the challenge: claims fraud prediction
TRANSCRIPT
The Challenge: Claims Fraud Prediction
The client has been in business since 2005. With a persistency rate of 69%, the client has an above industry average retention rate. However, over the last few years they have suffered from claims fraud which has impacted their revenues. In the past couple of years, the client has been able to identify fewer than 100 cases of early claims fraud. An early claim is defined as a claim which has been intimated within 3 years of its issuance.
The client receives about 700 early claims a month, and they wanted a predictive model which would help them predict the probability of a claim being a fraudulent claim.
About The ClientThe client is a life insurance carrier with over USD 3.3 Bn in gross written premium and nearly 20% of market share. As one of the oldest insurance carriers around, it has one of the highest customer retention rates at ~81%.
Aureus took two approaches to build a Claims Fraud Model – one using Logistic regression and the other by using decision trees. Both these models would run as soon as there was a claim intimation and categorize the claim in one of three buckets – Red (high probability of fraud), Amber (Medium Probability) or Green (Low Probability).
The approach used to develop both models was similar:
Approach: Identifying Fraudulent Claims
Case Study
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FraudPrediction
BusinessUnderstanding
Deployment DataUnderstanding
Evaluation DataPreparation
Modelling
Five main data sets were used to for the modelling purposes – Customer Data, Policy Data, Agents Data, Products Data and Claims Data. From these sets a few derived variables were also created that would help with the claims fraud prediction. A total of 29 different variables across these buckets were used for modelling.
The base data set was divided into three main parts – training data set, validation set and test data set. Models were built for both the training and validation data sets and only those variables which were significant on both sets were retained.
Both models delivered nearly 90%+ accuracy on the test data set.
Policy29
SignificantVariables
Claims
Product Agents
Customer
Key Benefits
The model will save the client nearly USD 400, 000 in terms of effort spent in investigation of genuine cases.
Overall turn around time will improve as fewer cases will now be referred for further investigation.
The model built by Aureus identified nearly 10-13% probable fraud cases. This is a substantial improvement over manual identification of fraud cases
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