Fraud Detection in Banking
using Big Data
By
Madhu Malapaka
madhu@wilshiresoft.com
For ISACA, Hyderabad Chapter
Date: 14th Dec 2014
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Agenda
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•
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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
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Fraud
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A deception deliberately practiced in order to secure unfair or unlawful
gain or causing loss to another party.
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Common Banking Frauds
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A bank is typically exposed to different types of frauds.
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Fraud Fighting Activities
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Fraud fighting activities can be grouped into three primary categories:
 Fraud Prevention - Proactive
 Fraud Detection
- Reactive
 Fraud Investigation - Action
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Enterprise Fraud Systems Diagnostic Anatomy
Source: www.executiveboard.com
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ATMS
Policy
ONLINE
Users
CREDIT
Data
Collection
Data
Analysis
Compliance
Fraud Detection
External
Data Feeds
Data Logs
Legal Action
Business Process Change
Adopt New Technologies
Report Management
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ATMS
Policy
ONLINE
Users
CREDIT
Data
Collection
Data
Analysis
FraudMA
P™
Reputation Manager
360
Compliance
Fraud Detection
External
Data Feeds
Data Logs
Legal Action
Business Process Change
Adopt New Technologies
Report Management
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Monitoring Account Holder Behavior
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It is organized around different phases or aspects of the online
banking process.
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ATMS
Policy
ONLINE
Users
CREDIT
Data
Collection
Data
Analysis
Compliance
Fraud Detection
External
Data Feeds
Data Logs
Legal Action
Business Process Change
Adopt New Technologies
Report Management
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How Banks can leverage
Data Mining capabilities of
Big Data
for
Fraud Detection
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BIG DATA
• 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.
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BIG DATA BY HADOOP
 Hadoop is a combination of :
• HDFS
• MapReduce


Storage
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.
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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
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Data Warehousing Layer on top of Hadoop.
Allows analysis and queries using SQL–like language.
 Mahout
• Scalable machine learning algorithms on top
of Hadoop.
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Hadoop Ecosystem
 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.
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Hadoop Ecosystem
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Banks Data Source
Identify Data Sources
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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.
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Social Network Data Providers
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This data works as input data to build big-data and can integrate with
Bank’s Customer data.
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Banks Internal and Purchased Data
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
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many terabytes of data,
sometimes many
PETABYTES
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BIG DATA
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Big Data Integration – Technology Stack
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Analytics
Data Logs
RDBMS
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Reporting Tools
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81% of global banks
say Big Data is a top
priority in 2015
Are You Ready?
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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
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