1. ©2011, Cognizant Fraud Control - IT Interventions and Solutions

2. | ©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

3. | ©2011, Cognizant 3 Solution Proposed Functional Solution Technical Solution Solution Proposed Components of the proposed solution Domain Knowledge

4. | ©2011, Cognizant Solution Proposed – Holistic View 4 MIS & Fraud Detection Reports Aggregate Level Fraud Modeling Anomaly Detection Rules Social network analytics Predictive Modeling Real-time Fraud Detection at various stages Detection at Underwriting Detection at Claims Process Stage Detection at Preauthorization Integrated Data Operational Data Store (ODS) Data CubesData Marts Data Integration 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 Technical Solution Functional Solution

5. | ©2011, Cognizant 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. | ©2011, Cognizant 6 Proposed Technical Solution

7. | ©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.

8. | ©2011, Cognizant 8 Technical Solution Overview The integrated data will consist of the Operational Data store (ODS), Data cubes built using SAS tools & Datamarts. 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) Oracle + SAS Cubes SAS FFI (SAS Enterprise BI) Fraud Suspect Extracts / Investigation feedback Oracle Enterprise Ed SAS FFI (SAS Enterprise DI)

9. | ©2011, Cognizant 9 Model Development & Modeling Techniques 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 Modeling Is Model Adequate No X (Contd.) Yes Score the Validation Data Examine the predictive ability Is Satisfactory Yes 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 ConsultSimulate Define DISC Analytics Methodology closely weaves business outcome with the statistical techniques Modeling Techniques proposed

10. | ©2011, Cognizant Exploratory Data Analysis 10 Sample

11. | ©2011, Cognizant Decision Tree Analysis 11 Sample

12. | ©2011, Cognizant Neural Networks 12 Sample

13. | ©2011, Cognizant Cognizants 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 Functional Features Automated & manual claims fraud referral from claims system Automated case assignment based on SIU user skills and availability Automated creation of relevant tasks for each case based on claim type Claim fraud scoring with 360 degree claims view Outside investigators assignment and tracking Compliance alerts and reports Regulator referral utility Technical Enablers Cloud ready Light weight case management/ workflow layer Rules engine interface Scoring engine to interpret predictive models and provide claim fraud propensity score Multi format claim investigation evidence update (Images, Audio Files, GIS Data etc) Third party reports interface Discussion forums and chat functionality to discuss with SIU gurus Sixth Sense Solution

14. | ©2011, Cognizant ©2011, Cognizant Thank you