Insurance Claims Management

What Is Insurance Claims Management?

Insurance claims management is the process of managing a claim from reception to settlement. The insurance claim process works particularly well with machine learning solutions that cut time and costs, leading to speedier resolution of claims to the satisfaction of both insurer and insured.

Why Is It Important to Have a Good Insurance Claims Management Model?

Insurance claims management help insurers acquire customers and provide them with personalized and efficient service. They process claims and detect fraud quickly while underwriting policies even more effectively.

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

A good model has data on the customer (like age, sex, medical condition, medical history, etc.), their policy, and claims they made. All data should be stored in one place so that new policy underwriting like policy modifications will consider previous claims.

What External Data is Essential for a Good Model?

Data from apps and sensors provide important information on the individual client, their lifestyle, and geographic area that a good insurance claims management model incorporates. For instance, wearable health device data can provide insurers with a wealth of data they can use to inform their underwriting and pricing processes.

Insurers can also market to health-tracking device users in the general population.

What External Data May Prove Useful for a Good Model?

Any information that provides data about the claim case can help to improve the model and achieve improved results. Data such as weather conditions and the economic situation may also prove useful in predicting insurance claims.

What Are the Main Challenges of This Use Case?

Using AI can make a situation like that of a “black box” where most of the business personnel do not fully understand why or how the predictive model undertook a certain action. As a consequence, the insurance company may lack the ability to document and defend decisions like the claim denials.

Regulators also increasingly press insurance companies to explain the inner workings of their predictive models. This is especially true where models are used in underwriting and the pricing of premiums in order to ensure there have been no discriminatory practices.

Interesting Case Studies and Blogs to Look Into

McKinsey: Insurance 2030: The Impact of AI on the Future of Insurance
R Bloggers: Machine Learning for Insurance Claims

Tangible Examples of Impact

Most insurers recognize the value of machine learning in driving better decision-making and streamlining business processes. Research for the Accenture Technology Vision 2018 shows that more than 90 percent of insurers are using, plan to use or considering using machine learning or AI in the claims or underwriting process.

Insurance Claim Analysis Using Machine Learning Algorithms