growth of new business - cross sell
TRANSCRIPT
The Challenge: Claims Fraud Prediction andGrowth of New Business byimproved Cross Sell
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. On the other hand, the client was also looking at decision models that would help them cross sell better by identifying closer prospect – product match.
About The ClientThe client is one the largest and profitable insurers of India. As a joint venture between one of India biggest banks and a European financial conglomerate, it had over 3 million active policies at the end of 2014-15.
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
www.aureusanalytics.com
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
www.aureusanalytics.com
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
Making Cross Sell EffectiveThe other challenge that the client wanted to address was how to make cross selling more accurate and effective. The cross sell model was designed to answer three primary questions:
The model developed would attempt to answer all the three questions together for large and medium ticket size product categories. The approach to make an effective cross sell model was straightforward.
WHOM TOSELL?
WHAT TOSELL?
HOW MUCHTO SELL?
www.aureusanalytics.com
Developing the model was an iterative process and used randomly selected samples for training and testing.
The significant variables identified in the modelling runs were used to further finetune the model. The model accuracy ranged from 90% for high risk products to 98% for low or medium risk products.
DefinedVeriables
DerivedVeriables
Back Testing& Turning
ModelDevelopment
InsightsDevelopment
Figure 1: High level Approach
Input = Data Used
1. Policy2. Customer Information3. Family History
Process - RandomForest Algorithm
Predictive Model
Output
For each customer,the model gives theprobability of the16 CATEGORIES
Web: www.aureusanalytics.com Email: [email protected]
Stay Connected With Us: @AureusAnalytics /company/aureus-analytics
Key Benefits
The precision of pitching the right product to the right person has improved by 5x.
The number of products that are now pitched to a customer have been reduced by nerly 70% thus giving the agents a more focused scope to work on.
The cross sell model has reduced the efforts wasted in miscalling by nearly 60%. This will translate into substantial cost savings.