How Machine Learning can Improve Insurance Underwriting

An intelligent machine learning models in the underwriting process can train underwriters to view risk factors confidently and standardize the risk scoring process throughout the company.

Fremont, CA: An underwriter’s work includes research, data entry, pricing risk, and negotiating that premium price with an agent. They need to determine the risk case by case and make an offer to satisfy the customers and monitor the market dynamics.

As underwriters access vast amounts of data, software, and file formats to find a premium for every policy, the work can be very complicated. Implementing machine learning can help improve the process.

Here are three ways machine learning can help in insurance underwriting:

Forecast the Outcome of a Claim

Differentiating between the massive spread of claim severity can help make accurate predictions on how much an incident will cost the insurance firms. Understanding the chance of occurrence and the severity of a claim adds towards forecasting the exposure of a firm.

Generate a Quote

A machine learning model can translate risk factors into a suggested premium according to the model’s historical data. In renewals, the machine learning model can distinguish if there are changes to the critical risk factors, which can interpret into an auto-generated quote similar to the previous year.

This allows insurance firms to standardize the charge’s rate throughout the firm, eliminating bias that may release into a premium calculation. It also will enable underwriters to save time on remedial applications, providing more time to evaluate new or unique prospects.

Examine an Individual’s Claim Risk

When an underwriter adds a new policy to their business book, they are taking on risk. The risk is based on the individual’s history and other factors. A machine learning system can gather a comprehensive view of the insured by collecting company data and third-party sources. The data science model can help categorize and establish quantifiable risk factors and help underwriters manage with sophistication. A classification model can see if an individual has the same characteristics to others in the population with claims filed. A match would help underwriters increase the premium.

In the underwriting process, intelligent machine learning models can train underwriters to view risk factors confidently and standardize the risk scoring process throughout the company.