With the help of IKASI’s sophisticated algorithms, targeted groups saw an increase of 150% in the expected renewal rate.
Integrated Causal AI for Financial Services
IKASI drastically improves approval rates, detects variable pricing to improve loan uptake, and identifies which lost customers can be reactivated and which to upsell.
IKASI AI runs simultaneous experiments on varying interest rates, fees, and credit limits for high risk customers. It detects opportunities to increase approval rates with marginal user profitability and reduce loss rates through credit limit reduction based on customer credit risk.
IKASI AI continuously improves the predictability of individual responsiveness to lower interest rates and rewards for low risk customers.
IKASI improves the effectiveness of rewards, promotions, and variable pricing on loan uptake and increasing transaction volumes for each individual.
Boosting Approval Rates and User Profitability
A national financial services firm was suffering from declining approval rates.
Insurance Company Case Study
A large insurance company was looking to tactically apply promotions to improve customer retention across multiple insurance products.
IKASI causal AI ran concurrent experiments on rejected applications to determine which credit terms should be amended. IKASI was able to differentiate applicant risk and provide risk-based credit terms, offering varying interest rates, fees, and credit limits based on each individual.
A granular analysis and recommendations of approval and loss rates proved to be highly profitable and successful.
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