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©2011, Cognizant
Fraud Control - IT Interventions and Solutions
| ©2011, Cognizant 2
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
| ©2011, Cognizant 3
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
| ©2011, Cognizant 7
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.
| ©2011, Cognizant 8
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
| ©2011, Cognizant
Exploratory Data Analysis
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Sample
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Decision Tree Analysis
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Sample
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Neural Networks
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Sample
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Cognizant’s Fraud Management Workbench
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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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