Artificial Insurance & Risk Assessment | Hitchings Insurance

What is “Artificial Insurance” & How is It Transforming Risk Assessment?

Artificial intelligence is one of those buzzwords that has been steadily gaining traction over recent years. More and more, we’re seeing AI start to impact different industries – including the insurance industry. The insurance industry has been slow to consider adapting AI software and tech to expedite some of its processes. But we’re starting to see artificial intelligence occupying chat boxes, manning phone lines, and even helping to detect fraud, which could change risk assessment!

Now there’s another term – “artificial insurance.” Artificial insurance is the incorporation of artificial intelligence in the insurance industry. More and more, we’re starting to see how “artificial insurance” is contributing to the ways that we gauge insurance premiums – and how we assess risk.

In this article, we’ll examine the impacts that artificial intelligence has on how insurance providers are gauging risk assessment, and more!

What is risk assessment?

When an insurance provider is selling a policy to a client, they’re trading the “potential financial harm” to their customers in exchange for a premium price. To ensure they’re gauging the premium price correctly, they’ll need to estimate how likely the chances are that a claim will arise. This is known as “risk assessment.” Risk assessment is essentially an in-depth study utilizing the insurance provider’s known statistics to determine the risks that the insurance carrier may be exposing itself to.

How does the involvement of AI affect it?

In the past, insurance underwriters would typically use applicant-provider information in order to determine client’s risk. They would then compare this with their in-house statistics to weigh the total risk. Each insurance provider may weigh risks differently, i.e.: one provider might weigh a client’s age heavier than their gender, or vice versa. It solely depends on the statistics they use.

Unfortunately, the issue with this is that clients can easily be dishonest, or they can make mistakes on their application. This renders these risk assessments inaccurate and unreliable.

Enter ML, or “machine learning.” ML allows insurers to assess risk through more abstract forms of information, such as reviews online, social media posts, etc., and pulls all the relevant information together in order to provide a more accurate assessment of the carrier’s potential risks.

How does improved risk assessment benefit both the provider and policyholder?

It might not sound like it, but using artificial insurance to more accurately gauge risk is beneficial for both the insured and the insurer.

For the insurer, it allows providers to attach more appropriate premiums to their clients. A more individualized approach to insurance policies could help to attract and retain more clients. Especially since they’ll only be getting the coverage they need according to their most accurate risk assessment. They’ll also be getting more personalized prices. In an industry where products rarely differ but prices often do, this can go a long way.

For the insured, they’ll be getting exactly what they’re paying for. There are no surprises when it comes to price. The higher premiums can therefore be allotted to the higher-risk clients while saving the lower-risk clients from seeing a rate increase.

The TLDR list of benefits is as follows:

  • Improved customer relationship management
  • Increased efficiency
  • More accurate rate calculations
  • Personalized insurance policies
  • Greater retention
  • Reduced human error

How does AI improve risk assessment?

There are a few ways that AI models can prove effective solutions for gauging risk assessment.

The first is advanced analytics. Advanced analytics use quick computations – faster than manual labor could perform – to gauge typical variables. As an example, for auto insurance, that might be driving location, age, gender, or driving speed. Advanced analytics obtain parameter values for each variable and calculate them quickly.

Optical character recognition (OCR) is a part of natural language processing which helps insurers automate their underwriting processes. This expedites repetitive duties that would otherwise be allotted to underwriters. OCR extracts information from digital documents faster, which insurers can use.

Chatbots. Although this is a lessor known means of AI improving risk assessment, AI can derive information from insureds on virtual chatboxes. This method can be used in partnership with other risk assessment methods to gain a more accurate measurement.

What is the future of AI?

AI continues to improve with every passing day. We can expect to see AI affecting more aspects of the insurance industry. Perhaps even advancing to where it may be capable of handling the backend of more basic claims scenarios to free up time for human employees to handle more complicated, taxing claims. This is a win-win for everyone, as it expedites the claims process and allows insurance companies to take on more clients with their freed-up time.

Moreover, the adoption of AI can improve insurance companies’ ability to stay competitive by advancing their processes with machine learning. They can even excel by prototyping their own algorithms.  Even though there’s an upfront cost, maintaining AI is actually rather inexpensive, and the costs you’ll save will go a long, long way.

Ultimately, there’s no telling exactly how AI will affect the insurance industry, or how it will continue to impact risk assessment processes. All we know is that it won’t be a seamless transition, but as advances continue to happen, we’ve already drastically been impacted in so many ways.