Fraud Control - IT
Interventions and Solutions
©2011, Cognizant
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
Core
Principles
•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
•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
2
| ©2011, Cognizant
Solution Proposed
Components of the proposed solution
Domain
Knowledge
Functional
Solution
3
| ©2011, Cognizant
Technical
Solution
Solution
Proposed
Solution Proposed – Holistic View
Additional
requirements
Functional Solution
MIS & Fraud Detection Reports
Aggregate Level Fraud Modeling
Predictive Modeling
Technical Solution
Rules
Social network
analytics
Real-time Fraud Detection at various stages
Detection at
Underwriting
Detection at
Preauthorization
Integrated
Data
Operational Data Store
(ODS)
Data
Integration
Extract, Transform &
Load (ETL)
Detection at Claims
Process Stage
Data Cubes
Data Quality –
Cleansing, Profiling
Data Marts
Data Standardization
& Certification
Transactional Data
Policy
4
Anomaly
Detection
| ©2011, Cognizant
Member
Claims
Lookup Data
Provider
Registration
Portal
Standardized IDs
for providers &
employers
ICD 10 Coding
Procedure codes
Functional Solution: Aggregate level Fraud Modeling & Analysis using data
Aggregate level Fraud Modeling - Components
Predictive Modeling
Social Network Analysis
Modeling of portfolio with various
methods, most suitable to be selected
based on results:
Analysis of possible collusion between industry
players, ie:



Logistic regression
Decision trees
Neural networks



Possible collusion between TPAs and providers
Collusion between a TPA and employer
Mis-selling / concealing by intermediater, agent,
underwriting office
Anomaly detection rules
Outlier detection rules
Evaluation from a clinical or business logic
standpoint to detect anomaly:
Identify outliers, compared to industry
experience, for scrutiny:



ICD - PCS mismatch
Age-gender appropriateness
Capacity / infrastructure appropriateness


ICD to total charges & charge break-up
Past payment for the same ICD to the same
provider
 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.
5
| ©2011, Cognizant
Proposed Technical Solution
6
| ©2011, Cognizant
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.
Core
Principles
•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.
•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.
7
| ©2011, Cognizant
Technical Solution Overview
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
SAS FFI (SAS
Enterprise DI)
Oracle
Enterprise
Ed
SAS FFI (Base SAS,
Enterprise Miner, OLAP
Cube Studio)
Fraud Suspect Extracts / Investigation feedback
8
| ©2011, Cognizant
Oracle +
SAS Cubes
SAS FFI (SAS
Enterprise
BI)
Model Development & Modeling Techniques
DISC Analytics Methodology closely weaves business
outcome with the statistical techniques
Define
Simulate
Investigate
Modeling Techniques proposed
Consult
X (Contd.)
Data Extraction from
different sources
Fine
tune the
model
Claims Data Merging
Predictive Modeling
No
Data Cleaning
Is Model Adequate
Outliers
Detection
Yes
Exploratory Data
Analysis
Score the Validation
Data
Identify the
Variables for
the Model
Data Split
No
No
Is Satisfactory
Is Adequate
Yes
X.
9
| ©2011, Cognizant
Examine the
predictive ability
Claims
Segmentation
Yes
Results and
Insights
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 topdown
• 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
Exploratory Data Analysis
10
| ©2011, Cognizant
Decision Tree Analysis
11
| ©2011, Cognizant
Neural Networks
12
| ©2011, Cognizant
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
Sixth
Sense
Solution
Fraud Management Workbench
Functional Features
Technical Enablers
• 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
• 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
13
| ©2011, Cognizant
Thank you
©2011, Cognizant
| ©2011,
©2011,Cognizant
Cognizant