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Fraud Control - IT Interventions and Solutions

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Key considerations for the functional solution

•Provide practical insights to insurers, through portfolio analysis and comparison to industry benchmarks

•Understand the difference between abuse and fraud: Fraud: knowingly, intentionally, willfully, ongoing for direct financial

gain Abuse: excessive, unwarranted, potentially not needed

•Focus on obtaining a demonstrable return on investment from project by prioritizing high financial loss practices, such as systematic collusion

•Deliver tools that can be deployed at all levels, ie: broker / agent / insurer / TPA / regulator and across functions – distribution / underwriting / claims processing

Core Principles

•A solution that provides a comprehensive data analysis and reporting environment facilitating MIS and fraud analytics reports, to dissect and highlight patterns trends, volume and scope of fraudulent claims observed

•Strengthening future data capture initiatives and develop greater data analysis capabilities within the insurance company

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Solution Proposed

Components of the proposed solution

DomainKnowledge

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Solution Proposed – Holistic View

4

MIS & Fraud Detection Reports

Aggregate Level Fraud Modeling

Anomaly Detection

RulesSocial network

analyticsPredictive Modeling

Real-time Fraud Detection at various stages

Detection at Underwriting

Detection at Claims Process Stage

Detection at Preauthorization

IntegratedData

Operational Data Store (ODS)

Data Cubes Data Marts

DataIntegration

Extract, Transform & Load (ETL)

Data Quality – Cleansing, Profiling

Data Standardization & Certification

Transactional Data

Member Claims Lookup DataPolicy

Provider Registration Portal

Standardized IDs for providers &

employers

Procedure codes

ICD 10 Coding

Additional requirements

Tech

nic

al S

olu

tion

Fu

ncti

on

al S

olu

tion

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Functional Solution: Aggregate level Fraud Modeling & Analysis using data

Flexibility: predictive models for fraud detection should be built using different statistical methods; the final models should be determined after analyzing the results.

Focus on enhancing predictive values (also reducing false positives) and continuous improvement as new data fields becomes available.

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Proposed Technical Solution

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Key Considerations for the Technical Solution

•Need for a Platform that can provide end-to-end capabilities, starting with Data Integration, Statistical Modeling, Fraud Detection, BI & Reporting.

• To choose a tool that supports advanced analytic approaches and fraud risk scoring techniques like anomaly detection, social network analysis.

•To build a comprehensive Operational Data Store (ODS) to hold persistent source system data in a standard model for reporting & analytical requirements.

•An unique approach to combine Modeling techniques to leverage the unique aspects of each of the techniques be it logistic regression, decision trees or neural networks.

Core Principles

•A solution that provides a comprehensive data analysis and reporting environment with MIS and fraud analytics reports, to dissect and highlight patterns trends, volume and scope of fraudulent claims

•A solution which caters to current requirements and is extensible to other lines of business.

•Leverage industry specific relevant frameworks, methodologies and processes to ensure flawless and timely delivery with utmost quality.

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Technical Solution OverviewThe integrated data will consist of the Operational Data store (ODS), Data cubes built using SAS tools & Data marts. This data will provide the base for the models & reports to be built for the solution

SAS FFI (Base SAS, Enterprise Miner, OLAP

Cube Studio)

SAS FFI (Base SAS, Enterprise Miner, OLAP

Cube Studio)

Oracle + SAS CubesOracle +

SAS CubesSAS FFI (SAS Enterprise BI) SAS FFI (SAS Enterprise BI)

Fraud Suspect Extracts / Investigation feedback

Oracle Enterprise

Ed

Oracle Enterprise

EdSAS FFI (SAS Enterprise DI)SAS FFI (SAS Enterprise DI)

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Model Development & Modeling Techniques

Identify the Variables for the

Model

Identify the Variables for the

Model

No

Exploratory Data Analysis

Data Split

No

Data Extraction from different sources

Claims Data Merging

Data Cleaning

Is Adequate

Yes

X.

Predictive ModelingPredictive Modeling

Is Model Adequate

NoNo

X (Contd.)X (Contd.)

YesYes

Score the Validation Data

Examine the predictive ability

Is Satisfactory

YesYes

Results and Insights

Claims Segmentation

Outliers Detection

Fine tune the model

Logistic Regression• Statistical technique used to identify the likelihood

of occurrence of a binary/ categorical outcome using multivariate inputs

• Logistic Regression can estimate the probability of making a fraud claim in next few months

Decision Tree• Decision Tree divides the population into segments

with the greatest variation in the objective variable at each segment . The algorithms usually work top-down

• Decision Tree supports in identification of the segments which are more likely to have fraud concentration

• The key variables/logic , that identify the fraud concentration in decision tree can also be used in Neural network for instant Fraud detection.

Neural Network• Artificial Neural network is non-linear data analytical

process used to identify complex relationships between inputs and output

• By detecting complex nonlinear relationships in data, neural networks can help make accurate predictions about real-world problems.

• Integrated learning capabilities in Neural network , where the significant logic coming out of Decision tree and logistic regression can be feed in .

• This will enable to continuously monitor and refine detection rules and techniques to reduce false positives and identify and respond to emerging threats

Investigate ConsultSimulateDefine

DISC Analytics Methodology closely weaves business outcome with the statistical techniques Modeling Techniques proposed

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Exploratory Data Analysis

10

Sample

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Decision Tree Analysis

11

Sample

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Neural Networks

12

Sample

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Cognizant’s Fraud Management Workbench

13

Fraud Management Workbench

Fraud Management Workbench will enable SIU users orchestrate the complete process of investigating a suspect claim referred to SIU, analyze the claim by its merits and label the claim to its logical closure

Sixth Sense

Solution

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©2011, Cognizant

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