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Bank fraud : using technology to combat losses / / Revathi Subramanian
| Bank fraud : using technology to combat losses / / Revathi Subramanian |
| Autore | Subramanian Revathi |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (193 p.) |
| Disciplina | 332.1068/4 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Banks and banking - Security measures
Bank fraud - Prevention Bank fraud - Prevention - Technological innovation |
| ISBN |
1-118-23397-2
1-118-88616-X 1-118-22032-3 |
| Classificazione | BUS027000 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910132237203321 |
Subramanian Revathi
|
||
| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Bank fraud : using technology to combat losses / / Revathi Subramanian
| Bank fraud : using technology to combat losses / / Revathi Subramanian |
| Autore | Subramanian Revathi |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (193 p.) |
| Disciplina | 332.1068/4 |
| Collana | Wiley & SAS Business Series |
| Soggetto topico |
Banks and banking - Security measures
Bank fraud - Prevention Bank fraud - Prevention - Technological innovation |
| ISBN |
1-118-23397-2
1-118-88616-X 1-118-22032-3 |
| Classificazione | BUS027000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 |
| Record Nr. | UNINA-9910807275403321 |
|
Subramanian Revathi
|
||
| Hoboken, New Jersey : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||