Transactional Data Analysis
for Antifraud decision support
       JSM, Denver, August 7th 2008


      Raul Moreno, David ...
Agenda

     I.     Fraud, a deception deliberately practiced
     II.    Fraud detection methods
     III.   Basic approa...
I. Fraud, a deception deliberately practiced
                  in order to secure unfair or unlawful gain


The fraud risk...
I. Fraud, a deception deliberately practiced
                  in order to secure unfair or unlawful gain


Some figures

...
II.   Fraud detection methods



Behavior
Attack                                 Defend

                                 ...
II.   Fraud detection methods



Behavior
    System behavior


     Customer behavior




     Fraud Behavior
II.   Fraud detection methods



Statistical fraud detection
(Bolton, Hand 2002)

Supervised
Logistic discrimination, neur...
III. Basic approach



Using Influence Diagram
III. Basic approach



Compacting through analysis computations
III. Basic approach



Some real world examples
   Accounting fraud                         Credit card fraud
III. Basic approach



Integration with existing technologies, datamining
Bad debt fraud

                                ...
III. Basic approach



Integration with existing technologies, learning
IV.   A numerical example



Credit card fraud example in detail
IV.   A numerical example



Scenarios, based on utility function

Scenario A: Fraudsters / Black Lists



Scenario B: Pre...
V.    Implementation insights



The framework

Conceptual Design
    1.    Experts build a basic influence diagram repres...
V.   Implementation insights



Architecture

                    Interface Layer (influence diagram)




     Application...
VI.                Discussion



Benefits
                                                                           Inter...
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Transactional Data Analysis for Antifraud decision support

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I. Fraud, a deception deliberately practiced
II. Fraud detection methods
III. Basic approach
IV. A numerical example
V. Implementation insights
VI. Discussion

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Transactional Data Analysis for Antifraud decision support

  1. 1. Transactional Data Analysis for Antifraud decision support JSM, Denver, August 7th 2008 Raul Moreno, David Rios
  2. 2. Agenda I. Fraud, a deception deliberately practiced II. Fraud detection methods III. Basic approach IV. A numerical example V. Implementation insights VI. Discussion
  3. 3. I. Fraud, a deception deliberately practiced in order to secure unfair or unlawful gain The fraud risk history Troy, 13th BC Lazarillo de Tormes, 16th Enron Corp., 2002 Fraud Types Specialised, Financial - Bad debt, Investments fraud, Credit Card, Money Laundering, Nigerian letters. Telecom – Subscription fraud, VOIP fraud. Insurance and Health Care fraud, Computer frauds and some cross-sectorials such as Identity theft New risks from ICT New technologies, new forms of fraud, new needs for anti-fraud Real time requirements and new focus around the new fraud driver: the transaction
  4. 4. I. Fraud, a deception deliberately practiced in order to secure unfair or unlawful gain Some figures •UK fraud courts: 1.02bn in 2007, 221m in 1995 (KPMG Forensic) •Card fraud: 90.5 m and Online banking in 2007 (UK APACS) •USA total fraud: 49.3bn in 2007 (Javelin Strategy & Research) •VAT fraud in Europe: 130bn in 2007 (International VAT Association)
  5. 5. II. Fraud detection methods Behavior Attack Defend Normal Anti-fraudster Reactive, scoring profiles, detailed Fraudster segmentation, combining existing with Anonymous, informed, extracted knowledge from data. hibernation and transformation capabilities, discret but not Excellent Anti-fraudster invisible, using knowledge to Proactive, evaluate the utility of any discover weaknesses, gain solution state, define fraud probabilities, knowledge and confidence learn new patterns from experience, from experience. Looking for problem understanding, Integrate existing technologies and knowledge,
  6. 6. II. Fraud detection methods Behavior System behavior Customer behavior Fraud Behavior
  7. 7. II. Fraud detection methods Statistical fraud detection (Bolton, Hand 2002) Supervised Logistic discrimination, neural network, SVM’s, Rule based methods, CART Unsupervised Profiling Outlier detection methods
  8. 8. III. Basic approach Using Influence Diagram
  9. 9. III. Basic approach Compacting through analysis computations
  10. 10. III. Basic approach Some real world examples Accounting fraud Credit card fraud
  11. 11. III. Basic approach Integration with existing technologies, datamining Bad debt fraud Datamining integration
  12. 12. III. Basic approach Integration with existing technologies, learning
  13. 13. IV. A numerical example Credit card fraud example in detail
  14. 14. IV. A numerical example Scenarios, based on utility function Scenario A: Fraudsters / Black Lists Scenario B: Premium Scenario C: SLA Scenario D: SLA & real operating costs
  15. 15. V. Implementation insights The framework Conceptual Design 1. Experts build a basic influence diagram representing the problem. Improving Design 2. Expanding influence diagram by performing datamining against data repositories, such as the datawarehouse. 3. Adding hypernodes on the random nodes to allow probability learning, also using data repositories. Execution 4. Solving qualitatively the influence diagram to determine the maximum expected utility. Precomputing the required posterior distributions. 5. If amenable, precomputing optimal antifraud decisions, given relevant antecessors. Results 6. For each new transaction, apply or compute the optimal decision. Updating model from data changes 7. Either periodically or after each transaction the probability model would be recomputed, and possibly the utility function.
  16. 16. V. Implementation insights Architecture Interface Layer (influence diagram) Application Layer (embedded, distributed or as an stand alone app) Semantic layer (enriching with probability and math functions) Business Intelligence Resolved by Data layer (streams, databases, cubes, files, messages)
  17. 17. VI. Discussion Benefits Inter nal O nly! I. Antifraud business user interface II. Learning capabilities, reducing the execution gap Self Learning t+X Monitorize Right Performance? Deployment Monitorize Model Deployment learning No Right Performance? Model Yes Self Right Performance? No Self Learning + Monitorize Monitorize Yes Life time Model Life time Current Current Deployment Deployment Model Model Model Data Design Data Design t Current approach Proposed approach
  18. 18. Thank you!

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