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Is Machine learning useful for Fraud Prevention?

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Is Machine learning useful for Fraud Prevention?

  1. 1. Introduction Expert Driven approach Data Driven approach Tools Conclusion Is Machine Learning useful for Fraud prevention? Andrea Dal Pozzolo 22/07/2015 1/ 18
  2. 2. Introduction Expert Driven approach Data Driven approach Tools Conclusion INTRODUCTION Frauds are old as the human race. They follow the money, e.g. credit cards are well-know for being targeted by fraudulent activities. We witness a growing presence of frauds on online transactions. Need of automatic systems able to detect and fight fraudsters. 2/ 18
  3. 3. Introduction Expert Driven approach Data Driven approach Tools Conclusion THE PROBLEM Fraud detection is notably a challenging problem because: Fraud strategies change in time, as well as customers’ spending habits evolve. Few examples of frauds available, so it is hard to model fraudulent behaviour. Not all frauds are reported or reported with large delay. Few transactions can be timely investigated. 3/ 18
  4. 4. Introduction Expert Driven approach Data Driven approach Tools Conclusion THE PROBLEM II With the large number of transactions we witness everyday: We cannot ask human analyst to check every transactions one by one. We wish to automatise to detection of fraudulent transaction. We want accurate predictions, i.e. minimise missed frauds and false alarms. Two standard approaches for FD: Expert Driven Data Driven 4/ 18
  5. 5. Introduction Expert Driven approach Data Driven approach Tools Conclusion EXPERT DRIVEN APPROACH A straightforward approach to automatise detection is to define rules that exploit fraud expert knowledge. E.g. IF transaction amount > e 10’000 & Betting website THEN Class = FRAUD 5/ 18
  6. 6. Introduction Expert Driven approach Data Driven approach Tools Conclusion CASE STUDY Rule: IF N Trans > 80 AND Tot Amt > 2000 THEN fraud Rule: ?? We can learn this by means of Machine Learning 6/ 18
  7. 7. Introduction Expert Driven approach Data Driven approach Tools Conclusion EXPERT DRIVEN PROS & CONS Pros Easy to develop. Easy to understand. Explain why an alert was generated. Exploit Domain Expert knowledge. Cons Subjective (Ask 7 experts, get 7 opinions). Hard boundaries. Difficulties thinking in more than 3 dimensions. Detect only easy correlations between variables and frauds. Able to detect only known fraudulent strategies. Become obsolete soon (fraud evolution). 7/ 18
  8. 8. Introduction Expert Driven approach Data Driven approach Tools Conclusion DATA DRIVEN APPROACH Use Machine Learning to learn automatically rules able to find fraudulent patterns. E.g. COUNTRY=USA & LANGUAGE=EN & HAD TEST=TRUE & NB TX>10 & GENDER=MALE & AGE> 50 & ONLINE=TRUE & AMOUNT>1000 & BANK=XXX THEN fraud 8/ 18
  9. 9. Introduction Expert Driven approach Data Driven approach Tools Conclusion WHAT’S MACHINE LEARNING? The design of algorithms that discover patterns in a collection of data instances in an automated manner. The goal is to use the discovered patterns to make predictions on new data. Figure : Training What is Machine Learning? The design of computational systems that discover patterns in a collection of data instances in an automated manner. The ultimate goal is to use the discovered patterns to make predictions on new data instances not seen before.                                Instead of manually encoding patterns in computer programs, we make computers learn these patterns without explicitly programming them . Figure source [Hinton et al. 2006]. 2 Figure : Testing Instead of manually encoding patterns in computer programs, we make computers learn these patterns without explicitly programming them.9/ 18
  10. 10. Introduction Expert Driven approach Data Driven approach Tools Conclusion MACHINE LEARNING PROS & CONS Pros Learn complex fraudulent pattern (use all features). Can ingest large volumes of data. Optimally model complex shapes. Predict new types of fraud. Adapt to changing distribution (fraud evolution). Cons Need enough samples. Some models are black box (not interpretable by investigators) 10/ 18
  11. 11. Introduction Expert Driven approach Data Driven approach Tools Conclusion IMPLEMENTATION Implementation steps: 1. Feature engineering (i.e. enriching the data using in-house information and external sources) 2. Transaction aggregation (create new features to model customer behaviour) 3. Train a ML model on the data and use it to predict new transactions. 4. Integrate feedbacks from investigators to improve the detection. 11/ 18
  12. 12. Introduction Expert Driven approach Data Driven approach Tools Conclusion CHOOSING THE ALGORITHM Thousands of ML algorithms available. The best one does not exist (No-free lunch theorem). However, some have better performances under certain conditions. Several studies have reported that Random Forest [3] is the most accurate for fraud detection [8, 2, 5, 7, 4, 1]. 12/ 18
  13. 13. Introduction Expert Driven approach Data Driven approach Tools Conclusion RANDOM FOREST Ensemble of decision trees (combination of >100 models). Robust to irrelevant feature. Easy to scale with Bid Data architecture (e.g. Hadoop). Return feature relevance. Rule extraction is possible. Figure : Decision Tree: predict play/not play based on weather conditions. 13/ 18
  14. 14. Introduction Expert Driven approach Data Driven approach Tools Conclusion MACHINE LEARNING TOOLS Which software should I use? R [6] appears to be the standard between data scientist. (a) kdnuggets survey 2015 (b) Rexer Analytics survey 2013 (c) Software used in Kaagle data analysis competitions in 201114/ 18
  15. 15. Introduction Expert Driven approach Data Driven approach Tools Conclusion WHY R? Open source (free) & developed by academics. Almost all ML algorithms implemented. 1 Microsoft, Amazon, IBM, SAP and many others have Machine Learning solutions based on R. 1 http://cran.r-project.org/web/views/MachineLearning.html 15/ 18
  16. 16. Introduction Expert Driven approach Data Driven approach Tools Conclusion WORRIED ABOUT R SUPPORT? Huge community of R-users. Most books/manuals available are free. Several R-consulting companies. Figure : Software popularity on statistically-oriented forums. 16/ 18
  17. 17. Introduction Expert Driven approach Data Driven approach Tools Conclusion CONCLUSION Machine Learning can efficiently support fraud detection. ML allows to automatise detection and reaction to frauds. Expert Driven and Data Driven approaches have both pros and cons. ML is not going to replace Expert Driven rules, but it allows to reduce False Positive. Random Forest is often the most accurate model for FD. I recommend to use the R software. 17/ 18
  18. 18. Introduction Expert Driven approach Data Driven approach Tools Conclusion Web: www.ulb.ac.be/di/map/adalpozz Email: adalpozz@ulb.ac.be Thank you for the attention 18/ 18
  19. 19. Introduction Expert Driven approach Data Driven approach Tools Conclusion BIBLIOGRAPHY [1] A. C. Bahnsen, A. Stojanovic, D. Aouada, and B. Ottersten. Cost sensitive credit card fraud detection using bayes minimum risk. In Machine Learning and Applications (ICMLA), 2013 12th International Conference on, volume 1, pages 333–338. IEEE, 2013. [2] S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland. Data mining for credit card fraud: A comparative study. Decision Support Systems, 50(3):602–613, 2011. [3] L. Breiman. Random forests. Machine learning, 45(1):5–32, 2001. [4] A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi. Credit card fraud detection and concept-drift adaptation with delayed supervised information. In Neural Networks (IJCNN), 2015 International Joint Conference on. IEEE, 2015. [5] A. Dal Pozzolo, O. Caelen, Y.-A. Le Borgne, S. Waterschoot, and G. Bontempi. Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41(10):4915–4928, 2014. [6] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2015. [7] V. Van Vlasselaer, C. Bravo, O. Caelen, T. Eliassi-Rad, L. Akoglu, M. Snoeck, and B. Baesens. Apate: A novel approach for automated credit card transaction fraud detection using network-based extensions. Decision Support Systems, 2015. [8] C. Whitrow, D. J. Hand, P. Juszczak, D. Weston, and N. M. Adams. Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery, 18(1):30–55, 2009. 19/ 18

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