Three scenarios, using machine learning to simplify insurance business problems

In actual claim cases, insurance institutions often need to use large amounts of data involving individuals, scenarios and other influencing factors. If combined with the restrictions of specific insurance clauses, the entire evaluation system will become more complicated. In addition, the insurance business is also divided into a large number of insurance types-life insurance and auto insurance are obviously not the same thing-each corresponds to different data and processes. Faced with such complex business elements, machine learning (ML) is expected to become a savior to promote efficient insurance practices.

The essence of insurance is a means to resist risks. The insurance industry needs to determine the rate based on expected expenditures to obtain a more normal positive return. However, this kind of understanding and setting of rates and expenditures, especially the method of maintaining profitability, is often extremely complicated. The entire insurance industry hopes that machine learning technology can “reach out” in time. What needs to be emphasized here is that machine learning, not artificial intelligence (AI), is highly expected. This is because it is generally believed that statistical tools based on machine learning often surpass neural networks and expert systems in accomplishing certain tasks. Or the effect of other pure AI solutions.

Below, let‘s take a look at the three basic problems that machine learning is expected to help the insurance industry solve.

>>> Insurance underwriting

Health and life insurance itself is very complicated, and its specific design needs to cover a variety of factors such as personal health, illness, and death risk. In the past, insurance underwriters have been using a set of judging factors, such as male/female, age, and whether or not they smoke. In addition, insurance business is also similar to financial business, and it often combines postal codes and other indicators to appear "defining phenomenon"-that is, no matter how high a customer is willing to pay premiums, they will not get insurance services.

The need to resolve these legal issues means that underwriting services not only involve personal health risks, but also legal risks. The insurer needs to conduct an analysis to exclude specific terms that may cause legal risks, and to maintain a stable profitable fund pool based on this.

This is also the ideal stage for machine learning to come into play. Modern computing systems provide sufficient performance to process massive amounts of data, and complex regression analysis can perform clustering to further support the analysis system. More importantly, many existing machine learning methods can provide value without AI technology.

Paul Ford, CEO and co-founder of Traffk, said: “In the insurance underwriting business, statistical models and program codes are improving the analysis capabilities of companies. We are currently using neural network models, but we still need to train/run time and necessary Strike a balance between accuracy to ensure that this type of engine has the value of actual promotion. Although the follow-up situation may change, but at present, our model does provide customers with analysis and profit improvement."

>>> Car claims

The other end of the insurance process is naturally the issue of claims. The complexity of claims not only troubles the insured, but also brings great troubles to the insurer. Taking the automotive industry as an example, insurance companies need to understand the different maintenance options and the needs of available parts, and considering the huge system of car manufacturers and models, I believe everyone can appreciate the difficulty of claim settlement.

Take car claims as an example. Estimating based on regular maintenance costs is obviously far from enough. Different car models have different calculation methods; even in the same type of car model, the maintenance cost will vary according to the scope of coverage and the supply of parts in the region.

In this regard, machine learning can provide support for claims settlement in a variety of ways. In addition, insurance companies can also use a variety of machine learning tools in the claims process.

For the first notice of loss (FNOL), the insurer needs to notify the insured of the result of the accident or damage assessment as soon as possible. If the overall loss can be quickly assessed, the entire process will become simpler and more efficient. In terms of loss assessment, machine learning technology does not seem to have a direct effect, but robotic process automation (RPA) is often used to simplify the entire claims process.

If the vehicle has other damages, or even some deep losses that cannot be quickly determined, machine learning can be used. The most typical tool is, of course, the AI ​​vision solution, such as guiding the customer to take photos of the vehicle through a manual application, so that the AI ​​system can analyze the damage, and then the back-end AI system maps the replacement parts and makes an estimate. Compared with the insured, the repair shop is undoubtedly more familiar with the loss determination process, and can also answer more specific questions raised by the insurer, quickly helping the insurance company to get an accurate amount of compensation.