Claim Propensity

Claim Propensity

Claim Propensity

The Challenge

The Auto Insurance Industry has always had challenges in managing volume and severity of insurance claims. Every insurer aims to improve their customer service, reduce claim settlement time while giving ample time for investigation. But a very prevalent trend is to rely on individual experience in handling claims and provide customer service, this is by and large a manual process. Although much has progressed from a claims reporting perspective, the overall claim settlement journey is costly and disintegrated. Another key challenge is to improve loss ratio and reduce claim cost by more successful and efficient investigation.

About the Company

The Solution

With iTuring, you can build extremely accurate claim propensity models to identify policyholders that are likely to claim. This provides an opportunity for early intervention, improved claim outcomes and processing efficiency. iTuring will not just help predict policyholders who are likely to claim but also predict the claim amount along with causes of claim with detailed information. This helps build an appropriate strategy to reduce claim settlement time and improve precision of risk assessment upfront.

For a leading General Insurance Company in North America, iTuring built a claim propensity model for auto insurance, to reduce turnaround time from assessment to settlement. This also helped claim review agents in identifying potentially risky customers, identifying driving factors leading to be a risky claimant, support their treatment strategy and approve appropriate claim amount.

To improve claim settlement time and efficiency, iTuring developed policyholder level risk causation metrics, to understand actual cause of claim. They also developed policyholder level contact strategy, improved claim settlement and assessment efficiency by 32% with a model accuracy of 85%.



Claim Settlement Efficiency

0 %↑

Time to Deployment

0 Weeks

Model Accuracy

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Why CyborgIntell

iTuring provides highly accurate predictions of policyholders that are likely to claim, but also predict claim amount with different degrees of confidence and provide a decision accuracy system for business to make claim approval decision faster and accurate.

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