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What Is Insurance Pricing?

Historically, insurers determine premiums by running linear regression on a small number of risk factors, largely reported by policy-holders. However, a good prediction model of individual insurance costs is becoming a business essential as competition in the industry and low customer switching costs become key drivers toward a pricing structure that covers insurers’ incurred costs.

Details

Why Is It Important to Have a Good Insurance Pricing Model?

Predictive algorithms allow insurers the opportunity to dramatically adjust premiums. A good model allows insurance companies to optimize pricing policies while predicting which clients are at higher risk to cause “large-loss cases” where they bring major lawsuits against the company during the insurance period.

More than the maximization of profit and income, price optimization also helps to increase the customer satisfaction and long-term loyalty.

What Internal Data Should I Have for a Good Insurance Pricing Model?

A good pricing model includes the amount clients pay based on age, coverage type, client needs. It should also account for personal information like claims history, driving record, credit, gender, marital status, health, hobbies, job, and more.

What External Data is Essential for a Good Model?

Essentially, any data that helps to predict risk and price more accurately must be included in a good pricing model. For example, property insurance needs continual monitoring of variables like neighborhood claims, construction costs, weather, and natural disasters.

What External Data May Prove Useful for a Good Model?

Additional useful data include information on customer types. In essence, machine learning algorithms adjust insurance products to customers based on personal preferences, behavior, and response to prices.

What Are the Main Challenges of This Use Case?

Verifying that the information clients provide to the insurance company is accurate and up-to-date is a major challenge. The biggest challenge, however, is climate change—the increasing frequency of natural disasters lead to premium increases and policy restrictions.

Interesting Case Studies and Blogs to Look Into

Quantee: Insurance Pricing with Interpretable Machine Learning

Tangible Examples of Impact

AXA, the large global insurance company, has used machine learning in a POC to optimize pricing by predicting “large-loss” traffic accidents with 78% accuracy.
Google Cloud Platform: Using machine learning for insurance pricing optimization

Relevant datasets

IBM MarketScan Research Databases

by ibm-the-weather-company

IBM MarketScan Research Databases provides one of the oldest continually-updated collection of health claims data in the USA. Organizations use this data to prove their value to healthcare professionals, insurers, and private individuals.

The data includes drug claims, dental claims, lab results, hospital discharges, and EMR data for millions of people in the country. It also contains workplace productivity data, telling institutions how many workplaces absences they suffer and how many of their healthcare workers suffer disability due to their work. 

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