The competition in today’s US insurance market is tough, with around 6000 businesses operating in this sector, according to the Insurance Information Institute. To beat this competition, insurers are exploring various options available to them, among which implementing machine learning (ML) technology across the insurance value chain.
In this article, read about five machine learning applications in insurance. Since the insurance sector has grown an immense appetite for data during the past two years, explore how ML can help your company unleash the potential of this data, process, and use it right.
Drivers of machine learning and data science in insurance
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 in the insurance industry 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.
Challenges of machine learning in insurance
Speaking about the drivers of ML in insurance, we should mention its challenges too:
- Lack of data: ML solutions are data-driven, and the performance of the future ML model largely depends on the data used to feed the algorithms. However, if the insurer lacks ML expertise, they might not know what data to collect. Paired with a skilled ML team, you can get advice on the best data collection methods as well as create synthetic data if needed.
- Expertise and technical competency: Of course, implementing ML in insurance might be difficult if you have never performed it before. But again, by choosing the vendor with a tank of ML knowledge, you’ll be able to make sense of your messy historical data, build proper data infrastructure, select the right ML algorithms, and train the model efficiently.
- Digital mindset and culture: Adopting ML isn’t enough if your personnel refuse to use it in their daily activities. So, make sure you build a data-driven corporate culture, with clear cooperation between IT and other departments. A good starting point is to organize training sessions to teach the personnel how to use ML to the fullest value.
Now finally we’re ready to move to specific applications of machine learning in the insurance industry.
Machine learning use cases in insurance industry
Here are some of the machine learning use cases in insurance:
1. Claims processing
The first application of machine learning in the insurance industry is 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 machine learning in insurance industry 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
Another use case of machine learning in insurance is fraud detection. 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 in insurance 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 of using ML in insurance 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 machine learning application in insurance. For instance, you can use machine learning in insurance industry 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 in the insurance industry, 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.
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 applications in insurance can 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
And the last option for applying machine learning to insurance is 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
The applications of AI and 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 the use of 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.
Applications of AI and machine learning in the insurance business
Let’s review a few other ways of how machine learning and AI can be useful to insurance companies.
Customer Lifetime Value (CLV) Prediction
By analyzing customer demographics, claims history, buying behaviors, and other insurance data, machine learning models can help insurers predict approximate revenue that a particular customer can generate for a company over their partnership. Based on this information, the company can up-sell or cross-sell by reaching out to these customers with personalized products and services.
VahanBima is a leading provider of insurance services in India. The company predicts CLV using a linear regression algorithm and then uses the model results to allocate resources better and target customers with 100% personalized experience offers.
Next to predicting the customer’s profitability, insurance businesses can also use machine learning to forecast the likelihood of particular customer behavior — for instance, their maintenance of the policies or refusal. Based on these insights, the company can then update its business strategy.
Insurance product recommendations
Product/policy recommendations are one more great AI application in insurance. Based on volumes of historical data, AI algorithms can analyze customer profiles and provide the most suitable policy offers for them out of those available. As a result, this will raise the insurer’s chance of a successful sale.
An interesting approach is to use collaborative filtering. This means that an ML system suggests a product to customers based on their similar risk profile and/or purchase history to other customers who have bought this product earlier.
Working with text documents is a significant part of an underwriter’s job. Going through tons of documents like health records, financial reports, claims history, etc., etc. takes at least half of a working day for those in insurance. No need to say that these tasks are monotonous and tiresome but require lots of accuracy.
Automatic data extraction with the help of machine learning can be a lifesaver in this case. By scanning through texts automatically, ML algorithms retrieve core words and/or phrases from unstructured insurance documents. Even better, they could identify synonyms or related words, e.g. searching for a “pet” when you’re looking for a dog.
Moreover, data scientists can also generate automatic summaries of documents by using NLP algorithms. This way, insurance agents won’t need to read 100-page financial reports.
In insurance landscapes, a lapse occurs whenever the customer stops paying the premium, and the contractual grace period expires. Among the applications of ML in insurance, we can also mention its usefulness in lapse management. ML can help to predict those policies that are at risk of expiring or terminating earlier. This way, insurance agents will have enough time to interfere and talk to clients about the reasons for unpaid premiums and negotiate terms of payment.
Another benefit here is the ability to segment customers based on the lapse risk with the help of ML. So the insurance company could use the factor of solvency when issuing policies to customers.
Car insurance companies can benefit from computer vision and automated damage inspections. With AI-based image processing, a customer can upload a photo of the damaged parts of the car, and the system processes the photo automatically. On the way out, both the insurer and the customer receive a detailed report covering which parts are replaceable and repairable (and which are not), together with the approximate estimate for the repair.
Intelliarts built a car damage detection solution, which is composed of two separate AI models. One of them indicates the damage, and the other identifies the affected parts. The outcomes are then compared against similar cases in a prepared image database to provide the customer with repair cost estimates.
Property risk assessments
Computer vision technology can come in handy to property insurance companies too. This innovative approach allows for the automated extraction of valuable information from images to help insurers with the assessment of property conditions, damages, and maintenance needs.
By interpreting visual data, computer vision systems provide home insurers with a more comprehensive and objective view of a property’s state. Companies can then use these insights to enable more efficient and faster property risk assessments.
If we’re speaking about customer service again, chatbots and all sorts of AI-based virtual assistants are of great help in insurance, facilitating customer interactions, answering queries, guiding users through the tricky claims processes, etc. These help to enhance customer experience by providing real-time responses, automating routine tasks, and improving overall efficiency in handling insurance-related inquiries.
A prolific example is the use of TextQBE by QBE North America, a global insurance leader. This AI-powered virtual assistant helps the company answer simple questions from customers about deductibles and process photos of receipts and other documents. Eric Sanders, senior vice president of QBE North America, recognizes this intelligent conversion platform as a great way to take their customer experience to the next level:
Customer satisfaction scores through the service have averaged 4.6 out of 5 – many with comments such as ‘great customer service, fast and friendly, answered all my questions.
Training employees is a crucial element for insurance companies to establish a reliable workforce within the organization. It can also become one more interesting application of AI in insurance.
By using AI in employee training, insurers can benefit from:
- A more personalized learning experience, which takes into account the job role, skill level, and learning style of employees
- Fast delivery of information and more custom content like the use of videos or one-to-one communication, which together reinforce the user’s learning goals
- Ability to translate training materials into multiple languages
- Personalized assessment techniques
- Automated feedback, with areas of excellence and improvement
- Opportunities to learn outside of their workplace
Intelliarts as your ML partner in insurance industry
Intelliarts combines vast expertise in machine learning and domain knowledge of the insurance industry. We provide technology consulting services, including AI consultation where we could assess your readiness and prepare a detailed roadmap for adopting ML into your processes. Our data science team could then help you with ML implementation.
Our portfolio counts a range of success stories in the insurance domain. Among those most interesting projects, we can mention:
- Improving cold calling success rates for property and car insurance company
- ML-powered solution to increase lead quality and agent efficiency for insurtech
- Predictive lead scoring model for insurance
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.
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.
Using 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.
How can machine learning be used in the insurance industry?
There are many use cases for machine learning in the insurance industry, from automated claims processing to fraud detection, price optimization, and automated risk assessment.
What are the major challenges for insurance companies when it comes to machine learning?
When deciding to go with ML techniques, the insurer may meet the problems of disparate data and data silos; low data quality; or the lack of data. All these can be handled under the guidance of experienced data scientists. Another problem that can arise is the lack of ML expertise, which may be solved by outsourcing ML implementation to a reliable vendor.
What is the role of machine learning in insurance?
ML development solutions can bring lots of value to insurance companies. For example, it can help with predicting trends and, hence, better decision-making and cost optimization. Another benefit includes increased customer loyalty thanks to faster and more quality services delivered. Insurers can achieve these via automated claims processing, personalization, and more accurate underwriting services.
How can Intelliarts help implement machine learning in the insurance business?
Intelliarts combines vast expertise in machine learning and domain knowledge of the insurance industry. We provide technology consulting services, where we could assess your readiness and prepare a detailed roadmap for adopting ML into your processes. Our data science team could then help you with AI and ML implementation for insurance business.