Few industries can benefit more from the ability to make predictions about future outcomes than the insurance sector. Much of the business model of being an insurer involves using models of past behavior and events to help predict what will likely come in the future.

Insurance firms can use this type of knowledge to estimate losses, set rates, determine which policies are appropriate to specific customers, forecast how customers will react to targeted promotions, etc. These insights can have a direct impact on the financial performance of insurers in a highly competitive market.

It’s not surprising, therefore, that insurance companies would be a natural user of automated feature engineering, which leverages domain knowledge to extract patterns or features from raw data to create useful models to predict events. With this capability and the power of machine learning (ML) technology, insurance providers can give themselves a significant advantage in their markets.

Insurance use cases for automated feature engineering

There are a number of possible use case scenarios for automated feature engineering in the insurance industry. Here’s one:

Health insurers base their business on best-guess predictions about their customers' healthcare needs. Data can help them predict, for example, whether customers with diabetes will need hospitalization, when and for how long. This is done by examining features about family health history, prescribed medications, or previous hospital stays and insurance claims.

All of these key types of features are required for a prediction problem to work and should be discoverable through automation in order to help build predictive models that can help the business effectively.

The main dataset insurance firms use is claims data, which is in a transactional format. There are many claims for each customer, and one of the biggest challenges the firm faces is how to summarize that data into one row per customer. This is difficult given the number of fields and the number of levels within each field.

Automated feature engineering works best on structured, time-series data, which allows firms to best leverage the time element of claims data to create valuable features. This makes it ideal for use cases in the insurance sector. It can provide a secure way to build meaningful machine learning features on a time-series problem while delivering superior predictive performance and trust worthy outcomes.

As insurers continually look for ways to gain a competitive edge, automation and the ability to accurately predict future developments with the help of ML and artificial intelligence (AI) will be vital. Technology executives, actuarial teams and line of business units in the sector weighing whether to leverage the latest technologies can be assured that the competition is likely considering the same.

For more about how insurance firms can benefit from automated feature engineering, contact us.