AI and machine learning are transforming data analytics by allowing companies of all sizes to develop predictive models five to fifteen times faster, and at a far lower cost. Insurers can overcome previous limitations imposed by data, systems, and staffing to incorporate more insights and information from across the enterprise into ever more precise predictions.
In this session, Neal Silbert, General Manager of Insurance at DataRobot, will cover some of the key challenges facing the insurance industry today and how AI, specifically automated machine learning, can make a critical impact throughout the business, including:
- Marketing: To identify better conversion opportunities and improve their quality and spread of risk;
- Underwriting: To allow for more granular differentiation among risks, and thus improved risk selection, tiering, and company assignment;
- Pricing: To allow for more precise prediction of loss propensity and more granular pricing models by coverage, market segment, industry or geography;
- Loss control: To distinguish more precisely among causes of loss in regards to their impact on loss ratios, thus allowing a more strategic approach to mitigation efforts; and
- Claims: To implement cost-effective claims “triage,” identifying individual claims for automated settlement, manual handling, or potential fraud.
