Early Claim Prediction
Early Claim Prediction
The Challenge
About the Company
- One of India’s largest non-bank life insurance companies
- Over 10 million policyholders
- 700+ branches
- 60,000+ insurance advisors
- USD 10+ Billion Total Sum Assured
The Solution
The conventional approach to risk assessment is built on rule-based policies driven by business experience. These policies are inflexible and once the rules are written they tend to be applied equally to every case. CyborgIntell improves risk assessment and helps automate decision making. iTuring uses historic data to understand which customers are likely to make an early insurance claim and provides accurate predictions at an individual customer level in real time. Not only does iTuring accurately predict which customers are most likely to claim early, it provides detailed explanations on risk factors to enable insurers decide product programs and make risk-based price decisions.
For the Life Insurance Company, CyborgIntell used iTuring to build a “Early Claim Prediction Model” to predict & manage early claims and reduce their overall loss ratio. iTuring built a new model in only 70 minutes and improved the predictive accuracy over the company’s existing model by 15%. In addition, the false positive rate was reduced by ~7%, thereby reducing administrative cost and improving customer experience.
Highlights
- At the start of the project - 80% predictive accuracy (AUC) for the existing early claim (pre-issuance risk verification) model
- 2 weeks from project start to production-ready application
- 400+ features created
- AI model built in 70 minutes with iTuring AutoML+
- 92% model accuracy (AUC) vs. 80% of previously built model
- Reduced false positive rate by ~7%
Impact
Predictive Accuracy
Time to Deployment
False Positives
Why CyborgIntell
Not only does iTuring provide highly accurate predictions of which customers are likely to claim early, it also provides explanations for why each customer is likely to claim. This allows business users to better understand their client’s behaviour and provide personalized, risk-based pricing recommendations.
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