INCREASING TAKE-UP RATE; AN AUTOFINANCE USE-CASE

In auto finance, one of the challenges faced by organisations is lower finance take-up rate. When a customer approaches a vehicle dealership in view of purchasing a vehicle, a dealership usually submits an application to multiple finance houses. However, a customer can only sign-up with one financier.

Optimized Analytics builds machine-learning algorithms to identify customers that are likely to take-up a vehicle finance product with our client. This then assists our client to proactively prepare a proposition that appeals to their potential customer to nudge them to sign-up with them. This has the potential to increase the take-up rate.

STREAMLINING THE CLAIM PROCESS; A SHORT-TERM INSURANCE USE-CASE

At claim stage, most short-term insurance companies treat all their customers the same. We believe that data should be used to segment customers into different clusters identifying customers whose claims should be scrutinised and the ones whose claims should be streamlined.

This form of segmentation has the potential to lead to increased customer satisfaction and retention, particularly for the best customers in the insurance portfolio.

IDENTIFY HIGH LOSS PARTS CLAIMS; AN AUTOMOTIVE INDUSTRY USE-CASE

When a new vehicle is introduced into a market, there is a likelihood that the vehicle being introduced has components that are potentially likely to result in high claim ratio for the aftersales motor warranty fund pool. Optimized Analytics is positioned to develop predictive models to identify parts/components that will result in increased claim ratio for the fund.