INSURANCE SOLUTIONS

ApSTAT Technologies provides a complete range of robust data mining solutions to address the complex risk estimation problems specific to the Property and Casualty (P&C) insurance market. Our experts can also develop custom models designed for your specific business needs.

Using state-of-the-art predictive analytics technologies along with stringent performance evaluation criteria, we provide decision support solutions that help our clients realize substantial savings.

Characterize an insured's profileGoal: characterize an insured's profile

What ApSTAT can do:

The customer lifetime value model identifies the customers that represent the best long-term profitability for an insurer.

The elasticity model enables an optimization of the premium structure by modeling the expected customer response to an eventual change in premium.
Whether to underwrite the riskGoal: whether to underwrite the risk

What ApSTAT can do:

The underwriting model makes use of all available information about a customer in order to help make the decision of whether to underwrite the risk, and to detect and reject bad risks up front.

The Risk Sharing Pool optimization model enables the profitable selection of drivers to transfer to the pool.

Read the RSP Case Study »
Establish the correct premiumGoal: establish the correct premium

What ApSTAT can do:

The pure premium model computes the expected loss incurred by underwriting a risk and provides substantially more precise estimates than traditional actuarial models, including generalized linear models (GLMs).

This model also allows to optimize the premium structure in a reinsurance context.

Read the case study pure premium estimation »
Efficiently track down fraudGoal: efficiently track down fraud

What ApSTAT can do:

The fraud detection model helps identify the most common fraud patterns, from the insured's profile, past actions and the actions of other insureds sharing a similar profile.

This model allows a more efficient dispatch of limited investigation resources towards cases that represent the highest likelihood of being genuine frauds.

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