Bank fraud : using technology to combat losses /
"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. Lo...
| Κύριος συγγραφέας: | |
|---|---|
| Μορφή: | Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Hoboken, New Jersey :
Wiley,
[2014]
|
| Σειρά: | Wiley and SAS business series.
|
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- Bank 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.
- Recording 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.
- Customer 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").
- 9. 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.
- 10. 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.
- DISTINCT STAGES OF A DATA-DRIVEN FRAUD MANAGEMENT SYSTEM.
