fraud detection in banking using big data by madhu malapaka [email protected] for isaca,...

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Fraud Detection in Banking using Big Data

ByMadhu [email protected]

For ISACA, Hyderabad ChapterDate: 14th Dec 2014

Wilshire Software Technologies

Revised: 14th Dec 2014

mailto:[email protected]

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Agenda



• 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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• A deception deliberately practiced in order to secure unfair or unlawful gain or causing loss to another party.



Fraud

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• A bank is typically exposed to different types of frauds.



Common Banking Frauds

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• Fraud fighting activities can be grouped into three primary categories:

Fraud Prevention - Proactive Fraud Detection - Reactive Fraud Investigation - Action



Fraud Fighting Activities

6Wilshire Software Technologies


Source: www.executiveboard.com

Enterprise Fraud Systems Diagnostic Anatomy

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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

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Policy

Data Collection

Data Logs

Banking

Servers

Data Analysis

Fraud Detection

Compliance

Legal Action



Report Management

Users

ATMS

ONLINE

CREDIT

External Data Feeds

FRAUD DETECTION

FRAUD

PREVENTION

FraudMAP™

Reputation Manager 360

FRAUD ACTIONS

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FRAUD

PREVENTION

Monitoring Account Holder Behavior• It is organized around different phases or aspects of the online

banking process.



FRAUD

PREVENTION

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Policy

Data Collection

Data Logs

Banking

Servers

Data Analysis

Fraud Detection

Compliance

Legal Action



Report Management

Users

ATMS

ONLINE

CREDIT

External Data Feeds

FRAUD DETECTION

FRAUD

PREVENTION

FRAUD ACTIONS

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How Banks can leverage Data Mining

capabilities of

Big Data for

Fraud Detection





• 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



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


Revised: 21/10/2013

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.



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



Hadoop Ecosystem



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



Social Network Data Providers

• This data works as input data to build big-data and can integrate with Bank’s Customer data.

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CRM/customer supportPOS/purchasesemail/documents/collab.BI & data warehousesystem & network logsweb logs/clickstreamgoogle analytics/omniturefacebook/twitter/yelp/foursquare/googleexperian/epsilon/acxiommobile devicessensorsproduct reviewsgoogle search results+ more

many terabytes of data,sometimes many

PETABYTES

Banks Internal and Purchased Data



BIG DATA



Big Data Integration – Technology Stack


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Data Logs

RDBMS

Analytics


Revised: 21/10/2013

Reporting Tools

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81% of global bankssay 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 MalapakaManaging DirectorWilshire Software TechnologiesHyderabad, IndiaCell +91 800 820 [email protected]

www.wilshiresoft.com



http://www.wilshiresoft.com/

fraud detection in banking using big data by madhu malapaka [email protected] for isaca,...

Documents

fraud slide

fraud prevention slide

big data slide

distributed data processing

common banking frauds

data processing primitives

volume fastmoving data

online banking process