Insurance companies around the world are adopting AI throughout their businesses because they know that they can either lead the Fourth Industrial Revolution or fall behind the competition. In a marketplace where the average P&C combined ratio is hovering close to 99 points, a single point improvement is exceptional and a dramatic increase in profitability. AI and automated machine learning are transforming P&C insurance operations, enabling teams to break away from competitors and lead the way. How can P&C operations get started?
Below are five examples of how P&C insurance companies can effectively leverage AI and automated machine learning:
- Rapid product development with dynamic pricing
- Individually develop loss predictions for claims, pricing, and reserving
- Distribution optimization
- Automated underwriting and marketing triage
- Underwriting risk portfolio optimization
Below we dive into two of the examples:
Rapid product development and dynamic pricing
Rather than getting bogged down with data and models, automated machine learning can empower insurers by rapidly exploring hundreds of modeling options, developing the most robust and precise models for their purposes. This huge time saver allows insurers to explore alternative pricing strategies, identify unique market segments, and automate product and service features.
Individually develop loss predictions for claims, pricing, and reserving
AI and automated machine learning can refresh and renew reliance on “top-down” reserving. Though this approach is reasonable for assessing a company’s overall exposure and aggregate financial position, it is not precise and does not identify the appropriate level of reserves for individual claims. With AI and automated machine learning, you can have it all. Automated machine learning brings in “bottom-up” loss reserving by projecting loss development for each claim individually, then establishing company estimates based on an aggregation of these individual claims. By automating rapid product development, P&C operations can reduce time to market by as much as 80%. This “bottom-up” approach identifies the claims most in need of priority claims handling and can also identify clusters of similar claims for the appropriate product and pricing action.
Want to learn more? Access the detailed, full list in our eBook Five Automated Machine Learning Solutions for P&C Insurance. Today’s P&C insurance organizations can start implementing these solutions to overcome everyday obstacles with impressive results.
About the Author:
Neal Silbert is DataRobot's General Manager of Insurance. As an insurance industry executive and management consultant, he has served as an analytics thought leader and driver of innovation for the last 25 years. Recently, he was the VP of Predictive Analytics at Zurich North America, focusing on bringing the latest advances in predictive analytics to insurance product development. Neal works closely with our data scientists and customers to define state-of-the-art AI solutions that drive maximum impact across the insurance enterprise.