1. Fraud Detection in Banking using Big Data By Madhu Malapaka madhu@wilshiresoft.com For ISACA, Hyderabad Chapter Date: 14 th Dec 2014 Wilshire Software Technologies Revised: 14 th Dec 2014 1

2. Agenda Wilshire Software Technologies Revised: 14 th Dec 2014 2 Common Banking Frauds Fraud Fighting Activities Enterprise Fraud Systems Diagnostic Anatomy Big Data Hadoop Ecosystem Banks Data Source Social Network Data Providers Big Data Integration – Technology Stack Reporting Tools

3. A deception deliberately practiced in order to secure unfair or unlawful gain or causing loss to another party. Wilshire Software Technologies Revised: 14 th Dec 2014 3 Fraud

4. A bank is typically exposed to different types of frauds. Wilshire Software Technologies Revised: 14 th Dec 2014 4 Common Banking Frauds

5. Fraud fighting activities can be grouped into three primary categories:  Fraud Prevention - Proactive  Fraud Detection - Reactive  Fraud Investigation - Action Wilshire Software Technologies Revised: 14 th Dec 2014 5 Fraud Fighting Activities

6. Wilshire Software Technologies Revised: 14 th Dec 2014 6 Source: www.executiveboard.com Enterprise Fraud Systems Diagnostic Anatomy

7. 7 Policy Data Collection Data Logs Banking Servers Data Analysis Fraud Detection Compliance Legal Action Business Process Change Adopt New Technologies Report Management Users ATMS ONLINE CREDIT FRAUD PREVENTION FRAUD ACTIONS External Data Feeds FRAUD DETECTION

8. 8 Policy Data Collection Data Logs Banking Servers Data Analysis Fraud Detection Compliance Legal Action Business Process Change Adopt New Technologies Report Management Users ATMS ONLINE CREDIT External Data Feeds FRAUD DETECTION FRAUD PREVENTION FraudMA P™ Reputation Manager 360 FRAUD ACTIONS

9. 9 FRAUD PREVENTION Monitoring Account Holder Behavior It is organized around different phases or aspects of the online banking process.

10. Wilshire Software Technologies Revised: 14 th Dec 2014 10 FRAUD PREVENTION

11. 11 Policy Data Collection Data Logs Banking Servers Data Analysis Fraud Detection Compliance Legal Action Business Process Change Adopt New Technologies Report Management Users ATMS ONLINE CREDIT External Data Feeds FRAUD DETECTION FRAUD PREVENTION FRAUD ACTIONS

12. How Banks can leverage Data Mining capabilities of Big Data for Fraud Detection Wilshire Software Technologies Revised: 14 th Dec 2014 12

13. Wilshire Software Technologies Revised: 14 th Dec 2014 13 Velocity  Moves at very high rates (think sensor-driven systems).  Valuable in its temporal, high velocity state. Volume  Fast-moving data creates massive historical archives.  Valuable for mining patterns, trends and relationships. Variety  Structured (logs, business transactions).  Semi-structured and unstructured. BIG DATA

14. Wilshire Software Technologies Revised: 14 th Dec 2014 14  Hadoop is a combination of : HDFS  Storage MapReduce  Computation  Hadoop Distributed File System (HDFS) Distributed file system for redundant storage. Designed to reliably store data on commodity hardware.  MapReduce A programming model for distributed data processing. A data processing primitives are functions: Mappers and Reducers. BIG DATA BY HADOOP

15. Wilshire Software Technologies Revised: 21/10/2013 15 Hadoop Ecosystem  Pig High-level data flow language. Made of two components:  Data processing language Pig Latin (Pig Scripts).  Compiler to translate Pig Latin to MapReduce.  Hive Data Warehousing Layer on top of Hadoop. Allows analysis and queries using SQL–like language.  Mahout Scalable machine learning algorithms on top of Hadoop.

16. Wilshire Software Technologies Revised: 14 th Dec 2014 16  Sqoop A tool to automate data transfer between structured datastores and Hadoop.  Flume Distributed data/log collection service. Collects data/log from their sources and puts in a centralized location for storage and processing. Hadoop Ecosystem

17. Wilshire Software Technologies Revised: 14 th Dec 2014 17 Hadoop Ecosystem

18. Wilshire Software Technologies Revised: 14 th Dec 2014 18 Identify Data Sources Consider what data sources you’ll need to take advantage of.  Existing data sources This includes a wide variety of data, such as transactional data, survey data, web logs, etc.  Purchased data sources Does your organization use supplemental data, such as demographics? If not, consider social media and news stream would complement your current data to create additional project value. Banks Data Source

19. Wilshire Software Technologies Revised: 14 th Dec 2014 19 Social Network Data Providers This data works as input data to build big-data and can integrate with Bank’s Customer data.

20. CRM/customer support POS/purchases email/documents/collab. BI & data warehouse system & network logs web logs/clickstream google analytics/omniture facebook/twitter/yelp/ foursquare/google experian/epsilon/acxiom mobile devices sensors product reviews google search results + more many terabytes of data, sometimes many PETABYTES Banks Internal and Purchased Data Wilshire Software Technologies Revised: 14 th Dec 2014 20 BIG DATA

21. Wilshire Software Technologies Revised: 14 th Dec 2014 21 Big Data Integration – Technology Stack

22. Wilshire Software Technologies 22 Data Logs RDBMS Analytics

23. Wilshire Software Technologies Revised: 21/10/2013 23 Reporting Tools

24. 81% of global banks say Big Data is a top priority in 2015 Are You Ready? Wilshire Software Technologies Revised: 14 th Dec 2014 24

25. Thank You! Questions? Wilshire Software Technologies, based in Hyderabad, India is engaged in Consulting & Training for Big Data Analytics. Contact Information: Madhu Malapaka Managing Director Wilshire Software Technologies Hyderabad, India Cell +91 800 820 4581 madhu@wilshiresoft.com www.wilshiresoft.com Wilshire Software Technologies Revised: 14 th Dec 2014 25