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Predictive Analytics for Smaller Business Insurance – A Deep Fryer Example

Large financial institutions have to deal with massive volumes of information when it comes to loan applications or requests for insurance policies.

It takes time for humans to go through all this, especially in cases where industry knowledge and judgment is required, such as small business loans or business insurance policies.

With personal insurance policies, such as auto or life insurance, there are well-developed actuarial tables and risk formulas, and credit ratings for personal loans.

When it comes to small business customers, the situation is vastly different. There’s still tremendous opportunities with small business for insurance companies.

Take, for example, a simple example of insuring two restaurants. One has a single fryer, and the other has four. Intuitively, it might make sense for the restaurant with four fryers to be riskier as there is more opportunity for an employee to be burned.

But in fact, the single-fryer restaurant is riskier, because it’s more likely to be a mom-and-pop operation without the kind of risk controls you have with a larger operation.

Understanding subtleties like this, can help you quantify risk in who we should generally assign lower premiums to and who we should assign higher premiums to, so you can be more competitive in the marketplace. This is a critical piece in the financial services space — how do I get the right price and do it as quickly as possible.

A human being might know, based on experience, which restaurant is riskier. But today’s firms need to make the decision almost instantaneously. Humans can’t keep up — but predictive analytics and AI can.

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