White Paper

3 Requirements for Fast-Tracking Machine Learning at Insurance Organizations

Predictive and prescriptive models can help insurers make smarter decisions faster, but implementation is stymied by lack of direction, legacy systems, and fear. This plan can help.

White Paper Summary

Machine learning is one of those buzzwords not everyone understands. It involves artificial intelligence (AI) … but what does that really mean in the context of an insurance company built on old IT networks and an even older set of ingrained processes?

“Machine learning is an approach to AI that gives a computer system the ability to obtain their own knowledge by extracting patterns from data instead of relying on hard-coded knowledge. In business, machine learning can help you predict what operational decisions will lead to an optimal outcome such as maximizing profitability based on the decisions you have made in the past. As more decisions are made and more data is collected, the algorithm continues to learn and make better predictions over time,” said Sean Naismith, Head of Analytics Services at Enova Decisions.

There are a multitude of operational decisions that feed the insurance process. What is an applicant’s exposure? What premium is competitive yet mitigates risk? Is a claim legitimate and compensable?


To learn more about Enova Decisions, please visit their website.

Enova Decisions is an analytics and decision management technology company that was formed in 2016 to enable businesses including financial services, healthcare, and telecommunications automate and optimize operational decisions through machine learning in real-time and at scale. For more information, visit www.enovadecisions.com.

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