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 is an indispensable partner helping us maintain our reputation both internally and externally by deploying, monitoring, and governing generative AI responsibly and effectively.
The generative AI space is changing quickly, and the flexibility, safety and security of DataRobot helps us stay on the cutting edge with a HIPAA-compliant environment we trust to uphold critical health data protection standards. We’re harnessing innovation for real-world applications, giving us the ability to transform patient care and improve operations and efficiency with confidence
DataRobot provides us with innovative ways to test new ideas. Given a problem and a dataset, DataRobot allows us to generate multiple prototypes 20% faster. And the process facilitates the learning evolution of our data scientists.
The value of having a single platform that pulls all the components together can’t be underestimated. Then there’s the combination of the technology and the collaborative DataRobot team. If either one of those wasn’t there, I would have looked elsewhere.