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_a332.1068/4 _223 |
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_aBUS027000 _2bisacsh |
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049 | _aMAIN | ||
100 | 1 | _aSubramanian, Revathi. | |
245 | 1 | 0 |
_aBank fraud : _busing technology to combat losses / _cRevathi Subramanian. |
264 | 1 |
_aHoboken, New Jersey : _bWiley, _c[2014] |
|
300 | _a1 online resource. | ||
336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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490 | 1 | _aWiley & SAS business series | |
520 |
_a"Capitalize on technology to halt bank fraudExamining the technology that is needed to combat bank fraud, Bank Fraud: Using Technology to Combat Losses equips corporate security and loss prevention managers with the necessary tools to determine an organization's unique technology needs. Looks at the technology needed to handle data intelligence Provides guidance to assess the technology necessary to battle fraud Features unique coverage of the history of fraud detection and prevention in banking Explores the challenges of fraud detection in a financial services environment; understanding corporate risk exposure; losses per assets; trending over time; benefits of technology Focusing on the financial crimes and insider frauds in operation nationally and internationally, Bank Fraud: Using Technology to Combat Losses arms fraud prevention professionals with authoritative guidance to detect and prevent such crimes in future"-- _cProvided by publisher. |
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588 | 0 | _aPrint version record and CIP data provided by publisher. | |
504 | _aIncludes bibliographical references and index. | ||
505 | 0 | _aBank Fraud; Contents; Preface; Acknowledgments; About the Author; CHAPTER 1 Bank Fraud: Then and Now; THE EVOLUTION OF FRAUD; Fraud in the Present Day; Risk and Reward; Secured Lending versus Unsecured Lending; Statistical Models and the Problem of Prediction; THE EVOLUTION OF FRAUD ANALYSIS; Early Credit Card Fraud; Separating the Wheat from the Chaff; The Advent of Nonlinear Statistical Models; Tackling Fraud with Technology; SUMMARY; CHAPTER 2 Quantifying Fraud: Whose Loss Is It Anyway?; Data Storage and Statistical Thinking; Understanding Non-Fraud Behavior; Quantifying Potential Risk. | |
505 | 8 | _aRecording the Fraud EpisodeSupervised versus Unsupervised Modeling; The Importance of Accurate Data; FRAUD IN THE CREDIT CARD INDUSTRY; Early Charge and Credit Cards; Lost-and-Stolen Fraud: The Beginnings of Fraud in Credit Cards; Card-Not-Present Fraud and Changes in the Marketplace; THE ADVENT OF BEHAVIORAL MODELS; FRAUD MANAGEMENT: AN EVOLVING CHALLENGE; FRAUD DETECTION ACROSS DOMAINS; USING FRAUD DETECTION EFFECTIVELY; SUMMARY; CHAPTER 3 In God We Trust. The Rest Bring Data!; DATA ANALYSIS AND CAUSAL RELATIONSHIPS; BEHAVIORAL MODELING IN FINANCIAL INSTITUTIONS. | |
505 | 8 | _aCustomer Expectations versus Standards of PrivacyThe Importance of Data in Implementing Good Behavioral Models; SETTING UP A DATA ENVIRONMENT; 1. Know Your Data; 2. Collect All the Data You Can from Day One; 3. Allow for Additions as the Data Grows; 4. If You Cannot Integrate the Data, You Cannot Integrate the Businesses; 5. When You Want to Change the Definition of a Field, It Is Best to Augment and Not Modify; 6. Document the Data You Have as Well as the Data You Lost; 7. When Change Happens, Document It; 8. ETL: "Extract, Translate, Load" (Not "Extract, Taint, Lose"). | |
505 | 8 | _a9. A Data Model Is an Impressionist Painting10. The Top Two Assets of Any Business Today Are People and Data; UNDERSTANDING TEXT DATA; SUMMARY; CHAPTER 4 Tackling Fraud: The Ten Commandments; 1. DATA: GARBAGE IN; GARBAGE OUT; 2. NO DOCUMENTATION? NO CHANGE!; 3. KEY EMPLOYEES ARE NOT A SUBSTITUTE FOR GOOD DOCUMENTATION; 4. RULES: MORE DOESN'T MEAN BETTER; 5. SCORE: NEVER REST ON YOUR LAURELS; 6. SCORE + RULES = WINNING STRATEGY; 7. FRAUD: IT IS EVERYONE'S PROBLEM; 8. CONTINUAL ASSESSMENT IS THE KEY; 9. FRAUD CONTROL SYSTEMS: IF THEY REST, THEY RUST. | |
505 | 8 | _a10. CONTINUAL IMPROVEMENT: THE CYCLE NEVER ENDSSUMMARY; CHAPTER 5 It Is Not Real Progress Until It Is Operational; THE IMPORTANCE OF PRESENTING A SOLID PICTURE; BUILDING AN EFFECTIVE MODEL; 1. Operations Personnel Need to Understand the Concept of a Fraud Score; 2. The Score Development Process Must Take into Consideration Operational Use and Constraints; 3. In General, Fraud Strategies Should Complement and Not Compete with the Fraud Score; 4. Fraud Strategies and Operational Processes Should Be Well Documented; SUMMARY; CHAPTER 6 The Chain Is Only as Strong as Its Weakest Link. | |
505 | 8 | _aDISTINCT STAGES OF A DATA-DRIVEN FRAUD MANAGEMENT SYSTEM. | |
650 | 0 |
_aBanks and banking _xSecurity measures. |
|
650 | 0 |
_aBank fraud _xPrevention. |
|
650 | 0 |
_aBank fraud _xPrevention _xTechnological innovation. |
|
650 | 4 | _aBank fraud. | |
650 | 4 | _aBanks and banking. | |
650 | 4 | _aCommercial crimes. | |
650 | 4 | _aWhite collar crimes. | |
650 | 7 |
_aBUSINESS & ECONOMICS _xFinance. _2bisacsh |
|
655 | 4 | _aElectronic books. | |
776 | 0 | 8 |
_iPrint version: _aSubramanian, Revathi. _tBank fraud. _dHoboken, New Jersey : John Wiley and Sons, Inc, [2014] _z9780470494394 _w(DLC) 2013046697 |
830 | 0 | _aWiley and SAS business series. | |
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_uhttp://onlinelibrary.wiley.com/book/10.1002/9781118886168 _zWiley Online Library [Free Download only for SUST IP] |
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