Using AI to Personalize Insurance Pricing Based on Customer Intent and Risk Assessment

Overview

The insurance industry is transforming rapidly with the advent of cutting-edge technologies such as Artificial Intelligence (AI) and Machine Learning (ML). Traditionally, insurers have relied heavily on historical data and cost models to determine premium prices. However, with increased competition and the availability of real-time data, there’s an urgent need for more adaptive and personalized pricing strategies. Dynamic pricing has emerged as a game-changer in markets outside the United States, providing an opportunity for insurers to fine-tune their pricing models and enhance customer acquisition and retention.

Problem Statement

A significant challenge for insurers is the high dropout rate of prospective clients who request a quote but do not complete their purchase. Traditional pricing strategies, rooted in static models based on historical claim data, may fail to capture the dynamic nature of the current market and the varying intent of buyers. This can result in either overpriced premiums, leading customers to seek competitors, or underpriced premiums, leading to potential losses for the insurer. The inability to personalize prices based on the customer’s intent to buy and risk assessment is a critical gap that needs addressing.

Solution Overview

By leveraging AI and ML models to predict a customer’s propensity to convert, insurers can dynamically adjust pricing in a more personalized manner. This involves integrating a dual approach—combining claims risk modeling with behavioral intent analysis. Technical cost models can assess the inherent risk and expected claims, while AI-driven tools can evaluate the customer’s buying intent based on various parameters such as their history, demographics, and real-time interaction with the website. Implementing this solution involves building and training ML models that utilize a comprehensive dataset capturing both claims risk and customer intent metrics. These models can then inform real-time pricing adjustments during a customer’s journey on the insurance website, providing tailored quotes that improve the likelihood of conversion. Business-wise, this approach not only aims to enhance the customer experience by offering more competitive and fair pricing but also seeks to reduce the dropout rates significantly. The Return on Investment (ROI) can be effectively measured by calculating the potential savings from reduced session dropouts.