you ask me now to describe this experience I would say that it’s like a road trip: you set yourself a destination, you have a loyal crew that is always there for you, a roadmap, supporters on the side and then the journey begins. Within the latter, you face setbacks with the help of others, you share knowledge, you meet new people and most importantly you get to know them†¦ This journey would not have been possible without the support, camaraderie and guidance of many friends, colleagues and my family.For all these reasons, I couldn’t let the journey end without expressing my gratitude to each and everyone of them. First and foremost, I would like to express my sincere gratitude to my supervisor, Philippe Spaas, who made it possible for me to work in this project under his supervision and in collaboration with IBM. It was a privilege to work alongside with him and a unique learning opportunity for me! I am indebted for his precious guidance and for the time dedicated not only in helping me understand how a research paper should be formulated, but also in reviewing the latter.Thank you! I am very thankful as well to Tybra Arthur, who graciously accepted me in her team and supported my internship, Jean de Canniere who accepted to be my Manager and without whom I wouldn’t have had this opportunity. In this line of thought, I am also grateful to Hans Van Mingroot who helped me secure this project in its negotiation phase. All three were key elements, and their support and guidance throughout the research were important to me and very much appreciated.I would also like to express my gratitude to Professor Mihhail Matskin at KTH – the Royal Institute of Technology – for having accepted this Master Thesis and for being my examiner. His insights and help were invaluable to achieve more sound end results and put together this ? nal report! In addition, I would like to ext end my personal thanks to my Erasmus Coordinator, Anna Hellberg Gustafsson, for her support, kindness and dedication for the duration of the research which was key to the organization of the latter.She is, for me, the best coordinator I have met and heard about! I would probably not have taken the appropriate steps to have this opportunity within IBM if it weren’t for the initial support and guidance of Karl De Backer, Anika Hallier, Anton Wilsens and last but not least Parmjeet Kaur Gurmeet. I truly value their follow-up both on the research and on my experience! On a special note I would like to thank Parmjeet for having been always a good mentor to me and for her support and trust ever since the Extreme Blue internship.I want to thank each IBMer with whom I came in contact with in the Financial Services Sector Department for welcoming me into their working environment and for making my stay very enjoyable. In addition to the aforementioned IBMers, among many others and in no speci? c order I would like to thank Daniel Pauwels, Patrick Taymans, Hedwige Meunier, Gauthier de Villenfagne, Michel Van Der Poorten, Kjell Fastre, Annie Magnus, Wouter Denayer, Patrick Antonis, Sara Ramakers, Marc Ledeganck, Joel Van Rossem and Stephane Massonet. It was a real pleasure to share the open space and, more importantly, to meet them!Dan Gutfreund at IBM Haifa was a key element in the development of this thesis. I am very thankful for the discussions we had about Fraud Detection and for his advice in the different phases that compose this research. In addition, I would like to extend my thanks to Jean-Luc Collet at IBM La Gaude for his valuable help in obtaining a stable virtual machine with InfoSphere Streams. I am thankful to Professor Gianluca Bontempi and Liran Lerman at Universite Libre de Bruxelles for ? nding the time to discuss about Fraud Detection and Data Mining techniques.Their insights were vital for the development of the prototype and the overall rese arch. On the same vein, I would like to thank Chris Howard at IBM Dublin for his help in understanding Stream Computing and InfoSphere Streams. His guidance was crucial for a timely comprehension of the ? eld without which I wouldn’t have been able to develop the prototype. I want to thank Mike Koranda and John Thorson at IBM Rochester for their help in understanding the integration of Data Mining and Stream Computing and how to achieve the latter in a more ef? cient manner.I really appreciated their help with the prototype, especially when atypical errors occurred to more quickly detect the source of the problem. I am also thankful to IBM, as a company, for providing me the opportunity and necessary facilities to conduct my thesis project, as well as to KTH, as university, for having allowed me to take on this experience. I want to take this opportunity to thank my friend, Thomas Heselmans, for having been there ever since the beginning of the research despite my busy agenda . His support and concern were vital in times of great stress and trouble, thank you for your friendship!The same applies to Stephane Fernandes Medeiros, a great friend of mine who was always there for me and followed my work very closely. In addition, I am thankful to two of my greatest friends, Nicola Martins and Alberto Cecilio, for their friendship, for always supporting me and always having my back. Margarida Cesar is a very important person in my life, and I would like to express my gratitude for all the discussions and advice we shared, as well as for the support demonstrated ever since we met. I always take her advice very seriously and she has helped me cope with dif? ulties in more than one occasion, namely during the thesis, and for that I’m very thankful! I am also very grateful to my friend, Arminda Barata, for all the help she provided me in moving and adapting myself to Stockholm. Without her help and concern I wouldn’t have felt at home so easily, and I wouldn’t have liked Stockholm from the very ? rst day. I would like to take advantage of this opportunity to thank all my colleagues and friends in Stockholm for making these two years of study unforgettable, and for shaping the person I am today.Among so many others, I would like to thank in particular Sanja Jankolovska, Boshko Zerajik, Pedram Mobedi, Adrien Dulac, Filipe Rebello De Andrade, Pavel Podkopajev, Cuneyt Caliskan, Sina Molazem, Arezoo Ghannadian and Hooman Peiro. I couldn’t have made it through without all of them! Last but de? nitely not least, because I didn’t have the chance to formally thank my friends in my previous studies, I would like to take this opportunity to extend my thanks to them for all the good moments we spent together throughout our bachelor degree as well as today.In particular I would like to thank Miruna Valcu, Rukiye Akgun, Vladimir Svoboda, Antonio Paolillo, Tony Dusenge, Olivier Sputael, Aurelien Gillet, Mathieu Duchene, Br uno Cats, Nicolas Degroot and Juraj Grivna. I reserve a special thank you note to Mathieu Stennier, for both his friendship and support throughout my academic life, and for having shared with me what were the best moments I had in Brussels while at University!I would very much like to express myself in Portuguese to my family so that they can all more easily understand what I have to say, thank you for your understanding: Nao podia deixar de agradecer a toda a minha familia o apoio que demonstraram ao longo deste percurso academico que conhece hoje um novo capitulo. Gostaria de agradecer a todos sem excepcao por acreditarem em mim e nunca duvidarem das minhas capacidades. Obrigado por estarem sempre presentes apesar da distancia, obrigado por se preocuparem comigo e por fazerem com que eu saiba que poderei sempre contar com voces!Sou verdadeiramente um ser afortunado por poder escrever estas palavras†¦ Um obrigado especial a minha grande avo Olga por estar sempre disposta a sac ri? car-se por nos e por telefonar quase diariamente a perguntar se estou bem e se preciso de alguma coisa. Agradeco-lhe do fundo do coracao esse amor que tem pelos netos e que tanta forca transmite! Queria agradecer tambem aos meus primos Rui e Hugo, que sao para mim como os irmaos que eu nunca tive, a forca que me transmitem para seguir em frente face as adversidades da vida. Ambos ensinaram-me imenso durante toda a vida e sao uma fonte de inspiracao constante para mim!A admiracao que tenho por eles foi como um guia que me levou onde estou hoje†¦ Obrigado por acreditarem em mim para levar a bom porto este projecto e por terem estado sempre presentes a apoiar-me! Gostaria de deixar uma mensagem de apreco ao David, que e mais do que um primo para mim, e um melhor amigo, que sempre esteve presente e sempre se preocupou comigo durante a tese. Foram momentos, frases e situacoes da vida que ? zeram com que o David se tornasse na pessoa importante que e para mim e ao longo da tese a s suas mensagens de apoio foram sempre bem recebidas porque deram-me um alento enorme.Aproveito tambem para agradecer a minha querida tia Aida e ao meu estimado primo Xico pela preocupacao que tem sempre comigo e por serem uma fonte de inspiracao para mim. Desejo tambem aproveitar esta oportunidade para agradecer a Nandinha e Jorginho todo o apoio que me deram nao so durante estes 6 longos meses mas desde os meus primeiros passos. Sao como uns segundos pais para mim cujo apoio ao longo deste curso e capitulo da minha vida foi primordial. Agradeco, do fundo do coracao, o facto de me tratarem como se fosse um ? lho, por me guiarem e sempre ajudarem! Tenho ainda um lugar especial reservado para o meu tio Antonio.Um tio que admiro muito, que sempre me quis bem e cujo dom da palavra move montanhas! O seu conselho e para mim uma maisvalia, e agradeco todo o seu apoio e ajuda durante esta investigacao e sobretudo por me guiar quando nao ha estrelas no ceu. Aproveito para vos deixar a todos um pedido de desculpa por nao estar presente como gostaria, e agradeco o facto de que apesar de tudo voces estejam todos de pe ? rme atras de mim! Sem o vosso apoio nunca teria feito metade do que ? z! Costuma-se guardar o melhor para o ? m, e por isso nao podia deixar de agradecer aos meus pais tudo o que ? eram e fazem por mim! A lingua de Camoes e escassa para que eu consiga descrever o quao grato estou†¦ Dedico-vos esta tese, por sempre me terem dado todo o amor, carinho, e ajuda necessaria para ter uma vida feliz e de sucesso. Deixo aqui um grande e sentido obrigado por terem estado sempre presentes quando mais precisava, por me terem sempre apoiado a alcancar os meus objectivos, por me terem ensinado a viver, a amar, a partilhar e a ser a pessoa que sou hoje. Obrigado! Em particular gostaria de agradecer ao meu pai a compreensao que teve comigo durante este periodo mais ocupado.Agradecer-lhe a ajuda em conseguir por um meio termo as coisas e a olhar para elas de outro pr isma. Agradeco tambem a calma que me transmitiu e transmite, e o apaziguamento que me ensinou a ter face as adversidades da vida. Sem estas licoes de vida, que guardarei sempre comigo, sinto que a tese nao teria sido bem sucedida e eu nunca teria alcancado tudo o que alcancei! A minha mae, agradeco†¦ por onde hei-de comecar? Pela ajuda diaria durante a tese para que os meus esforcos se concentrassem no trabalho? Pela inspiracao diaria de um espirito lutador que nao desmorona face as di? culdades e injusticas da vida?Agradeco por tudo isto e muito mais pois sem a sua ajuda diaria nao teria conseguido acabar a tese. A admiracao que tenho pela sua forca e coragem ? zeram com que eu tentasse seguir os mesmos passos e levaram-me a alcancar patamares que considerava inalcancaveis! A paciencia que teve durante todo o projecto, mas sobretudo no ? m, e de louvar, e sem o seu ombro amigo teria sido tudo muito mais complicado. Obrigado a todos por tudo! Thank you all for everything! Filip e Miguel Goncalves de Almeida Table of Contents 1 Introduction Part I: Setting the Scene 2 Retail Banking and The State of the Art in Detection and Prevention of Fraud 2. The Retail Banking Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. 1. 1 A Short Walk Down Memory Lane . . . . . . . . . . . . . . . . . . . . 2. 1. 2 The Retail Banking IT Systems’ Architecture . . . . . . . . . . . . . . 2. 2 Fraud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. 2. 1 Internet and E-Commerce Fraud . . . . . . . . . . . . . . . . . . . . . 2. 2. 2 Other Consumer Fraud . . . . . . . . . . . . . . . . . . . . . . . . . . 2. 3 Current Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. 3. 1 Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. 3. 2 Analytics and Statistical Fraud-Scoring . . . . . . . . . . . . . . . . . 3 Problem De? nition 3. 1 Weak Links in Currently Available Solutio ns . 3. 1. 1 Bank Card and Pin Code . . . . . . . . . 3. 1. 2 One-Time-Password or Card Reader . . 3. 1. 3 Biometrics . . . . . . . . . . . . . . . . . 3. 1. 4 Analytics and Statistical Fraud-Scoring 3. 2 Facts and Figures . . . . . . . . . . . . . . . . . 3. 2. 1 France . . . . . . . . . . . . . . . . . . . 3. 2. 2 United Kingdom . . . . . . . . . . . . . 3. 3 E-Commerce and Internet Banking . . . . . . . 3. 4 Mobile Banking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 3 3 3 4 6 6 12 12 13 14 15 15 16 17 18 18 19 19 19 20 21 22 22 23 23 23 24 24 25 25 28 28 29 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 31 31 31 32 32 33 34 Research Methodology 4. 1 Objective of the Research . . . . . . . . . . . . . . . 4. 2 Data Collection . . . . . . . . . . . . . . . . . . . . 4. 2. 1 FICO’s E-Commerce Transactions Dataset . 4. 2. 2 Personal Retail Bank Transacti ons . . . . . 4. 3 Data Analysis Plan . . . . . . . . . . . . . . . . . . 4. 3. 1 Partitioning of the Data . . . . . . . . . . . 4. 4 Instruments and Implementation Strategy . . . . . 4. 4. 1 InfoSphere Streams . . . . . . . . . . . . . . 4. 4. 2 SPSS Modeler . . . . . . . . . . . . . . . . . 4. 4. 3 MySQL Database . . . . . . . . . . . . . . . Part II: Behind the Curtains 5 Phase 0: Data Preprocessing 5. Getting to Know the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1. 1 Attributes and their Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1. 2 Attributes in the Retail Banking Industry and in FICO’s Dataset . . . . . . 5. 1. 3 Statistical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 2 Data Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 2. 1 Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 2. 2 Supervised Merge and Transformat ion of Nominal and Categorical Data . 5. 3 5. 4 5. 5 5. 6 . 7 5. 8 Cleaning Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3. 1 Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3. 2 Noisy Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 4. 1 Transformation of Times and Dates . . . . . . . . . . . . . . . . . 5. 4. 2 Transformation by Normalization . . . . . . . . . . . . . . . . . . Sampling Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 5. 1 Clustering using K-Means Algorithm . . . . . . . . . . . . . . . 5. 5. 2 Under-Sampling Based on Clustering . . . . . . . . . . . . . . . . Preprocessing Data with Stream Computing . . . . . . . . . . . . . . . . 5. 6. 1 Receiving and Sending Streams of Transactions . . . . . . . . . . 5. 6. 2 Retrieving and Storing Data to a Database . . . . . . . . . . . . . 5. 6. 3 Data Preprocessing using SPSS Solution Publisher . . . . . . . . . 5. 6. 4 Data Preprocessing using a Non-Generic C++ Primitive Operator Rule-Based Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 7. 1 Streams with a Business Rules Management System . . . . . . . . Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 36 36 37 37 37 39 40 41 42 42 43 45 45 46 48 49 50 51 51 52 53 53 54 55 56 57 57 58 60 60 61 62 62 63 63 66 71 71 73 76 77 6 Phase I: Data Classi? cation 6. 1 Supervised Learning . . . . . . . . . . . . . . . . . . . 6. 1. 1 Ensemble-Based Classi? er . . . . . . . . . . . . 6. 2 Classi? cation Algorithms . . . . . . . . . . . . . . . . 6. 2. 1 Support V ector Machines . . . . . . . . . . . . 6. 2. 2 Bayesian Networks . . . . . . . . . . . . . . . . 6. 2. 3 K-Nearest Neighbors (KNN) . . . . . . . . . . 6. 2. 4 C5. 0 Decision Tree . . . . . . . . . . . . . . . . 6. 3 Classi? cation using the Data Mining Toolkit . . . . 6. 3. 1 Weaknesses of the Approach . . . . . . . . . . 6. 4 Classi? cation using SPSS Modeler Solution Publisher 6. 4. 1 Implementation Details . . . . . . . . . . . . . 6. 5 Model Retraining Architecture: High Level Overview 6. 6 Final Thoughts . . . . . . . . . . . . . . . . . . . . . . 7 Phase II: Anomaly Detection and Stream Analysis 7. 1 Data Aggregation . . . . . . . . . . . . . . . . . . . . 7. 2 Bank Customers Aggregation Strategy . . . . . . . . 7. 3 Anomaly Detection . . . . . . . . . . . . . . . . . . . 7. 3. 1 Techniques for Anomaly Detection . . . . . . 7. 3. 2 Mahalanobis Distance . . . . . . . . . . . . 7. 4 Stream Analysis . . . . . . . . . . . . . . . . . . . . . 7. 4. 1 Window-Based Operators . . . . . . . . . . . 7. 4. 2 Window-Based Anomaly Detection Strategy 7. 5 Final Thoughts . . . . . . . . . . . . . . . . . . . . . Part III: Critical Review 8 Overall Evaluation 8. 1 Performance Measurement Techniques . . . . . . . . . 8. 1. 1 Performance Metrics . . . . . . . . . . . . . . . 8. 1. 2 Accuracy Levels . . . . . . . . . . . . . . . . . 8. 2 Data Preprocessing and Business Rules Analysis . . . 8. 3 Data Classi? cation . . . . . . . . . . . . . . . . . . . . 8. 3. 1 Un-preprocessed Classi? er Analysis . . . . . . 8. . 2 Preprocessed Un-Sampled Classi? er Analysis 8. 3. 3 Preprocessed Sampled Classi? er Analysis . . . 8. 3. 4 Ensemble-Based Classi? er Analysis . . . . . . 8. 4 Anomaly Detection . . . . . . . . . . . . . . . . . . . . 8. 5 Overall Concept . . . . . . . . . . . . . . . . . . . . . . 8. 6 Future Work . . . . . . . . . . . . . . . . . . . . . . . . 8. 6. 1 Extend Services . . . . . . . . . . . . . . . . . . 8. 6. 2 eXtreme Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 78 78 79 80 80 81 83 84 87 88 89 90 91 92 i 8. 7 8. 6. 3 Architecture and Data Mining Algorithms . . . . . . . . . . . . . . . . . . . . . . . Final Thoughts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 94 95 i vi 9 Conclusion Appendix A Supporing Figures Glossary List of Figures Figure 1. 1 Figure 2. 1 Figure 2. 2 Figure 2. 3 Figure 2. 4 Figure 2. 5 Figure 2. 6 Figure 2. 7 Figure 2. 8 Figure 2. 9 Figure 3. 1 Figure 3. 2 Figure 3. 3 Figure 3. 4 Figure 3. 5 Figure 3. 6 Figure 3. 7 Figure 3. 8 Figure 4. 1 Figure 4. 2 Figure 4. 3 Figure 4. 4 Figure 4. 5 Figure 4. 6 Figure 4. 7 Figure 5. 1 Figure 5. 2 Figure 5. 3 Figure 5. 4 Figure 5. 5 Figure 5. 6 Figure 5. 7 Figure 5. 8 Figure 5. 9 Figure 5. 10 Figure 5. 11 Figure 5. 12 Figure 5. 13 Figure 5. 14 Figure 5. 15 Figure 6. Figure 6. 2 Figure 6. 3 Figure 6. 4 Figure 6. 5 Lost in Translation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . As-Is Banking IT Architecture . . . . . . . . . . Hype Cycle for Application Architecture, 2009 To-Be Banking IT Reference Architecture . . . . MitB Operation . . . . . . . . . . . . . . . . . . Possible Paypal website (1) . . . . . . . . . . . Possible Paypal website (2) . . . . . . . . . . . Keyboard State Table method . . . . . . . . . . Windows Keyboard Hook method . . . . . . . Kernel-Based Keyboard Filter Driver met hod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 5 5 5 8 10 10 11 11 11 16 16 17 20 20 20 21 21 24 25 26 26 27 28 29 30 32 32 33 34 35 35 36 37 40 40 42 45 46 48 50 51 52 53 54 Components of the Chip and Pin Attack . . . . . . . . . . . . Attack to Card Illustrated . . . . . . . . . . . . . . . . . . . . One-Time-Password Hacking Material and Architecture . Number of European Internet Users and Online Purchasers Forecast: US Online Retail Forecast, 2010 to 2015 . . . . . . . Web Growth has Outpaced Non-Web Growth for Years . . . US Mobile Bankers, 2008-2015 . . . . . . . . . . . . . . . . . US Mobile Banking Adoption . . . . . . . . . . . . . . . . . . CRoss-Industry Standard Process for Data Mining . . . . . . . . . Streams Programming Model . . . . . . . . . . . . . . . . . . . . . â€Å"Straight-through† processing of messages with optional storage. Backup and Fail-Over System for Streams . . . . . . . . . . . . . . Multiple-Machines Architecture . . . . . . . . . . . . . . . . . . Analytical and Business In telligent Platforms Compared . . . . . Global Flow of Events: Stream-Based Fraud Detection Solution . Overall SPSS Modeler Stream for the Of? ine Data Preprocessing Phase Frequency of Transactions per Hour . . . . . . . . . . . . . . . . . . . . Amount Transferred per Transaction . . . . . . . . . . . . . . . . . . . . Data Feature Selection in SPSS . . . . . . . . . . . . . . . . . . . . . . . Data Preparation Preprocessing Phase in SPSS . . . . . . . . . . . . . . SPSS Stream CHAID Tree Model . . . . . . . . . . . . . . . . . . . . . . CHAID Tree for Data Reduction . . . . . . . . . . . . . . . . . . . . . Filtering Null Values with SPSS . . . . . . . . . . . . . . . . . . . . . . . Cyclic Values of Attribute hour1 . . . . . . . . . . . . . . . . . . . . . . K-Means Modeling in SPSS . . . . . . . . . . . . . . . . . . . . . . . . . Clustering with K-Means in SPSS Modeler . . . . . . . . . . . . . . . . Stream-based Application: Data Preprocessing and Rule-Based Engine Stream-based Application: Data Preprocessing . . . . . . . . . . . . . . Stream-based Application: Rule-Based Engine . . . . . . . . . . . . . . Interaction Between a BRMS and a Stream-based Application . . . . . Classi? cation in Stream-Based Application .Ensemble-Based Classi? er . . . . . . . . . . . Classi? cation in SPSS . . . . . . . . . . . . . . Support Vector Machines (SVMs) Illustrated Example of a Bayesian Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i Figure 6. 6 Figure 6. 7 Figure 6. 8 Figure 7. 1 Figure 7. 2 Figure 7. 3 Figure 7. 4 Figure 7. 5 Figure 7. 6 Figure 7. 7 Figure 7. 8 Figure 7. 9 Figure 7. 10 Figure 7. 11 Figure 7. 12 Figure 7. 13 Figure 8. 1 Figure 8. 2 Figure 8. 3 Figure 8. 4 Figure 8. 5 Figure 8. 6 Figure 8. 7 Figure 8. 8 Figure 8. Figure A. 1 Figure A. 2 Figure A. 3 Figure A. 4 Figure A. 5 Figure A. 6 K-Nearest Neighbors Illustrated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Section of C5. 0 Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SPSS C&DS: Classi? er Retraining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anomaly Detection Stream-based Application . . . . . . . . . . . . Aggregate Bank Customers . . . . . . . . . . . . . . . . . . . . . . . Learning a classi? er model for the normal class of transactions . . Transaction not belonging to a cluster . . . . . . . . . . . . . . . . .Transactions far from the clusters’ center . . . . . . . . . . . . . . . Mahalanobis Distance Illustrated . . . . . . . . . . . . . . . . . . . . Mahalanobis Distance: Stream-based Application . . . . . . . . . . Window Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tumbling Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . Sliding Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Partitioned Keyword . . . . . . . . . . . . . . . . . . . . . . . . . . . Account average expenses and frequency of transactions in 3 days Window-Based Analysis: Stream-based Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 55 60 61 63 64 65 65 66 67 71 71 72 73 73 74 78 79 84 86 88 89 92 92 94 ii iii iii iv iv v Benchmarking Stream-based Application: Concept for Each Processing Step . . Confusion Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison between Un-Preprocessed and Preprocessed Data: Accuracy Levels Comparison between Sampled Datasets: Accuracy Levels (TP/FP) . . . . . . . Stream Analysis: Debited Account . . . . . . . . . . . . . . . . . . . . . . . . . . . Overall View of the Solution: Accuracy Levels (TP/FP/FN) . . . . . . . . . . . . Overall St ructure of the Financial Services Toolkit . . . . . . . . . . . . . . . . . . In-Memory Database with InfoSphere Streams . . . . . . . . . . . . . . . . . . . . Stream-Based Application: a Flexible and Multifaceted Architecture . . . . . . . Stream-based Application: Overview . . . . . . . . . . . . . . . . . . . . . . . . . . Time per Transaction for each of the Data Preprocessing Approaches . . . . . . . Time per Transaction for Preprocessing the Data and Examine the Business Rules . Metrics Data Classi? cation Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anomaly Detection Time per Transaction . . . . . . . . . . . . . . . . . . . . . . . . Fraud Detection: Time per Transaction . . . . . . . . . . . . . . . . . . . . . . . . . List of Tables Table 3. 1 Table 5. 1 Table 5. 2 Table 6. 1 Table 7. 1 Table 8. 1 Table 8. 2 Table 8. 3 Table 8. 4 Table 8. 5 Table 8. 6 Table 8. 7 Table 8. 8 National fraud in France categorized by transaction type . . . . . . . . . . . . . . . Communalities PCA/Factor Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . Steps for Under-Sampling Based on Clustering (SBC) . . . . . . . . . . . . . . . . . . Supported Mining Algorithms: Data Mining Toolkit . . . . . . . . . . . . . . . . . . Hardware Speci? cation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Individual Classi? er Accuracy Levels – Un-Preprocessed Training Set . . . . . Individual Classi? er Accuracy Levels – Un-Sampled Preprocessed Training Set Multiple Sampling Ratios Analyzed . . . . . . . . . . . . . . . . . . . . . . . . . Multiple Sampling Ratios Analyzed . . . . . . . . . . . . . . . . . . . . . . . . . Ensemble-Based Classi? r: Balanced . . . . . . . . . . . . . . . . . . . . . . . . Ensemble-Based Classi? er: Maximum Fraud Detection . . . . . . . . . . . . . Ensemble-Based Classi? er with Mahalanobis: Balanced Model Combination . Ensemble-Based Classi? er with Mahalanobis: Maximizing Fraud Detectio n . . . . . . . . . . . . . . . . . . . . . . . . . 19 34 41 56 77 81 83 85 85 87 87 89 89 List of Algorithms Algorithm 1 Algorithm 2 Algorithm 3 Algorithm 4 Algorithm 5 Algorithm 6 Algorithm 7 Algorithm 8 Algorithm 9 Algorithm 10 Algorithm 11 Algorithm 12 Algorithm 13 Algorithm 14 InputSource: Receive Incomming Transactions . . . . . . . . . . . . . . . . ODBCEnrich: Enrich an Incomming Transaction . . . . . . . . . . . . . . . . Non-Generic C++ Primitive Operator: Manual Preprocessing . . . . . . . . . Preprocessing: Manual Preprocessing of Incoming Transactions . . . . . . . Functor: Split Stream for Preprocessing and Rule-Based Engine . . . . . . . . Join: Append Business Rules to Preprocessed Transaction . . . . . . . . . . . Join: Append Business Rules to Preprocessed Transaction . . . . . . . . . . . Data Mining Toolkit Operator: Decision Tree C5. 0 Classi? er . . . . . . . . . . Non-Generic C++ Primitive Operator: Supervised Analysis . . . . . . . . . . Classi? cationEnsembl e: Constructor() . . . . . . . . . . . . . . . . . . . . . . Classi? cation Ensemble: process(Tuple & tp, uint32_t port) . . . . . . . . . . Variance-Covariance Inverse Matrix used in the Mahalanobis Distance . . . Individual Account Anomaly Detection Approach . . . . . . . . . . . . . . . Voting Protocol: Mahalanobis Distance, Window-Based and Classi? er Score . . . . . . . . . . . . . . 43 44 45 46 47 47 47 56 58 58 59 68 75 75 Chapter 1 Introduction â€Å"A journey of a thousand miles must begin with a single step† Lao Tzu â€Å"If you work on fraud detection, you have a job for life†. These were the words used by Professor David J.Hand1 in one of his talks to synthesize the vast research ? eld that is Fraud Detection. Indeed, this ? eld consists of multiple domains, and is continually evolving through time with new strategies and algorithms to counter the constantly changing tactics employed by fraudsters2 . In this line of thought, currently available solutions ha ve been unable to control or mitigate the everincreasing fraud-related losses. Although thorough research has been done, only a small number of studies have led to actual Fraud Detection systems [27], and the focus is typically on novel algorithms aiming at increasing the accuracy levels.To this end, we want to look at the problem from a different angle, and focus on the foundations for a real-time and multi-purpose solution, based on a technology known as Stream Computing, able to encompass these algorithms while creating the possibilities for further research. We subdivide our study in three main parts. We begin with an overall understanding of the topic being discussed by de? ning the research environment, its problems and presenting the solutions currently available. In addition, we conclude this ? rst part by both specifying the structure, and outlining the objective of the research.The second part explores the overall course of action to bring about a Stream-based Fraud Detect ion solution. From this perspective, we discuss different strategies previously researched in Data Preprocessing, Data Classi? cation and Behavior-based Analysis, and tackle their combination and integration in a Stream-based application. Last but not least, we review the overall solution proposed, and examine the possibilities offered by the latter for further research in the ? eld of Fraud Detection in the Retail Banking Industry. Senior Research Investigator and Emeritus Professor of Mathematics at the Imperial College of London, and one of the leading researchers in the ? eld of Fraud Detection – http://www3. imperial. ac. uk/people/d. j. hand – link to the presentation: http://videolectures. net/mmdss07_hand_stf/ 2 a person intended to deceive others (i. e. one who commits fraud) [de? ned in the Glossary] 1 Part I: Setting the Scene â€Å"Great things are not done by impulse, but by a series of small things brought together† Vincent van Gogh Fraud Detection in itself is interlinked with numerous ? lds of study, and before the play’s main action, we want to set the stage. In order to avoid getting off track and allowing you to better understand the scope, contents, choices made, and requirements of the research, we divided this act in three scenes. In the ? rst, we introduce the main actors – namely banks, bank customers and fraudsters. In addition, we also present the current situation in the Detection and Prevention of Fraud in banks, describing the techniques being used both to counter and to commit fraudulent transactions. The second scene introduces the overall problem of fraud in the Banking Sector.It identi? es the weaknesses of the latest solutions, and quanti? es fraud losses as accurately as possible in some European countries and this based on the most recent data. We then take a step further and comment on new trends, and predict possible risks banks might incur from them. Before the end of the act, we introdu ce the two main parts of the play, as well as how we intend to approach the problem. More precisely, we provide some speci? cs regarding the research conducted, the tools used and the plan followed to reach our conclusions. Figure 1. : Lost in Translation 2 Chapter 2 Retail Banking and The State of the Art in Detection and Prevention of Fraud â€Å"There are things known and there are things unknown, and in between are the doors of perception† Aldous Huxley Businessmen and politicians, before sealing deals or taking political decisions, are known to go through a phase of reconnaissance – the military term for exploring enemy or unknown territory. Just as it is important to them, so it is for you when you are about to dive into the speci? cs of a real-time fraud detection solution.In this line of thought, it is important to grasp the context of the research to better understand the concepts discussed. To do so, we start this chapter with an overall view of the Retail Ba nking Industry, to understand both its services and IT architecture (Section 2. 1); we continue with a de? nition of fraud together with a description of the different fraud types that affect banks and how they operate (Section 2. 2); lastly, we give an overview of some of the current solutions available (Section 2. 3). 2. 1 The Retail Banking Industry To describe the banking industry’s evolution that started earlier than 2000 B.C. [91], deserves almost a research paper on its own. For this reason, and because we don’t want to divert from the topic, we start by solely providing a simple and brief resume about the origins of the banking industry (Section 2. 1. 1). The latter is an interesting talking point that not only allows you to understand how it all started, but also to perceive the challenge of keeping a bank pro? table. Additionally, it is a good introduction to understand a more technical description of the IT architecture behind the banking services (Section 2 . 1. 2). 2. 1. 1 A Short Walk Down Memory LaneIt all started with barter back in the time of Dravidian India, passing through Doric Greece to preRoman Italy, when a cow or an ox was the standard medium of exchange. [91] However, given the dif? culty of trading fairly, evaluating different goods with the same standards, and ? nding suitable goods for both parties involved, the invention of â€Å"money† inevitably developed. Indeed, the origin of the word money is pecunia in Latin, which comes from pecus, meaning cattle. Through time, money evolved in the different civilizations and became not only a symbol but also a key factor in trading.Together with the development of the art of casting, the different mediums of exchange evolved gradually from random precious metals to what we now know as currency. This developments made our forefathers the proponents of the ? rst banks for reasons that are still of applicability in today’s banking system. The code of Hammurabi in th e early 2000 B. C. stated â€Å"If a man gives to another silver, gold or anything else to safeguard, whatsoever he gives he shall show to witnesses, and he shall arrange the contracts before he makes the deposits. [91] It is therefore clear that the Babylonians already placed back in their time their valuable possessions in a safe place, guarded by a trusted man. 3 Nevertheless, the real inspiration for the banking system as we know it today came from the Greeks. Unlike the Babylonians, the Greeks didn’t have a government and therefore the country was divided into independent states that were constantly either at war or in a state of unrest. [91] In these turbulent times, they found Temples to be the only safe place able to survive the test of wartime.They were seen as safe deposit vaults, marking the beginning of the functions of our current banks. Indeed, records show that the Temples not only kept money safe but also lent the funds at a certain interest rate. In addition , even though safeguarding the money started as a service free of charge, it soon turned into a business where small commissions were applied. The banking industry continued to evolve through time, from the commercial development of the Jews; passing by the establishment of the Bank of St.George, the Bank of the Medici and the Bank of England, to the rise of the Rothschilds, and the development of banking in the land of the Vikings. [91] At this moment in time, a major bank is a combination of a dozen of businesses, such as corporate, investment and small business banking, wealth management, capital markets. One among these is the retail banking industry. [46] The retail banking industry is characterized by a particularly large number of customers and bank accounts in comparison to any other banking business, which results in a much higher number of transactions, services and products.In addition, it relies more and more on technology due to the levels of cooperation between banks, retailers, businesses, customers leading to an ever-increasing amount of information processing requirements. In a nutshell, today’s banks follow the same principle described earlier by borrowing from clients in surplus and lending to those in de? cit. This triangulation is a win-win situation for the bank and its customers: the bank makes revenue from the net interest income, which is the difference between what it pays to the lending customer and what it receives from the borrower.Nevertheless, the bank can’t lend all the deposits and needs to guarantee that a certain percentage is kept aside to satisfy customer withdraws and requirements. [92] Even though the situation varies from bank to bank, it is noteworthy to mention that â€Å"more than half of a retail bank’s revenue, perhaps three-quarters, comes from this intermediation role in the form of net interest income†. [46] To conclude, in today’s world, and after years of evolution, retail ban ks provide you with a multitude of services for which they charge fees, mainly to cover the maintenance of the infrastructure and the bank’s structure.These added up together account between 15% to 35% of the net interest income. [46] Among the services you can ? nd payment services, phone banking, money transfer, ATMs1 , online banking, advisory services, investment and taxation services, mobile banking and many more. How does a bank ef? ciently govern, offer and maintain all these services? 2. 1. 2 The Retail Banking IT Systems’ Architecture Just as banking services evolved through time so did the overall back-end architecture allowing a bank to provide all the aforementioned services. This evolution was especially prominent after the unveiling by Barclays Bank f the ? rst ATM machine in 19672 : from that moment on, banks started investing heavily in computerized systems with the goal of automating manual processes in an effort to improve its services, overall status in the market and cut costs. From this perspective, the IT systems of banks matured from the creation of payment systems together with the launch of the international SWIFT network3 in the 70s, to today’s core banking system: a general architecture that supports all the channels and services of a bank and where each one of them is digitalized.An overview of such general architecture is illustrated in Figure 2. 1 [77]. 1 acronym for Automated Teller Machine, a machine that automatically provides cash and performs other banking services on insertion of a special card by the account holder [de? ned in the Glossary] 2 http://www. personal. barclays. co. uk/PFS/A/Content/Files/barclays_events. pdf 3 Society for Worldwide Interbank Financial Telecommunication (SWIFT) is a member-owned cooperative that operates a worldwide standardised ? nancial messaging network through which the ? nancial world conducts its business operations http://www. wift. com 4 This architecture was in plac e in many banks some years ago, and still is in some cases, but even though it provides the clients with all the necessary banking tools, it had certain drawbacks that became visible through the modernization and improvement of services. As it is described by both Microsoft [82] and IBM [77]: the as-is architecture has no true enterprise view of a customer because information is duplicated, which leads to inconsistent customer services and promotions across channels; when adding new or changing current products, it takes time to bring Figure 2. : As-Is Banking IT Architecture (source [77]) them to the market and a signi? cant amount of changes to the core system code. This leads to a dif? culty in responding quickly to new challenges and evolving regulatory pressures. Faced with the aforementioned problems, banks had the need to change towards a more ? exible and ef? cient architecture that would allow them to comply with the ever-changing needs of the clients and of the technology. With this n mind, the major players in core banking have switched to a Service-Oriented Architecture (SOA) with the intended goal of improving growth, reducing costs, reducing operational risks, and improving customer experience. [69] [94] [83] [77] [82] As reported by Forrester in a survey in 2007 [82], out of 50 European banks, 53 percent declared they were already replacing their core system while 27 percent were planning to do so and 9 percent had already completed a major transition. The same survey assessed that 56 percent of the banks already used SOA and 31 percent were planning to.Additionally, in Gartner’s 2009 report (Figure 2. 2 [28]), supports this strategy and believed that SOA-based architectures was increasingly being adopted and would be widely accepted in a time frame of 2 to 5 years. In the latest update (2011th Edition [29]), SOA is entering the Plateau of Productivity, which indiFigure 2. 2: Hype Cycle for Application Architecture, 2009 cates that the ma instream adoption is starting to take off. (source [28]) With this transition to an agile banking platform with a more ? exible product de? ition built on SOA principles, banks expect to gradually simplify their business and become more ef? cient in the long term. Indeed, the aforementioned platform which is illustrated in Figure 2. 3, is meant to provide the banks with faster and easier ways to update the system and comply with changing industry regulations and conditions. Additionally, by having a holistic view of the customer-relevant data across systems, a bank is able to better focus and analyze it with the goal to improve its customers experience by investing in more ef? cient and ? xible customer-centric offerings. Lastly, the architecture allows for integrated customer analytics and insight capabilities. In this line of thought, a stream-based real-time fraud detection solution would be easy to integrate in such an architecture, allowing the bank, as we will see later on, to broaden its services, data analysis capabilities and detect fraud in realtime. Figure 2. 3: To-Be Banking IT Reference Architecture (source [77]) 5 2. 2 Fraud When one wants to get something from others illegally he can do it in two ways: force or trick them into doing so. The ? st is better known as robbery and is usually more violent and noticeable; the second is known as fraud, which is more discrete and therefore preferred by fraudsters. [76] From this we can understand that fraud includes a wide variety of acts characterized by the intent to deceive or to obtain an unearned bene? t. [30] Many audit-related agencies provide distinct insights into the de? nition of fraud that can be brie? y summarized in this way: De? nition 1. Fraud consists of an illegal act (the intentional wrongdoing), the concealment of this act (often only hidden via simple means), and the deriving of a bene? (converting the gains to cash or other valuable commodity) [30] Given this de? nition, we can furt her classify the known types of fraud by victim, perpetrator and scheme [76]: †¢ Employee Embezzlement – Employees deceive their employers by taking company assets either directly or indirectly. The ? rst occurs without the participation of a third party and is characterized by an employee who steals company assets directly (e. g. cash, inventory, tools, etc. ). In the second, the stolen assets ? ow from the company to the perpetrator through a third party.Indeed, indirect fraud happens usually when an employee accepts bribes to allow for lower sales or higher purchases prices, or any other dishonest action towards the company. †¢ Vendor Fraud – This type of fraud usually happens when a seller overcharges its products; ships lower quality goods; or doesn’t ship any products to the buyer even though it received the corresponding payment. Vendor fraud happens more frequently with government contracts and usually becomes public when discovered, being one of the most common in the United States. Customer Fraud – Customer fraud takes place when a customer doesn’t pay for the products he purchased, pays too little, gets something for nothing or gets too much for the price. All these situations occur through deception. †¢ Management Fraud – Management fraud, also known as ? nancial statement fraud, is committed by top management who deceptively manipulate ? nancial statements. The interest behind these actions is usually to hide the real economic situation of a company by making it look healthier than it actually is.However, for the purpose of this research, and given the fact that we are focusing on fraud perpetrated in the retail banking industry, we will mainly focus on every possible bank transaction that a customer can perform. The research will be based in debit, online banking – namely electronic bill payment and giro transfers – and debit plastic card transactions. Fraud that can be perpet rated against these transactions falls within the category known as consumer fraud. Additionally, the latter can be sub-categorized in Internet and e-commerce fraud and other (non-)internet related fraud that we will now describe in more detail. . 2. 1 Internet and E-Commerce Fraud The Internet†¦ a technology that was unknown to many of us 25 years ago and is used now by billions of people either at home, work or on-the-go. We can ? nd webpages from business home pages, to informational wikis, passing through social networking sites; ? les that take the form of text, audio or video; and a multitude of services and web applications. It took just 3 years for the Internet to reach over 90 million people while the television and the radio took respectively 15 and 35 years to reach 60 million people! 76] This is how fast the medium through which e-commerce fraud takes place has evolved. This informational and technological revolution led to new ways for fraud to be perpetrated while techniques to avoid it have dif? culties to keep up with the pace. Today, businesses depend on the Internet to perform paperless transactions and exchange information between them: they mostly use e-business connections, virtual private networks (VPNs1 ), and other specialized connections. 76] This type of commerce is known as e-commerce, or electronic commerce, because it takes place over electronic systems. Therefore, even if you think you are not using the Internet, any operation you make at a local branch, any withdraw you do from an ATM or any purchase you make at a local store with your bank card, a Network transaction takes place. 1 it’s a method employing encryption to provide secure access to a remote computer over the Internet [de? ned in the Glossary] 6Since most businesses rely on Network-based transactions and, as we will describe later on, Internet users use the network more and more frequently to buy products or services, the North American Securities Administ rators Association (NASAA) considers that Internet fraud has become a booming business. [76] With this in mind, there are three standpoints that need to be taken into consideration when describing in more details the risks involved in this category that undermine banks and more importantly their customers: risks lying inside and/or outside the organization.Risks Inside Banks and Other Organizations The main risks come from within the bank. [76] Indeed, a perpetrator with inside access has knowledge regarding the environment, the security mechanisms and how to bypass them. Additionally, any employee with access to the organization’s network has automatically bypassed ? rewalls and security checks making it easier to in? ltrate systems, steal information or data and cause damage to the bank. From this perspective, the most common example is the superuser access that most IT-related employees (e. g. rogrammers, technical support, network administrators or project managers) have within the company’s infrastructure and database systems. [76] In one survey, â€Å"more than a third of network administrators admitted to snooping into human resource records, layoff lists, and customer databases†. [76] A related survey found that â€Å"88 percent of administrators would take sensitive data if they were ? red, and 33 percent said they would take company password lists†. [76] Even if a perpetrator does not have personal access to the targeted system and information, there are techniques that he can use to get at them indirectly, i. . via a person of interest: – Snif? ng, also known as Eavesdropping: Snif? ng is the logging, ? ltering, and viewing of information that passes along a network connection. Applications are easily and available for free on the Internet, Wireshark1 and tcpdump2 that allow network administrators to troubleshoot any possible problem in the network. Nevertheless, these applications can as easily be used by hackers to gather information from unencrypted communications. 76] A good example is the usage of unencrypted e-mail access protocols like Post Of? ce Protocol 3 (POP3) or the Internet Message Access Protocol (IMAP) instead of other more secured ones. Since e-mail clients check messages every couple of minutes, hackers have numerous opportunities to intercept personal information. [76] A user could in addition encrypt the body of the email by using Secure/Multipurpose Internet Mail Extensions (S/MIME) or OpenPGP in order to avoid that sensitive information passes through the network in plain text.Even though security experts have successfully managed to encrypt emails, the reason behind this lack of security is that they have failed to take into consideration the needs of the end-user – namely, â€Å"the ability to occasionally encrypt an email without much trouble at all†. [113] – Wartrapping: Wartrapping happens when hackers set up free access points to the Internet t hrough their laptops in speci? c locations like airports or inside a company’s headquarters. Users, unaware that the wi? passes through a hacker’s computer, connect to the latter and navigate the Internet as if they had a secured connection.When logging their internet banking services and performing transactions, or simply access their emails, the hacker can see the bits and bytes of every communication passing through any laptop in the clear. In this line of thought, hackers can get caught in their own web as companies are also using what they call honeypot traps. The latter is an information system resource, like a computer, data, or a network site (e. g. wireless entry), whose purpose is not only to divert attackers and hackers away from critical resources, but also to serve as a tool to study their methods. 1] These systems are placed strategically so to look like part of the company’s internal infrastructure even though they are actually isolated and monito red by administrators of the organization. One of the most widely used tools is honeyd3 . [89] 1 2 3 http://www. wireshark. org/ http://www. tcpdump. org/ http://www. honeyd. org/ 7 Passwords are the Achille’s heel of many systems since its creation is left to the end user who keeps them simple and within his or her preferences and life experiences (e. g. birthdays, family names, favorite locations or brands).In addition, users tend to re-use the same password for different purposes in order to avoid having to remember different ones, which leads perpetrators to gain access to different services and accounts with a single password from the person. In addition, another source of threats are the laptops and mobile devices that many employees take with them outside the company’s protected environment. While in these unsecured contexts, the devices are exposed to viruses, spyware, and other threats that might compromise again the integrity of other organization’s sy stem once these computers are plugged in the network.Viruses, trojans and worms are able to enter the protected environment without having to go through ? rewalls and security checks, making it easier to in? ltrate key information systems and bypass defense mechanism. Risks Outside Banks and Other Organizations The Internet not only became a source of services to users and companies but also a rich medium for hackers to gain access to personal systems. Indeed, when performing attacks, hackers are relatively protected because they cross international boundaries – which puts them under a different jurisdiction than the victim of the attack – and are mostly anonymous – making tracking dif? ult. Therefore, the Internet became the defacto technological medium to perform attacks and there are numerous ways of doing so: – Trojan Horses: A trojan horse is a program designed to breach the security of a computer system and that has both a desirable and a hidden, us ually malicious, outcome. [86] These programs can be embedded in a bank user’s computer when he views or opens an infected email, visits or downloads a ? le from an unsecured website or even when visiting a legitimate website that has been infected by a trojan. [85] From this perspective, a good example is the man-in-the-browser (MitB) attack, represented in Figure 2. , which uses trojan horses to install extensions or plugins in the browser that are used to deceive a bank customer: Whenever a speci? c webpage is loaded, the Trojan will ? lter it based on a target list (usually online banking pages). The trojan extension waits until the user logs into his bank and starts to transfer money. When a transaction is performed, the plug-in extracts data from all the ? elds and modi? es the amount and recipient according to the hacker’s preferences through the document object model (DOM1 ) interface, and resubmits the form to the server.The latter will not be able to identify whether the values were written by the customer or not and performs the Figure 2. 4: MitB Operation (source2 ) transaction as requested. [85] – ATM Attack Techniques: An Automated Teller Machine (ATM), is a computerized device that allows customers of a ? nancial institution to perform most banking transactions and check their account status without the help of a clerk. The device identi? es the customers with the help of a plastic bank card, which contains a magnetic stripe with the customer’s information, together with a personal identi? ation number (PIN) code. [2] ATMs are attractive to fraudsters because they are a direct link to customers information and money, and there are security pitfalls with their current architecture [2]: the way data is encoded in the magnetic media makes it easily accessible if a hacker invests some money to buy the easyto-be-found equipment, and time to decode and duplicate the contents; in addition, with a four 1 An interface that let s software programs access and update the content, structure, and style of documents, including webpages [de? ed in the Glossary] 2 www. cronto. com, blog. cronto. com/index. php? title=2fa_is_dead 8 digit PIN, not only will one in every 10. 000 users have the same number but it also allows brute force attacks to discover the combination. Not to mention the possible physical attacks on ATMs which cannot be considered as fraud (see De? nition 1), there are a couple of ways fraudsters steal money from bank customers [2]: 1. Skimming Attack: skimming is the most popular approach in ATMs and consists in using devices named skimmers that capture the data from the magnetic strip.These devices can be plugged in an ATM’s factory-installed card reader and allows for download of all personal information stored on the card. In addition, to obtain the PIN code fraudsters use either shoulder-sur? ng and hidden video cameras, or distraction techniques while the customer uses the ATM. [2] S ometimes fraudsters take a step further and create their own fake teller machines to deceive bank customers; this is considered to be a spoo? ng attack that we will describe in more details below. [39] 2.Card Trapping: this tech

Running Head: Substance abuse in prisons.

In most prisons in the world, alcohol dependence is the most prevalence substance abuse.   Most prisoners are said to depend on alcohol and drugs, to relieve stress and tension.   This has been linked to the fact that most inmates are imprisoned for drug – related offences.   These criminals are believed to introduce drugs and alcohol in prison.   The majority of the prison population has shown some evidence of substance abuse.   In Canada, research has shown that at least 7 – 10 inmates have engaged in activities of drug and alcohol abuse.The use of drugs and alcohol in prisons is highly spreading.   Research has shown that women have more severe substance abuse problems than men.   They have also shown a tendency of being involved in hard drugs such as cocaine and heroine than men. These women also have a higher rate of injection drug use than men, at approximately 91%.Heroin is the type of drug abused by most inmates in state prisons of Toronto and Mont rà ©al, while in Atlantic Canada alcohol and cocaine are the most abused. Alcohol and drugs are available in almost all prison facilities in the world.   Research studies have shown that drug trade is much more rampant and violent in the prison than on the street. Despite the efforts to eliminate drugs in prison inmates have always been discovered to test positive for drug tests.The types of drugs available in prison include marijuana, alcohol, drugs, cocaine, tranquilizers, opiates, Hallucinogens, stimulants, sedatives and heroin.   Marijuana is the most common drugs while heroin is the least available drug in prison.Drugs treatment programmes available in California state prison.Programmes for drugs abuse treatment in this prison include, detoxication programmes, therapeutic programmes, drug courts, transitional treatment, maintenance and aftercare and pharmacotherapy methods. In California prison state intensive intervention services are reserved for offenders who are viewed as high risk.   The treatment available is designed to target behaviors that are predicative of criminal behavior.In this prison, treatment techniques such as coping skills training have been used to reduce substance abuse.   Inmates are trained on how to cope in prison without drugs and also given social skills and employment skills. These skills assist in keeping them focused on other positive activities.   Structured relapse prevention techniques are established to prevent inmates from relapsing into substance abuse.   Stress management sessions are held in prisons to help prisoners deal with drug withdrawal symptoms. Willing prisoners are asked to set their goals of treatment and work step by step to achieve them.   This way they are motivated to stop drug abuse.   Community reinforcement techniques have also been started. The community offers its services to the prisoners through voluntary services to encourage motivate them in stopping substance abuse.   However , these treatment programs have not been very effective in the prison.   The programs are not enough to stop usage of substance abuse in prisons.ConclusionI think that more treatment programmes should be established in the prison.   Control and detection programmes should be established to detect any form of drug smuggling into the prison.   Visitors and new prisoners must be thoroughly checked before entering the prison compound.   Routine searches should also be done in the prison frequently to discourage any form of drug or alcohol storage in the prison. Comprehensive screening and assessment of inmates on entry should be performed to identify the treatment programme suitable for each patient. Drug detection dogs should be introduced in the prison to facilitate drug detection in the prison.   Visitors’ areas should be modified to the designs and layouts that are open and easily accessible.   The visiting areas should be completely separated from the inmates sec tion by an impassable barrier. The way against drug and substance should be intensified to eliminate them from our society.References.Substance abuse in corrections. http://www.ccsa.ca/pdf/ccsa-011058-2004.pdf.as retrieved on 26/10/2007.Alcohol info. http://www.alcoholinfo.nsw.gov.au/justice_system/corrections_system as retrieved on 20 Jul 2007.Heroin addiction treatment. http://www.alcoholinfo.nsw.gov.au/justice_system/corrections_system as retrieved on 20 Jul 2007.

Operation Anaconda 2 Essay Example | Topics and Well Written Essays - 1000 words - 1

Operation Anaconda 2 - Essay Example The unique geographical setting of The Gardez-Khost -Shahi-Kot areas in Afghanistan, which consist of harsh climate and high altitude, fostered the growth of Taliban and Al-Qaeda militants. So, the research and task collection conducted by various intelligence organizations under the direct control of United States Army Special Forces and Joint Special Operations Command proved much important to the Operation Anaconda. Several intelligence agencies like Central intelligence agency (CIA) and Federal Bureau of Investigation (FBI) played significant role in different levels of the operation. For instance, CIA paramilitary forces participated in the mission among special operation forces. Other agencies are: Bureau of intelligence and research, Defense Intelligence Agency (DIA), Imagery Intelligence, National Reconnaissance Office, National Geospatial-Intelligence, signal intelligence, Human Intelligence, (HUMINT) and SOF military Reconnaissance. The Special reconnaissance (SR), controll ed by small division of well qualified military workforce played the crucial role in research and task collection stages. So, one can see that the research and task collection conducted by various intelligence organizations in the Gardez-Khost Shahi-Kot areas prove the strengths and limitations of various intelligence organizations in critical situations. Human intelligence (HUMINT) is the oldest method of intelligence service which collects information from human source. The most important and famous intelligence department in the united state is the CIA. During Operation Anaconda, The combined joined task force mountain’s intelligence wing relied upon human intelligence. But in Gardez-Khost -Shahi-Kot areas, the human intelligence personal failed to recognize the exact number of militants and their strength. The Signals intelligence (SIGINT) provided information on presence of Taliban leaders

Explain how maximum performance might beast be extracted from a small Essay

Explain how maximum performance might beast be extracted from a small engine - Essay Example Initially, two stroke engines were used to power toy cars, but now they are being used in lawn-mowers, motorcycles, and power saws. Two stroke engines are also undergoing modern innovations which are meant to convert them into better, modified engines. This paper reflects on strategies to optimize small engines’ performance. The first thing that one has to consider is that there should be no engine deposits present in the engine. Engine deposits may be there even if the engine is in its best mechanical condition. Carbon deposits may be present in the combustion chamber or in the valves of the cylinder (Totten, Westbrook & Shah 2003: 470). The fuel injectors may also be rusty. Carbon deposits and rust are the culprits that make the small engines perform less than they can when they are in new condition (see fig.1). Hence, tuning must be done, which requires much training, patience, and energy on the part of the owner (Pickerill 2009: 499). One can be sure whether or not proper tuning has been done by hearing the sound of the engine. If it is like new engine, that means tuning is proper, leading to maximized performance. Maximized performance is guaranteed when synthetic lubricants are used. Synthetic lubricants are known as saving engines because they are good friction reducers, and they â€Å"improve engine performance, durability, and prolong draining periods†, state Stachowiak and Batchelor (2011: 60). ... â€Å"Conventional engine oil begins to break down at 260 degrees F. Synthetic keeps going at 300+ degrees F. Truth is, if your engine's oil temperature is 260 to 300 degrees F, you have greater problems than which oil to choose†, writes Smart (2013, par. 4). So, synthetic lubricants are recommended to extract optimized performance from small engines. Moreover, one should make sure that there is enough compression in the engine to perform at its best, writes Senatore (1999: 51). Increased compression means more horsepower. Obviously, one cannot increase compression at home. The engine provider can best suggest how to increase compression and maintain cam selection. A specific compression ratio must be maintained (see fig.2). It is actually the comparison of the volume of the cylinder with the space left when the piston is at the top of stroke. Although the engine performs better at a high compression ratio (Buzelli 2006: 41); yet, the compression ratio should not go beyond 10. 0:1, otherwise it is likely to result in detonation or pre-ignition, which results from low octane in the engine. It is also called pinging. Fuel and spark curves must also be kept into consideration while maximizing compression. It is also important to keep a check on the throttle body of the engine, since a strong, high-performance throttle body guarantees optimized performance (Kojima 2002: 25). Much more horsepower and comparable torque can be gained through a large throttle body of the engine (Evans 2004: 44). However, that needs a check. Too large a throttle body makes the engine loose power. â€Å"Throttle bodies that are much too big for an engine lose their authority way below full throttle, because once the engine is fully loaded, it simply cannot use more

Forks over knives Essay Example | Topics and Well Written Essays - 500 words

Forks over knives - Essay Example d that children from the country’s affluent parts consumed plenty of animal-processed foods that exposed them to a higher risk of getting liver cancer in their later years. As for Esselstyn, his discovery comes from his experiences as a surgeon as he realizes that several of the diseases he regularly treats are practically unheard of in parts of the world where animal-processed foods are not consumed. Fulkerson bases the film on investigations conducted by the two researchers as from the 1980s when they initially meet. In an attempt to convince people that plant-based and whole foods have the potential of reversing lifestyle diseases and degenerative ailments, he features the pioneering study by Campbell and Esselstyn in China. Fulkerson includes alarming data on the huge sums of money spent on medical bills and the high number of Americans that are obese; to paint the grim picture that is reality. This is helpful in raising awareness and making it clear that changes in one’s diet need to start now. Fulkerson also trails the lives of a chosen set of patients who have taken on a plant-based diet and witnessed positive changes in just a short while. The positive changes include remarkable improvements in cholesterol levels, blood pressure and arterial occlusions. The illustration of these stories brings on great persuasive value to the message of the films adding on is the fact that Fulkerson gets on a similar diet himself. These affirmative effects coupled with scenarios of places such as New Guinea where regenerative ailments are unknown and foods consumed are purely plant-based, brings out the simple fact that animal protein is detrimental to one’s health (Groen). Featuring nutrition experts and celebrity cameos, he comes out to dismiss all the myths about fish and chicken being the best source of proteins while milk being the easily digestible way of getting calcium. He, therefore, through Caldwell and Esselstyn, emphasizes on adopting a diet that

Leadership Assignment Example | Topics and Well Written Essays - 4000 words

Leadership - Assignment Example Toffler’s (1970) conception of â€Å"transcience† was way ahead of its time. Looking forward more than 3 decades ahead, he has painted a frightening picture of how weary the situation will be if one will not be able to adapt to the ongoing acceleration of change. Transcience is the feeling of temporariness and impermanence which is what Toffler predicted would be the adjustment mechanism of contemporary man. In terms of employment, transcience may be seen in how employees cannot stay put in one job as they grab as many opportunities as they could just to get ahead. They may jump from one job to another, eager to try out the latest job openings in the newest companies. Such restlessness evades the development of loyalty to the current employer and even hinders the formation of strong bonds with co-workers. Ideally, young workers seek jobs related to their interests. However, when the need for a job is at hand, they may forsake their interest and take whatever job is avai lable. They find out more about the job in the early days, and get to gauge if they have chosen well or not, a job that they can establish their career on. As employees vacillate from job to job, they try to find what is in it for them†¦ is it challenging enough? Does it tap into their skills? Does it pay better? Hence, managers should make job offers as attractive as possible to recruit the best candidate for the position. Transcience significantly impacts an organization’s performance, as the frequent loss of employees to other companies can paralyze operations. Recruiting and training new staff involves a great deal of time, effort and resources that may drain the organization and hamper its smooth flow. Managers would prefer to work with a steady group of workers that would grow with the company. Providing an orientation to the job at hand is crucial in an employees’ decision to stick with a company for a long time. As in most situations, the first impression usually sets the expectations of people. Managers must prepare well for such an orientation because it is one way to send a clear signal to the employee that the organization is committed to his success. Organizational-level orientation intends for employees to feel welcomed and that they are now part of the organization; learn more about the organizational mission and vision, culture, language, history and structure. Employees come to better understand the basic workplace principles and policies. It prevents unwanted employee turnover is the provision of an effective orientation program to guide new hires through their jobs and the company in general. Even an experienced professional can be vulnerable to feeling abandoned when he is left on his own to survive unaided in a new and strange work environment. Apart from a standard employment contract, employees may also wittingly or unwittingly expect a â€Å"psychological contract† with the employer that includes open and hones t communication, managerial support and challenging and interesting work. Employees are becoming increasingly aware of the non-monetary rewards that companies can provide them. It is generally important that the job not only fulfills their economical needs, but their socio-emotional needs as well. This means a discussion of compensation and benefits should go beyond the concrete rewards of the

The End of Euro Disneys as a White-knuckle Ride Essay

The End of Euro Disneys as a White-knuckle Ride - Essay Example It is not expected for growth in the European sector to be experienced until 2010 or 2011, which will require Disneyland Europe to find innovative methods to draw higher crowd volumes and ensure profit success. Additionally, rising commodity prices which will be felt in 2009 will seriously erode the potential for profitability in this year, with the business forced to cut costs where necessary and appropriate. One publicity expert identifies that theme park industries are highly competitive and strategic success involves the volume of guests which pass through the entrance turnstile every day (Kohl, 2000: 48). Further, the author identifies that attendance is so widely important that many executive leaders have hourly tourist counts pumped into their mobile devices for the sake of tracking customer numbers (Kohl). This is especially true at Disneyland Europe which relies on customer volumes to generate annual profit. Getting higher sales volumes, however, in the midst of the current credit crunch requires understanding competitive behaviors whilst redesigning Disneyland Europe with a new, modernized logo to attract the contemporary consumer. Readjustments to marketing will be the proverbial saving grace for Disneyland Europe in the next five years. In 2009, Disneyland Europe must make a stronger effort to understand its customers. Since over 40 percent of all customers are French, repositioning the EuroDisney brand to focus on French lifestyle can ensure a higher revenue stream during the current economic crisis. This will be increasingly difficult beyond 2008 and will require research into existing consumer behaviors in French markets. Snyder (2002) offers that the French tend to link Disneyland Europe with American culture, giving them a negative perception of â€Å"cultural imperialism† in which the theme park is  attempting to force American lifestyle into a proud, French culture. Negative cultural perceptions must be eliminated whenever possible in the new marketing model.  

Impact of Instructional Coaching on Students Thesis

Impact of Instructional Coaching on Students - Thesis Example The thesis represents the comprehensive analysis regarding the subject matter of impact of instructional coaching on students’ achievements. The report comes under division into five major components. The introduction of the testimony elucidates the background information with reference to the topic of concern, which comes under following by the literature review segment that enlightens the deeper learning about the topic under discussion. The hypothesis explicitly states the theme that would come under focus in the project report. The findings of various themes under discourse are also a crucial part that has come under integration that would enlighten how the evaluation methods can help assess the impact of instructional coaching on students and their performances. ... Therefore, all the inhabitants of each culture respect the value and role of knowledge, as they are well aware of the fact that education is one of the imperative elements that can make an individual reach the heights of success (Adams &  Hamm, 2005). As everyone comes under acquaintance with the advancements that the world has encountered until the twenty first century, this has brought constructive alterations, developments, growth, and expansions in all fields that even include the education and learning practice and curriculums for the students. As an outcome of such novelties, the methods and techniques of teaching have also transformed, and hence augmented the new and modern ways of schooling and education. Thus, this process has become an ongoing practice that can lead to enhance the performance of the students and boost their level of understanding (Adams &  Hamm, 2005). Out of numerous teaching methods that have come under introduction and practice, instructional coachin g is amongst one of those modern ways that has come under implementation in the recent times. According to the web definition, an instructional coach is someone who â€Å"maintains a non-evaluative, learning relationship as a professional developer working with a teacher. Both coach and teacher share the expressed goal of learning together, thereby improving instruction and student achievement† (Gwazdauskas &  Northcentral University, pp.8, 2009). In other words, an instructional coach is primarily and professionally accountable for implementing the evidence-based learning practices into the classrooms of the school in accordance with the teachers and

Probation and Parole in the United States Essay Example for Free

Probation and Parole in the United States Essay Abstract Employee security problems and high risk caseloads progressively will move agents away from customary probation ideas of casework in the direction of ideas lined up with control or disciplinary beliefs. This study analyzes how the role of probation and parole has altered and what the future will be like counting on how we respond to the alterations at hand. This study also reviews changes in the criminal policies that have transpired. Probation and Parole in the United States: Changes in the Correctional System since World War II Under Florida law, the Department of Corrections is to keep the public safe and out of harm’s way, supply a protected and friendly environment for employees and criminals; work in joint venture with the community to add programs and services to lawbreakers; and oversee criminals at a grade of security appropriate with the hazards they present (Florida Department of Corrections, 1992). These duties pose marvelous trials for the employee’s management of the Florida Department of Corrections and the corrupt justice system as a whole. To have an insignificant impact on these matters, the system must make some foremost changes in how corrupt justice is distributed. This is necessary as the crime rates continue to rise; as courts misplace their influence in deterring crime by equalizing quick, rigid sentences; as correctional organizations accept inmates today and let them go tomorrow to ease overcrowding; and as criminals become more dangerous. The penalties of our failure to arise the surge of crime are frightening. An article in USA Today (1991) stated: Probation agencies have become seriously under-staffed just as they are being depended upon more strongly than any issue in history; probation, rather than prison, is the most widespread pattern of punishment. Probation agents have become less and less engaged in  their usual function, more connected to communal work and are more absorbed with law enforcement, following down those who have defied the periods of flexibility. Regardless of an astounding increase in the number of institutional facilities, an exceptional number of criminals have been put under strict watch in the community. The Florida Department of Corrections has more than 100,000 felony criminals under supervision in Florida communities. If it were not for overcrowding, numerous of these criminals would be assisting long sentences in state organizations. Regulation enforcement, the court, the correctional system, and probation and parole have the same duties for the malfunction of the criminal justice system. The difficulties documented above are huge. To deal with these difficulties, much of what we do and how we do it will need to be different. Many inside and outside alterations currently have been made to respond to the individual safety concerns of probation/parole agents in Florida: adjustment of agencies to provide for larger employee security, soft body armor, and firearms. This study task calculates areas where change is required, as well as obstacles that stand in the way of change. According to Edward W. Sieh (1990), Over the past so many years, probation communities have grown to more than 18% compared to about 15% in jail and prison communities and almost 13% in the number of paroles. Close to 2/3 of the complete correctional community was under probation supervision in the community at the end of 1985. Sieh decided that huge alterations in the criminal community have led to alterations in sentencing, as well as in how officers are expected or needed to do their job. Twenty-five percent of convicted felony criminals get probation. Other judgments encompass complete supervision and shock probation. Sieh’s study recognized two essential forms for overseeing criminals—â€Å"remedy† and â€Å"justice†. The remedy model demonstrates customary  casework and rehabilitation, and assesses change within the system. Sieh attacked the remedy mode, showing that it is inherently violent and unjust. It supposes a power over the impulse of criminals, out of capacity to their incorrect doing; it ignores information about the communal rather than therapeutic basis of crimes, and it treats clients as absolute objects of disciplinary policy to manipulate at a whim. The justice standard deals with the increasing occurrence of punishment. Sieh accepts that the standard was made from public appeal for belief of punishment with the smallest risk to the community. In his description of the justice standard, the officer is not at all worried about assisting alterations in the criminal, court instructions become the direction for supervision. Criminal success or failure counts upon his/her agreement with the conditions of supervision, and not the blame of the officer or the system. Complete supervision, in his outlook, is a process of the justice standard. Sieh assumed that the system is moving firmly nearer to a disciplinary one—the justice standard. He questioned contemporaries in the field to gaze for equity. Harris, Clear, and Baird (1989) discovered that the probation system completely extends to support remedy beliefs. Morran and Linder (1985) were in unison. They found more considerably, that electronic devices, sophisticated drug and alcohol area testing kits, and computerized information of new arrests are assisting to a larger focus of the law enforcement duty. Many of the criminals who carry out crimes are presently under supervision. Collaboration between regulation enforcement and probation would increase effectiveness. At the same time, supply for larger security for probation officers who might otherwise be needed to make late evening calls to tough positions without backup would not hurt either. Ten years before, supervising the whereabouts of a criminal by the use of an electronic monitor emerged light years away. Cold War undercover operations employed the use of electrical monitoring apparatus. The Florida corrections  system has intensely cooperated in a supervising program for more than ten years. Today, 800 criminals are under electrical monitoring oversight. The courts have vigorously approved the use of the apparatus. It is expected that the electrical monitoring will be utilized at elevated rates in the future. Between 1984 and 1988 the probation caseload went higher from 1.74 million to 2.36 million individuals (Bureau of Justice Statistics, 1989). Numerous administrations have furthermore noticed that the probation populations are criminals who often begin with a high risk of threat to the probation agents (Guynes, 1988, Petersilla, Turner, Kahan, Paterson, 1985; Snyder, 1986). It is without question very necessary that officers are supplied with an access of security to make sure that they don’t become victims. The future is ours to shape and change, or to just let it occur. Given a perception of much important advancement, we should start to increase the speed of some of the more attractive future anticipations and slow up those with less attractive anticipations. To adjust the course of unattractive purposes, the setting up of balancing standards and practices will be needed.

Benefits of Internal and External Analysis

Benefits of Internal and External Analysis TASK 1 Discuss the importance of environmental analysis in managing business organisations. Differentiate external and internal environment. Give at least 3 variables in the external and internal environment. Discuss the potential effects of these variables to the business entity. In what way the stakeholders can affect the future of the business? External and Internal Environment Some of the internal and external environment are:- Internal environment include inside the association, which are the worker mentality, new gear, methodology, workforces. The association has the control of these matters in light of the fact that it happen inside the association unless like nature. For the nature, is obviously expressed with the saying outer itself which implies outside of the associations which impact the progressions in the association which the association does not have the control of it. Outside environment are included by thePolitical, Economy, Social, Technology, Legal and Environment. (Class Discussion) b. Ordinary management and Strategic management Management: Accomplishing work through individuals. Thats the exemplary business school meaning of administration. Vital Management: Setting objectives and destinations for a venture (a business or organization as a rule’’). Strategic approach in managing an organization by taking into consideration the environmental forces: technological, commercial, economic, financial, political, regulatory, socio-cultural and physical environments, service markets.( books.google.co.nz/books?id=xelnAgAAQBAJ) c. Stakeholders and customers Stakeholders refer to individuals and groups that have interest in the organization and/or its products or services. Stakeholders may include but are not limited to – customers, suppliers, owners, shareholders, lenders, managers, employees, employee organizations, local community, competitors and regulatory bodies. They are each one of the individuals who have a stake in the association, i.e., who have a certain enthusiasm toward the presence of the association, yet the association does not EXIST for the stakeholder. It tries to fulfill the needs of stakeholders by fulfilling the needs of its customers. Stakeholders are the driven power. Not the main impetus. Customers are the reason for which the association exists and stakeholders are each one of those diversions, inward and outside, that met up with the end goal of fulfilling customer needs and in doing so expect some return for their exertion. Customers are those who pay money to get services and can demand for services.( smallbusiness.chron.com) 1.1 Give at least 3 variables in the external and internal environment. Two variables internal factors: Staff satisfaction, business management, research and development. 1: strengths of the business 2: Rules and regulation of the company 1: The strengths of the company: workers, promotion scheme and communication skills are some of the basic strengths of the company. If the promotion plan is not good and your staff is not doing their job properly it will surely effect the internal working of the company. 2. Rules and regulation of the company: If you want to make good business rules of your organization it should also be followed and implemented for the better working of organisation as it is the only method to control the business internally. Three variables of external factors: 1: Political Factor 2: technical Factor 3: social factor 1: The political factor: Political factors greatly affect the business environment, when parties changes in the government and then business policy are changed it greatly affect the working of the company for example pay rise etc. Everyone has to folloup new rules. 2: Technological Factor: In these technological factor days is the main factor of any business organization if you are other competitors are using new technical equipment, challenging production as compare to your business will increase, so will affect your organization downside. 3: social factor: social factors also affect the external factor, because you always change the structure of society such as women working, families. How customers, communities behave and their beliefs. ( Google images) a. In what way the stakeholders can affect the future of the business? A stakeholder is anyone who can affect or is affected by the actions of a corporation (organization, company or business). It is enterprise are the people and electorates that help, either intentionally or automatically, to its riches making limit and exercises, and that are along these lines its potential beneficiaries and/or hazard bearers. b. Discuss the importance of environmental analysis in decision making. Choice making in ecological tasks could be unpredictable and apparently unmanageable, mainly due to the innate exchange offs between socio political, natural, biological, and financial variables. The determination of fitting healing and decrease methodologies for defiled destinations, area use arranging, and administrative methods frequently includes different extra criteria, for example, the conveyance of expenses and profits, ecological effects for diverse populaces, security, biological danger, or human qualities. Some of these criteria cant be effortlessly dense into a fiscal quality, somewhat in light of the fact that ecological concerns regularly include moral and good standards that may not be identified with any monetary utilize or worth TASK 2 Using the diagram as shown in the next page, discuss the importance of environmental analysis and its impact to the business entity by answering questions A, B and C. a. How will the competitors, suppliers and customers affect the business? If we have more competitors in a business, the business activity would be increased. This is because with more competitors, the consumers would have more choice. Besides, increased competition would also result in competitive cost which would further attract the consumers. Suppliers can affect your availability of product based on their inventory or delivery time. Suppliers can affect your costs based on their prices changing, their credit terms, etc. Client administration or client consideration portrays connections between a business and a client intended to expand the clients fulfillment with the items, administrations and general mission of the business. Basically, any correspondence between a potential, present or past client and a business, for example, discussions in a physical store, via telephone or on email, may be viewed as a piece of client administration. Poor client administration can have a few inconvenient consequences for a business. b. Analyze the influence of the following macro-environmental forces to the proposed business Legal/Regulatory Rules and regulations in business creates, limits, or constraints a right, creates or limits a duty, or allocates a responsibility. Regulation can take many forms: legal restrictions promulgated by a government authority, contractual obligations that bind many parties (for example, insurance regulations that arise out of contracts between insurers and their insured) Social/Cultural Social Accomplishing work through individuals. Thats the fantastic business school meaning of administration. Key Management: Setting objectives and goals for a venture (a business or organization by and large variables can influence a business as tastes and preferences can change. For instance, if a business that offers a ton of garbage and undesirable sustenance, and the country is getting to be more wellbeing cognizant then the business will create lesl revenue A real socio-social component impacting organizations and business choices is changing buyer inclination. What was prevalent and chic 20 years prior may not be well known today or 10 years not far off. Distinctive styles and necessities can undermine long effective items and administrations. For instance, a garments organization should continually be mindful of changing inclination when making new items or it will rapidly get to be antiquated. Technological Technical analysis is a security analysis methodology for forecasting the direction of prices through the study of past market data, primarily price and volume. Economic Economic Analysis for Business is to help you understand the structure of the economy, how an economy operates, why economic problems occur and the reasons why policy makers make the decisions they do. c. Who are the stakeholders of the business? A corporate stakeholder is that which can affect or be affected by the actions of the business .It defines stakeholders as those groups without whose support the organization would cease to exist. Stakeholders refer to individuals and groups that have interest in the organization and/or its products or services. Stakeholders may include but are not limited toend ash customers, suppliers, owners, shareholders, lenders, managers, employees, employee organizations, local community, competitors, and regulatory bodies. For Mr. webby He needs to target the right customers and must work on there feedback. Must look food quality. He must be familiar with current people demand. He has to look for more suppliers and for them he should do some promotions. He should put lower price for his products. Get more promotions for customers. Update everyday’s deals and prices. TASK 3 Miss Parker is the longtime general manager of MAC, the biggest food chain in Dunedin. The owner of MAC plans to open a new branch in Auckland City. Since the general manager knows that you have a Diploma in Business from IANZ, she seeks your advice how to do the following steps in strategic management: a. Statement of the Business Mission Having a specific mission statement gives identity to an organization, as well as a guideline for future decisionsand particular actions. It provides the framework or context within which the companys strategies are formulated. Its like a goal for what the company wants to do for the world. Mission statement that can be used by Miss Parker is â€Å"We are committed to the highest standards of food quality and customer service† MAC is the best food chain in Dunedin and now in Auckland. b. Environmental Analysis Considering that the organization does not operate in isolation, the planning group should analyze the environment within which the organization operates. This assessment should include legal and political considerations, social and cultural trends, economic circumstances, technological development, and, where appropriate, the organization competitive position. c. Identification of Strengths and Weaknesses This includes the weighing of the organizations capabilities and limitations. This form of analysis must be inclusive in a sense that it has to take into account the financial resources, human resources, the operation of both technical and organizational systems, and quality of work. Analysis of the organizations strengths and weaknesses must be guided by the mission of the organization. d. Values of Organizational Leaders The final step in preparing to develop strategic alternative is to take into account the values, interests, and aspirations of those who will guide the organization into the future. It must consider the people quote s response in the same environmental and organizational analysis differently. e. Development of Alternative Strategies At this stage, the planning group can proceed in the formulation of alternative strategies. One of the suggested methods in identifying alternative strategies is to draw quote scenarios of the future in order to anticipate and foresee possible outcomes. Future patterns will help an organization plot its direction. Miss Parker must look for good location and check current market pricing. She has to look for more suppliers and some better deals for them. She should keep on checking market demand. Should keep food as checking on which culture people living on those areas. Flexible opening hours and services. Keep getting various foods and changing promotions.( www.bbc.co.uk †ºHome†ºBusiness Studies†ºBusiness environment) Task 4 You are a manufacturer of beauty products in New Zealand. Your business market power is heavily dependent on the exportation of these products. If the global economy is not in a good condition and the countries where you export your products are experiencing inflation, serious poverty and unemployment, what are the possible effects of the global economy to your business in New Zealand? Make a flowchart to present the effects of the macro-environment to the business entity. Some of the possible reasons are :- Because of poverty it can affect sociality to not to buy products. To continue keep going on my business I must look at the raw materials of my products. For raw materials I do need to find another good quality of raw materials but cheaper in prices as well. Keep checking on cost cuttings. Put more promotions to encourage people Keep checking on market demand. Technologically I do need to improve more also handy for people for example, advertising, online advertising. Also should make a handy website so people can look easily also a viability of buying online for time saving. Seek for good sales workers(can help to improve business) Try some new products as in current market demand. poverty Effect on countries