Use of Data Mining and Machine. Use of Data Mining and Machine. Learning for Fraud Detection. Learning for Fraud Detection. Welcome!

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1 Use of Data Mining and Machine Use of Data Mining and Machine Learning for Fraud Detection CPE PIN Code: UKLK Learning for Fraud Detection Welcome! Ramazan Isik, CFE, CIA, CRISC, CRMA Chief Audit Executive DenizBank Denizbank (Sberbank Group)

2 Use of Data Mining /Machine Learning for Fraud Detection Ramazan Işık, CFE, CIA, CRISC, CRMA Chief Служба Audit внутреннего Executive, аудита DenizBank January, 2017

3 Sberbank at a Glance The largest bank in Russia (by total assets, loans and deposits.) & One of the top 30 banks in the world 137 mio Retail & 1.1 mio Corporate Clients Presence in 22 Countries 16 territorial banks in Russia located across 11 time zones. EMPLOYEES COUNTRIES BRANCHES ATMs CUSTOMERS Mio 3

4 DenizBank at a Glance The 5th largest private bank in Turkey according to consolidated assets at $41 bn. One of the 2 private banks having branches in 81 Provinces in Turkey Presence in 6 Countries (Turkey, Russia, Austria, Germany, Cyprus, Bahrain) 1st at asset growth with 30% average in previous 10 years EMPLOYEES CITIES BRANCHES ATMs CUSTOMERS Mio 4

5 DenizBank s Innovation Culture DenizBank MOST INNOVATIVE BANK OF THE YEAR BAI 2014 DenizBank MOST INNOVATIVE BANK OF THE YEAR BAI 2016 DenizBank GLOBAL INNOVATOR EFMA ACCENTURE INNOVATION AWARDS 2015 Facebook Banking Twitter Loan fastpay Mobile Loan Financial Word Innovation Awards Most Innovative Application of Technology Grand Steve Sales& Customer Service Silver Stevie Dijital Deniz Facebook Banking Channel Innovation MPE Awards Best Payment Solution fastpay 5

6 Internal Audit Department Head of Internal Audit Branch Audit Head Office & Subsidiaries Audit Examinations & Investigations IT Audit Fraud Data Analytics 6

7 4 Vs of Big Data 7

8 Big Data 8

9 Data Mining Discovering patterns in large data sets, involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. 9

10 Machine Learning Machine Learning is the study and construction of algorithms and models that can learn from and make predictions on data. Generally operated by a model, for example, inputs for future predictions. Machine Learning MODEL PREDICTION 10

11 Machine Learning in Daily Life Face Recognition Virtual Personal Assistant Self-Driving Cars Road Traffic Monitoring and Prediction Personalization and Recommendation Systems Medical Diagnosis 11

12 Use of AI & Machine Learning in Financial Services 12

13 Use of AI & Machine Learning in Financial Services Customer Lifetime Value (CLV) Value at Risk Calculation Collateral Analysis Campaign Analytics Suspicious Activity Reporting Collection Delinquency Customer Segmentation Silent and Proactive Churn Social Media Listening Stress Testing Pattern Recognition and Machine Learning to Detect Fraud Simulations to Predict Default Risk Central Limit Management Determining Regulatory Capital 13

14 Is It Possible not Working with BD/ML? BD/ML is like the Internet in the early 2000s. Using BD/ML will provide a competitive advantage in the near future (2-5 years). Soon (after 5 years), not using BD/ML will be a competitive disadvantage. Available technologies will boom and experts will not be sufficient at all. Companies that have begun to implement and use a DB / ML technology will have the advantage of understanding and applying new technologies. 14

15 Business Analytics Practices in DenizBank Business Analytics Practices in DenizBank 15

16 Analytical Journey of DenizBank CRM Marketing Models Churn Campaign Optimization Customer Lifetime Value KK (Revenue CRM Prediction) Borrower s Income Prediction Fraud External Fraud Internal Fraud Detection Sept 2005 Jan 2008 Mar 2010 Sep 2012 Dec 2012 Dec 2014 Jul Jul 2008 Apr 2011 Mar 2013 Mar 2013 Jul 2014 Risk PD, EAD, LGD Aplication Scorecard Overdue Debt Collection Optimization Operational Efficiency ATM Cash Optimization ATM Location Selection 16

17 Data Analytics Practices in the Internal Audit Department Data Analytics Practices in Internal Audit Department 17

18 Use of Data Analytics for Fraud Detection Use of Data Analytics for Fraud Detection 18

19 Why We Need Machine Learning to Detect Fraud? 59 Rule-Based Scenarios Scenario Results Per Day Millions of Transactions-Customers Different Employee Profiles High Number of False Positive Scenario Results 19

20 Objectives of the Inter-Fraud Tool Project Easy interface integrated with the core banking platform Very easy to compose a scenario without an IT development and coding Decreasing false positive results for making more accurate sampling Analyzing links and relations among customers and employees Making quick queries for past transactions 20

21 Infrastructure Inter-Fraud Tool Datamarts (customer, employee, transaction, relation) The model was developed with IBM SPSS Modeler and coded into the core banking platform Data mining technique: Decision tree Anomaly identification in terms of employee, customer, relation, and transaction 21

22 Infrastructure Inter-Fraud Tool Learning through newly encountered types of fraud cases Easy-to-use interface Link analysis: Diagrams showing both financial and non-financial relations Determining employee risk 22

23 3 Pillars of the Inter-Fraud Tool Data Mining & Learning Algorithm Detection of Suspicious Transactions Investigation of Suspicious Transactions 23

24 Data Mining & Learning Algorithm Data Mining & Learning Algorithm Detection of Suspicious Transactions Investigation of Suspicious Transactions 24

25 State-of-the-Art Machine Learning Machine learning algorithm; Learning: Similar patterns of fraud cases by using real data from 2012, 2013, 2014, and Prediction: Using the model, rules, and the input to reach approximate or definite results which are similar to previous fraud cases. 25

26 Analytical Dimension of Inter-Fraud Tool Transaction Anomaly Score Customer Anomaly Score Employee Anomaly Score Relation Anomaly Score Transaction Datamart (69) Customer Datamart (380) Employee Datamart (420) Relation Datamart (45) Trx Amount Currency Code Trx Channel Order User Cash Cover Balance/Limit Debit/Credit Applicant Beneficiary... Customer Age Customer Type Channel Usage Transaction Types Transaction Frequency Transaction Amount RFM... Employee Age Mission Name Off-Hours Transactions Number of Trx/Customer Customer Monitoring Logs Performance... Related customer/ other/employee Relationship Type(78) (family, financial, business,neighbor...) Relation RFM Score... Transaction Risk Score 26

27 Anomaly Identification Customer Behavior Anomalies Employee Behavior Anomalies Frequency of financial transactions Channel usage habits Type of transactions, time of transactions, amount of transactions Work hours Query types and frequencies (frequency of customer signature, id, queries) Transaction types, amounts, frequencies Relation Anomalies Transaction Anomalies What is the relation type? (Relative, sister, brother, neighbor, same school.) Link Analysis Recency analysis Frequency Analysis Monetary Analysis 27

28 Detection of Suspicious Transactions Data Mining & Learning Algorithm Detection of Suspicious Transactions Investigation of Suspicious Transactions 28

29 Easy to Compose a Scenario 29

30 Easy to Compose a Scenario Composing a scenario from a data source or combining two scenarios without IT development Pre-defined best practice scenarios Exception lists Parametric structure for more effective scenarios 30

31 Easy-to-Use Inter-face 31

32 Easy-to-Use Inter-face Easy filtering scenario results Advanced data mining and transaction risk score to focus on riskier cases Easy access to investigation tools 32

33 Investigation of Suspicious Transactions Data Mining & Learning Algorithm Detection of Suspicious Transactions Investigation of Suspicious Transactions 33

34 Powerful Investigation Tools 34

35 Powerful Investigation Tools Simulation screen for past query Link analyses function Financial relations Non-financial relations Powerful investigation modules Customer monitoring Employee monitoring Case history Employee-customer Relations monitoring 35

36 Main Structure of Employee-Risk Matrix Employee risk score Credit history of the employee Duration of working period with other colleagues Job classification of the employee Past disciplinary penalties of the employee Vacation behaviors (not taking annual leave/working while on annual leave) Working period in the recent role

37 Benefits of the Inter-Fraud Tool Compared to Previous Tool 38% Decreased (354 high risk decreased to 220 high risk) % Time Saving (9 hours to 1 hours) 90% More Efficient (1 hour to 6 min.) % Decreased (10 min. to 3 min.) 89% Time Saving (9 hours to 1 hours) 05 37

38 Significant Cases Detected by Inter-Fraud Tool Branch employee s transactions on his father's account for his benefit High volume commercial activities in Branch employee s account Branch employee s usage of his relative s credit card for his/her own benefit Money withdrawal transactions made by an unauthorized person from 94-year-old branch customer s account 38

39 Questions? Ramazan Işık, CFE, CIA, CRISC, CRMA Chief Audit Executive, DenizBank linkedin.com/in/ramazanisik

40 Welcome! Use of Data Mining and Machine Learning for Fraud Detection Ramazan Isik, CFE, CIA, CRISC, CRMA Chief Audit Executive DenizBank (Sberbank Group)