TrustedChoice

Problem

Trusted Choice, a leading platform connecting insurance consumers with independent agents, faced challenges in accurately calculating referral fees for insurance claims. Their existing pricing model lacked precision, leading to inefficient financial planning and missed revenue opportunities. This inaccuracy hindered their ability to effectively match agents with clients, affecting overall business performance.

Solution

To address this issue, Trusted Choice partnered with Zams to develop a custom AI-driven Dynamic Claim Price model. Utilizing regression analysis, this model accurately predicted referral fees based on real-time claim data. The implementation process was swift, with the model being built and deployed in just one week, ensuring rapid enhancement of pricing precision.

Outcome

The introduction of the AI-driven model led to a 7% increase in revenue within the first month, significantly boosting Trusted Choice's financial performance. Additionally, conversion accuracy improved by 12%, resulting in better matches between agents and clients. The swift deployment of the model ensured that these benefits were realized promptly, underscoring the effectiveness of the collaboration with Zams.