Fraud Control - IT Interventions and Solutions

1. Fraud Control - IT Interventions and Solutions

2. Key considerations for the functional solution • Understand the difference between abuse and fraud: • Fraud: knowingly, intentionally, willfully, ongoing for direct financial gain • Abuse: excessive, unwarranted, potentially not needed • Provide practical insights to insurers, through portfolio analysis and comparison to industry benchmarks • Focus on obtaining a demonstrable return on investment from project by prioritizing high financial loss practices, such as systematic collusion Core Principles • Deliver tools that can be deployed at all levels, ie: broker / agent / insurer / TPA / regulator and across functions – distribution / underwriting / claims processing • 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

3. Solution Proposed Components of the proposed solution Domain Knowledge

4. Solution Proposed – Holistic View MIS & Fraud Detection Reports Additional requirements Aggregate Level Fraud Modeling Predictive Modeling Anomaly Detection Rules Social network analytics Provider Registration Portal Functional Solution Real-time Fraud Detection at various stages Standardized IDs for providers & employers Detection at Underwriting Detection at Preauthorization Detection at Claims Process Stage ICD 10 Coding Integrated Data Operational Data Store (ODS) Data Cubes Data Marts Procedure codes Data Integration Technical Solution Extract, Transform & Load (ETL) Data Quality – Cleansing, Profiling Data Standardization & Certification Transactional Data Claims Policy Member Lookup Data

5. 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.

6. Proposed Technical Solution

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. Core Principles • 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. • 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.

8. The 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 Technical Solution Overview Oracle Enterprise Ed SAS FFI (SAS Enterprise BI) Oracle + SAS Cubes SAS FFI (SAS Enterprise DI) SAS FFI (Base SAS, Enterprise Miner, OLAP Cube Studio) Fraud Suspect Extracts / Investigation feedback

9. DISC Analytics Methodology closely weaves business outcome with the statistical techniques Model Development & Modeling Techniques Modeling Techniques proposed Simulate Consult Define Investigate • 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 Data Extraction from different sources X (Contd.) Predictive Modeling Fine tune the model • 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. Claims Data Merging No Is Model Adequate Data Cleaning Outliers Detection Yes Score the Validation Data Exploratory Data Analysis • 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 Identify the Variables for the Model Examine the predictive ability Data Split No No Is Satisfactory Is Adequate Claims Segmentation Yes Yes Results and Insights X.

10. Exploratory Data Analysis Sample

11. Decision Tree Analysis Sample

12. Neural Networks Sample

13. Cognizant’s 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 Fraud Management Workbench Sixth Sense Solution

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