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 overview, we 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.
DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.
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