Counter fraud management solution provides detection, response, and

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Financial fraud analysis
Counter fraud management solution provides detection, response, and
investigation of fraudulent transactions by collecting data from multiple
sources and applying analytical models across various touch points in
the transaction chain.
This solution is based on the Data and Analytics Reference Architecture.
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Runtime flow
1. Enterprise compliance officers customize and
configure the analytical processing system and
deploy analytical models to look for identity theft
and to correlate financial activity.
5. Analytical learning models are created in IBM SPSS
and business rules are applied to auto-classify
outliers, assign risk categories, and hand over to
agents for case management.
2. Data is collected from multiple sources such as
financial transactions, non-financial transactions,
profile transactions, and more.
6. User’s profile data is updated as transactions are
marked suspect and case management applications
are used to track the investigation.
3. Collected data is correlated with other data sources
such as sensor data, partner data within the
ecosystem, and more.
7. Visualization tools are used by compliance and fraud
officers to discover hidden linkages in flagged
transactions. This data is used to update analytical
models for improved future accuracy.
4. All raw data is sent to the data repository (IBM
BigInsights®). Analytical models are deployed to
detect outliers such as profile anomaly detection, link
detection, and more.
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Components
COMPONENT
DEFINITION
PRODUCT
Data sources
Includes different information sources that may
contain data of interest for cyber security such as
NetFlow, DNS, network logs, and more.
Streaming computing
Includes stream processing systems that ingest and
process large volumes of highly dynamic, timesensitive continuous data streams.
InfoSphere® Streams
Data integration
Copies and correlates information from disparate
sources to produce meaningful associations related
to primary business dimensions.
InfoSphere Information Server
Data repositories
Organizes the data stored in the cloud environment
into repositories supplied by the cloud provider.
IBM BigInsights for Apache Hadoop,
InfoSphere for Apache Hadoop
Enterprise user directory
Provides storage for and access to user information
to support authentication, authorization, or profile
data.
IBM Security Directory Server
Actionable insight
Includes SaaS or on-premises applications which are
used to derive information from the data in a
convincing and understandable manner upon which
an organization can take an action.
IBM SPSS family
Enterprise data
Includes metadata about the data and systems of
record for enterprise applications.
InfoSphere Master Data Management
This is one product mapping for this scenario. For other applicable products, see the Data and Analytics Reference Architecture and the
other scenarios that are based on it.
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Business drivers
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Reduce vulnerability to fraudulent
transactions and complex fraud
schemes.
Fraud is no longer an “acceptable
cost of doing business”.
Operational losses apply
significant pressure on
profitability.
Customer convenience, trust and
satisfaction drive brand choice
and must be earned on an
ongoing basis.
Requirements
Functional requirements
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•
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The solution must support both structured and
unstructured data sources.
The solution must provide analytics across all data
sources and customer touch points.
Integration with existing case management and other
forensic applications is required.
The solution must use predictive modeling techniques
on “at risk” networks to identify hidden fraud and to
better prioritize “organized fraud”.
The solution must provide a mechanism to reduce
false positives by applying new analytics on top of
flagged transactions.
Non-functional requirements
Security
• Reduce false positives in fraudulent transactions.
Performance
• Reduce time in processing low-risk claims.
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