The databases, pricing applications, enrichments and connectivity that underpin today’s modern insurers are not just tools of the industry, they are a fundamental necessity. To gain a competitive edge, businesses need to ensure they have the best tools at their disposal and the successful companies that have already embraced machine learning into their tech-stack are beginning to see real wins.
With machine learning for insurtech still in its infancy, it goes without saying that once the business driver has been identified, a carefully considered decision will need to be made on what tech to deploy. It can certainly seem like a jungle with all the neural networks, decision trees and random forests to navigate. So how to decide?
One should be comfortable with the quality and integrity of their chosen data science team. The true substance of an AI lies in the quality of its creator; the data scientist who knows not only the latest software and how to write efficient code, but who also understands the subject matter and the meaning of the data. The second key component in the decision is the data itself:
• What data can be used to train on as well as run with?
• Will the end result have been trained on public data, your own data, or a combination?
• What external public and private enrichment will be available?
• How will the model be kept up to date to avoid drift?
• Does it all adhere to the most stringent regulations?
With all this considered, will the end result deliver actionable insight to improve the bottom line of your business? If it does, whether the end result looks visually absorbing or not, you are on the right path.
Machine learning tools are fantastic at performing given tasks. Trained and delivered well, a single AI tool can be used to enhance risk selection and turn a profit. Combining multiple AI tools to predict price, assess risk and detect fraud, while continually evaluating performance and retraining…well, that’s just the fundamental necessity of tomorrow’s insurer.