Predictive Analytics in Insurance: New Trend or the Industry’s Future?

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Artificial intelligence has been the word on every insurance industry executive’s lips for a while now. And its potential is truly incredible — smart decision-making and predictive analytics, more accurate insights, and automated processes, to name a few of the benefits. But in reality, can artificial intelligence in insurance live up to its reputation, and is AI/ML decision-making in insurance really useful or just another trend? Here we’ll take a look at the real benefits of the use of AI in insurance and what ways it can be applied to get the most benefit for your business.

How Does AI in Insurance Make an Impact?

Back in 2016, IBM conducted a survey relating to business values and AI technology. In it, 56% of C-level executives stated that reducing underwriting risk was a priority, with others saying that operational efficiency came first.
Over 40% of those surveyed noted that cognitive computing, such as AI in insurance, would play a major role in reducing risk. By 2020, that went up to 80%, a marketed increase indicating growing confidence and success of AI tech.
As a whole, the insurance industry is valued at over $5.5 trillion today and is expected to grow to over $6.3 trillion in the next 4 years, making it more important than ever that insurance providers have the tools they need to complete their work effectively and reduce risk.
Application of artificial intelligence in insurance is one way to do both. Depending on the precise tools used it can target two sides of business effectively. Let’s take a closer look at decision-making and predictive analytics in insurance.

Executive decision making

When it comes to the big decisions behind your business, you want to make sure that you have the tools at hand that can inform your decision-making accurately and quickly. The insurance industry is fast-moving and that means being able to adapt to changes quickly, such as new risks, emerging markets, and more.
AI in insurance industry can help you decide significant business decisions based on the very latest data. AI’s potential to elicit insight from a sea of data quickly, means you have the tools you need at your fingertips to decide all major company decisions from where to open a new office, which services to invest in, new insurance products to onboard, and more.

Client-facing decision-making

At the same time, AI/ML decision-making in insurance tools can also be used in other areas of your business too. And that goes beyond just executive decision-making. By integrating smart AI tools into your day-to-day processes, you can not only improve business efficiency by automating routine processes but also reduce risk too.
AI and machine learning in insurance can be utilized for a variety of client-facing functions. For example, AI-powered software can engage to optimize form filling and onboarding processes or build data models that empower companies to make lending decisions faster, and so much more.

How Companies Can Effectively Use AI in Insurance industry

Now that we know a little about the business areas in that AI software in insurance can be used, let’s break it down and explore the details a little more of the areas where the application of artificial intelligence in insurance is set to be onboarded in the future.

Connected devices

Big Data is big news and with smart technology, we are more connected than ever before. But what does that have to do with the insurance industry? Just like fitness bracelets track movement for exercise, technology can be fitted to vehicles, for example, that tracks the quality of a person’s driving. This may inform future quotes for the person with careful drivers receiving lower premiums than those proven to be less safe on the road. At the same time, similar practices could be used for health insurance and more.

Data ecosystems

Continuing with the theme of data, let’s talk about how data ecosystems will inform future decisions for a business. Just as connected devices can be used to personalize quotes and offerings, wider data ecosystems can inform decision-making. Big Data can be utilized to inform predictive analytics in insurance, such as Scoring Machine, which created decision-making models that can be used throughout the organization. These aid businesses in tailoring their offerings to their client base and ensuring it meets the needs of the client without creating undue risk.

Cognitive technologies

Neural networks or cognitive technologies can be employed by insurance companies to process the large amounts of data they are faced with. Not only does this allow a business to tailor its offerings based on a client’s behavior, but such tech can also be used to optimize processes and automate routine procedures.

What Are the Challenges to Decision-Making with AI in Insurance?

So, will it be smooth sailing into an artificial intelligence future? Unfortunately, this may not always be the case. Developing a digital transformation plan for your company will need to include strategic planning on how to make AI decision-making in insurance more effective and realistic. Some of these challenges will include:
● 100% automated decision-making isn’t realisticNor should it be. While smart predictive analytics in insurance can inform decisions, automating 100% shouldn’t be encouraged. AI technologies can effectively analyze data, but they often cannot explore the more human “why” of how circumstances occurred, and this could be a decision-changer. For example, if we take a career gap, this could be caused by maternity leave to take care of a child, illness, or redundancy, which shouldn’t affect an individual’s ability to get insurance but may be viewed in a negative light.
● Data is by nature historicEven with the fastest AI data analysis systems, the data it is analyzing is always in the past. This makes it intensely challenging to predict the future accurately. While predictive analytics can give a general indicator of future events or behavior, this isn’t a completely full-proof solution and the data’s historical nature should be remembered.
● Bias decision-makingAI is only as good as the people who programmed it. To date, there have been numerous examples of AI solutions gone wrong. However, that doesn’t mean it’s all bad. It’s essential to carefully examine which decisions you want your AI to be able to make and sure Quality Assurance to ensure it is doing so correctly. Unintentionally biased software could lead to poor decision-making if action isn’t taken on time.

Why Should you Consider the Use of AI in Insurance?

In spite of the challenges that face companies that use AI software, there are insurmountable benefits that counter each and every one of these. By onboarding the right AI tools, businesses can effectively boost their capabilities, automate processes, ensure smart decision-making, and reduce risk while doing so. Artificial intelligence isn’t a fad. In fact, it is a growing industry with huge potential and early adopters could potentially gain a market advantage by onboarding the right tech early on.
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Support

Email: moc.liamg%40enihcam.gnirocs.ofni

Legal

Коротков Володимир Миколайович, 3438908750

All rights Reserved