According to the Insurance Information Institute, about 6000 insurance companies in the US. This means that there is a lot of competition in this market. In the first half of 2021, just the money invested in the insurance market was worth $7.1 billion.
To beat this competition, insurers are looking into all their options, which can help them improve their business and keep customers coming back. One of these strategies is to use machine learning to solve business problems across the insurance value chain.
This article talks about five ways machine learning is used in the insurance business. The insurance industry has become a vast data eater in the last two years. Machine learning helps insurers get the most out of this data, process it correctly, and use it to its fullest potential.
Predictive Analytics in Insurance – Machine Learning and Data Science Trends
Machine learning (ML) is more than just a buzzword. It is a way for insurers to bring new ideas into the workplace. Modern insurance companies choose machine learning and data science for many reasons:
Insurance Predictive Analytics – Data Volume Increase
More and more people use connected consumer devices like smartphones, smart TVs, and fitness trackers. This is why the insurance industry is getting more and more data. With this information from IoT devices, insurers can better understand who their customers are.
Potential to Automate
McKinsey thinks that by 2025, 25 percent of the insurance industry will be done by machines. There are, in fact, a lot of things in the industry that could be done by computers, from handling claims to canceling policies. AI and ML technologies are also beneficial when it comes to automating things.
Everywhere Open-source
Since the industry collects a lot of data, open-source protocols are becoming more popular to ensure this data is shared and used everywhere. Also, the private and public sectors work together to make safe and secure ecosystems where data can be shared.
Covid-19 Response
Insurance companies have lost a lot because of the pandemic. Still, the insurance companies that had used smart technologies seemed better ready for Covid-19. For example, even though many of their employees worked from home, they were able to process claims quickly and correctly.
Now let’s talk about how machine learning is used in the insurance business.
Machine Learning and AI in the Insurance Industry
1. Claim Filing
Machine learning brings unique opportunities to claims management. It can help companies get rid of any manual work to give better and faster service to their customers. Besides this, automated claims processing means better decisions, and risks will be lower.
Here are some other ways that machine learning is used in claims management:
Claims Registration
Usually, registering a claim takes a long time and a lot of information. ML can help insurers figure out how to fix these problems by giving them analytical insights.
Claims Triage
ML can also be used to score risks and decide how to handle them. If a machine learning (ML) system can learn from the past, it will be able to prioritize insurance claims more quickly and accurately.
Claims Volume Forecast
A common mistake in the insurance business is to set the premiums before signing the contract. In this case, an insurance agent has to do a lot of manual work and estimate how many claims will happen and how much they will cost. With an ML system in place, it will be easier to make accurate predictions for each claim and probably less time. As a result, this can cut down on the time it takes to settle claims and make things better for customers.
Smart Audit
The quality of claims audits goes up when ML algorithms are used. Technology helps find only the wrong claims and needs to be looked into.
Example
The Fukoku Mutual Life case shows how using AI and ML to handle claims can be helpful. The insurance company uses AI and deep learning to manage data about claims. Technology makes it easier for the insurance company to find medical records related to the case and figure out how much to payout. So, the Japanese insurance company can now say that its productivity has gone up by 30% and saved around $1 million a year in costs.
2. Fraud Detection and Prevention
One of the most critical problems in the industry is fraudulent claims. Coalition Against Insurance Fraud says that businesses lose $80 billion every year because of insurance fraud. This means that insurers have to add these costs to premiums, which raises prices by an average of 10 to 20%.
Since ML algorithms are great at finding outliers and classifying large datasets, they are a good fit for detecting and stopping fraud. An ML system looks for patterns and analyzes how people act, like how they buy things. If it sees anything out of the ordinary, it tells the insurer right away.
So, here’s why you should use ML to find fraud:
- It detects potential frauds more quickly and accurately.
- In addition to structured data, ML algorithms can analyze unstructured and semi-structured data, such as claim notes. This is in contrast to traditional predictive models, which only allow insurers to use structured data.
- ML enables insurance companies to supplement existing data sources with new ones, improving fraud detection results. Companies, for example, may wish to incorporate public data or third-party IoT.
Example
The success of the Turkish insurance company Anadolu Sigorta is an excellent example of what can be done. Before implementing a predictive fraud detection system based on machine learning, the company wasted two weeks manually checking fraud claims. The costs were high because the company handled between 25,000 and 30,000 shares every month.
When Anadolu Sigorta switched to a predictive system, it could find claims in real-time. So it’s no wonder that its ROI went up by 200% in just one year. It saved $5.7 million on costs because it caught and stopped fraud.
3. Customer Service
One more exciting way that machine learning is used is in customer service. For example, you can use ML to automatically divide customers into groups to learn things about them that your marketers can’t find out on their own. So, an insurer doesn’t have to look for patterns in big datasets by hand; an ML model will do it for them.
In this case, insurance companies have two options:
- Use supervised machine learning and modified rules and settings based on their operations.
- Select unsupervised ML and let the model build datasets and discover patterns on its own.
What do you like best? With ML doing a lot of the segmentation analysis for insurers, businesses have more time to work on marketing campaigns and look for new business opportunities.
One more way to get the most out of ML is personalized marketing. 74% of consumers say they are happy to get advice from machines made by computers. AI and ML technologies make this possible by gaining insights from large amounts of data and seeing patterns in customers’ behaviors, attitudes, preferences, and personal information.
You can use this information to give your end-users personalized offers, suggestions, loyalty programs, messages, and prices.
Example
MetLife, a life insurance company, decided in 2015 to use data to determine how to divide its customers into groups. MetLife used ML to improve its go-to-market strategy and did very well. At the time, insurers only used ML for risk management and underwriting.
ML algorithms helped the insurance company better understand the needs, behaviors, and attitudes of its customers and, as a result, increased its competitive edge. MetLife would later say this was “the biggest change to their brand in over 30 years.”
4. Underwriting
ML-enabled risk management systems allow insurers to speed up and make it easier for underwriters to do their jobs. AI and ML can’t wholly take the place of human risk assessment in the insurance industry, of course. Still, new technologies can help make underwriting more efficient and help make smarter decisions.
For instance, machine learning could be helpful in insurance when:
- Underwriters must decide how thoroughly to investigate the case, for example, full vs. simplified underwriting.
- An insurer must decide who will handle the case, such as a junior vs. senior specialist.
- A company wants to add new data sources to its decision-making process, such as using GIS (geographic information system) data in property insurance to track the state of the property and adjust pricing.
Example
A good example is the story of one global reinsurer, which is a company that helps insurance companies with money. Using historical and geospatial data, this group has made an ML algorithm to determine how likely floods will happen in the area.
This use of the ML-based system made it possible for the reinsurer to:
- Tenfold the time spent on underwriting.
- With 80% accuracy, predict what the market will do in the future.
- Case acceptance should be increased by 25%.
5. Price Optimization
ML algorithms can also help a lot when building a good pricing model for an insurance company. A traditional way to optimize prices is to fit the GLM (Generalized Linear Model) to claims and premiums from the past. GLMs have been used in insurance pricing for a long time, but this traditional method isn’t the best.
- Does not account for the volatility of insurance pricing. Because of constant changes in claims procedures, regulatory requirements, and so on, pricing uncertainty is high in this sector.
- In some cases, it does not work. Using the same GLMs approach, the outcome — quoted premiums — can vary from insurer to insurer. According to a study conducted by the Institute and Faculty of Actuaries, even for an ordinary risk, this difference can amount to $1000.
When ML is used to optimize prices, prices are more accurate and flexible. For one thing, insurers can change prices because machine learning algorithms can find patterns in data, combine new sources and information, and spot trends and unique needs early on. For another, companies no longer have to use industry benchmarks to figure out how much each premium should cost. Instead, they can use predictive models to determine the best price for each premium.
Example
AXA is a vast global insurance company that has tried to improve its prices using deep learning techniques. The business knew that 7 to 10% of its customers cause a car accident every year. Most of these accidents were minor and didn’t cost the insurance company much, but 1% of them were big losses with big payouts.
As you might expect, AXA wanted to figure out how to predict these big losses so it could change its prices and save money. It used machine learning and made an experimental neural network model to do this. The insurance company put 70 different risk factors into the model, and in the end, it was able to make predictions that were 78 percent accurate. By tweaking the model, the company may be able to raise its prices even more.
Conclusion
In the last ten years, insurance companies have made and collected more data than ever before. The bad news is that, according to the Accenture study, insurers only use 10–15 percent of this information.
Machine learning can help insurers get the most out of their data and improve their business in many ways, such as finding fraud, reducing risks, processing claims, and setting prices.