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

Agenda Wilshire Software Technologies Revised: 14 th Dec 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

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 Fraud

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

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 Fraud Fighting Activities

Wilshire Software Technologies Revised: 14 th Dec Source: Enterprise Fraud Systems Diagnostic Anatomy

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

Wilshire Software Technologies Revised: 14 th Dec FRAUD PREVENTION

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

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

Wilshire Software Technologies Revised: 14 th Dec 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

Wilshire Software Technologies Revised: 14 th Dec  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

Wilshire Software Technologies Revised: 21/10/ 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.

Wilshire Software Technologies Revised: 14 th Dec  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

Wilshire Software Technologies Revised: 14 th Dec Hadoop Ecosystem

Wilshire Software Technologies Revised: 14 th Dec 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

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

CRM/customer support POS/purchases /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 BIG DATA

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

Wilshire Software Technologies 22 Data Logs RDBMS Analytics

Wilshire Software Technologies Revised: 21/10/ Reporting Tools

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

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 Wilshire Software Technologies Revised: 14 th Dec