Machine learning is the new buzz in the insurance sector. Explore five applications of ML in insurance, its drivers, and examples.
The competition in today’s US insurance market is tough, with around 6000 businesses operating in this sector, according to the Insurance Information Institute. The value of capital invested in the insurtech market alone made up $7.1 billion in the first half of 2021.
To beat this competition, insurers are exploring various options available to them and that can help them enhance their business operation and customer loyalty. One of these strategies is to introduce machine learning to solve business problems across the insurance value chain.
In this article, read about five machine learning applications in the insurance sector. During the past two years, the insurance sector has grown an immense appetite for data. And now machine learning helps insurers unleash the potential of this data, process and use it right.
Drivers of machine learning and data science in insurance
Machine learning (ML) is more than a hype approach used to bring innovation to the insurer’s workplace. Modern insurers choose ML and data science because of a bunch of reasons:
Increase in data volumes — Today, connected consumer devices, such as smartphones, smart TVs, or fitness trackers are becoming increasingly popular. This explains the growing number of data in the insurance industry. Insurers can use this data received from IoT devices and evaluate their customers’ profiles more accurately.
Strong potential for automation — McKinsey predicts 25% of the insurance industry to be automated by 2025. The industry indeed has lots of areas to be automated, from claims management to policy cancellation. AI and ML technologies are very useful when it comes to automation.
Open-source everywhere — With tons of data accumulated in the industry, open-source protocols are also becoming mainstream to make sure this data is shared and used across. Also, private and public sectors join forces to create reliable ecosystems where data is shared safely and securely.
Better response to Covid-19 — The pandemic has taken a great toll on insurance businesses. Still, those insurers that have incorporated intelligent technologies appeared better prepared for Covid-19. For example, they were able to process claims fast and accurately, although having a large part of their workforce working from home.
Now let’s move to specific applications of machine learning in the insurance industry.
Machine learning use cases in insurance
1. Claims processing
Machine learning brings unique opportunities in claims management. It can help companies get rid of any manual processing and, hence, provide end-users with better and faster service. Apart from this, automated claims processing means improved decision-making and reduced risks.
Here are more specific applications of machine learning in claims management:
Claims registration: Typical claims registration process takes lots of time and is data intensive. ML can provide insurers with analytical insights on how to remove these operation inefficiencies.
Claims triage: ML can also be useful in scoring and triaging risks. If an ML system learns based on past experience, it will be able to prioritize insurance claims faster and more accurately.
Claims volume forecast: A typical stumbling block in an insurance practice is to set premiums before signing any insurance contract. An insurance agent, in this case, has to go through lots of manual work and make predictions about the number of claims occurrences and approximate claims amounts. With an ML system in place, the forecast for individual claims will be less error-prone and probably take less time. As a result, this can decrease the overall claims settlement time and improve customer experience.
Smart audit: Using ML algorithms in claims audit improves the quality of such audits. Technology helps to identify only those claims that are indeed incorrect and need review.
Read also about data extraction in claims processing as another great use of machine learning.
Example in real life
The Fukoku Mutual Life case illustrates the benefits of using AI and ML in claims management. The insurance company handles claims data with the help of AI and deep learning. Technology helps the insurer automatically find and access medical documents related to the case as well as calculate the pay-offs. As a result, the Japanese insurer can now boast of a 30% increase in productivity and cost savings of around $1 million a year.
2. Fraud detection
Fraudulent claims represent one of the most critical challenges in the industry. Coalition Against Insurance Fraud states that insurance fraud costs businesses $80 billion annually. This makes insurers add these costs to premiums and increase pricing from 10 to 20% on average.
Since ML algorithms work great for anomaly detection and classification of large datasets, machine learning is a good fit for fraud detection and prevention. An ML system detects patterns and analyzes consumers’ behaviors, for example, transaction methods. If it notices any abnormal activity, it warns the insurer immediately.
So, here’s why you’d better choose ML for fraud detection:
It identifies potential frauds faster and more accurately
Next to structured data, ML algorithms can analyze non- and semi-structured data, including claims notes. This contrasts ML to traditional predictive models, which limit insurers to using structured data only
ML allows insurance companies to add alternative data sources to the existing ones, which improves fraud detection results. For example, companies may want to involve public data or third-party IoT.
Example in real life
An inspiring example is the success story of the Turkish insurance company, Anadolu Sigorta. Before implementing an ML-based predictive fraud detection system, the company wasted two weeks manually checking claims for fraudulent activity. As the company processed 25,000 to 30,000 claims a month, the costs were high.
After switching to a predictive system, Anadolu Sigorta became able to detect claims in real-time. So, no wonder that it improved its ROI by 210% in one year only. Its total cost savings, thanks to fraud detection and prevention, included $5.7 million.
3. Customer Service
Customer service makes up one more interesting application of machine learning. For instance, you can use ML for automatic customer segmentation to get insights about customers that your marketers cannot discover by themselves. This way, an insurer doesn’t have to manually analyze large datasets to seek patterns — an ML model will do this for you.
Insurance companies have two ways in this case:
Use supervised ML and alter rules and settings based on their operations
Choose unsupervised ML and allow the model to build datasets and find patterns on its own
What’s the best part? With ML doing a large part of segmentation analysis for insurers, businesses get more time for developing marketing campaigns and searching for new business opportunities.
Personalized marketing is another way to reap the full benefits of ML. 74% of consumers say they’re happy to get computer-generated advice from machines. And AI and ML technologies make this possible by extracting insights from large amounts of data and seeing patterns in customers’ behaviors, attitudes, preferences, and personal info.
Use this info to provide individual offers, recommendations, loyalty programs, messages, and pricing to your end-users.
Example in real life
In 2015, a life insurance company, MetLife, decided to take a data-driven approach to customer segmentation. At the time when insurers used ML solely for risk mitigation and underwriting, MetLife centered on ML to foster its go-to-market strategy and achieved great results.
ML algorithms helped the insurance company to understand its customers’ needs, behaviors, and attitudes better and, hence, maximize its competitive advantage. Later, MetLife would summarize this experience as “the most significant change to their brand in over 30 years.”
Don't miss an opportunity to read a case study on how ML can boost cold calling effectiveness and, thus, help businesses improve customer service.
The use of ML-enabled risk management systems allows insurers to speed up and facilitate underwriters’ work. Of course, AI and ML cannot entirely replace manual risk assessment in the insurance sector. Still, new technologies can contribute to operational efficiency and intelligent decision-making in underwriting.
For example, machine learning in insurance could be useful when:
Underwriters should decide on how deeply to investigate the case, e.g. full vs. simplified underwriting
An insurer needs to decide whom to assign the case, e.g. junior vs. senior specialist
A company wants to add alternative data sources to improve its decision-making process, e.g. to use a GIS (geographic information system) data in property insurance to track the property state and adjust pricing
Example in real life
A good example here is a success story of one global reinsurer, i.e. a company that provides financial support to insurance companies. Using historical and geospatial info, this organization has built an ML algorithm to analyze the risk of floods in the area.
This implementation of the ML-based system allowed the reinsurer to:
Reduce time spent on underwriting in ten times
Model what to expect from the market in the future with 80% accuracy
Increase case acceptance by 25%
5. Price optimization
ML algorithms can also be a tremendous help to insurers in building an effective pricing model. A traditional price optimization approach means accommodating GLM (Generalized Linear Model) to historical claims and premiums. GLMs are traditionally used in insurance as the main pricing technique, but this conventional approach
Doesn’t take into consideration the changeability of insurance pricing. Pricing uncertainty in this sector is high because of constant changes in claims procedures, regulatory requirements, and so on
Doesn’t work in certain circumstances. Taking the same GLMs approach, the result — quoted premiums — can differ from one insurer to another. The study conducted by the Institute and Faculty of Actuaries proves that even for an ordinary risk, this difference can reach up to $1000
Using ML for price optimization brings more accuracy and flexibility to pricing. For one thing, insurers can adjust prices dynamically — ML algorithms can discern patterns from data, integrate additional sources and information, and notice trends and new demands at early stages. For another, companies no longer need to orient on industry benchmarks but can make use of predictive models to set an effective price for each premium.
Example in real life
AXA is a global insurer giant that has tried using deep learning techniques to optimize its pricing. The company knew that 7 to 10% of its customers cause a car accident annually. While most of these accidents weren’t serious and cost little to the insurer, 1% of these made up large-loss cases with huge payouts.
As you might expect, AXA wanted to predict those large-loss cases to improve its pricing and cut costs. For this purpose, it turned to machine learning and produced an experimental neural network model. The insurer entered 70 different risk factors into the model and eventually achieved 78% accuracy in its predictions. By fine-tuning the model, the company has a good chance to improve its pricing more.
In the last decade, the insurance sector has produced and accumulated as much data as never before. The bad news is that insurers use not more than 10-15% of this data, according to the Accenture study.
Machine learning can help insurers use the data they have access to to its fullest potential and improve their business in a range of ways, from fraud detection to risk mitigation to claims processing and price optimization.
Want to get started with machine learning in insurance? Or maybe optimize your existing ML system? Contact our talented ML engineering team, and we will gladly help you improve your business operations.