The Definitive Guide to Machine Learning in Insurance
In this guide, read about the great potential of ML to increase insurers’ operational efficiency, boost customer service, and accessibility.
From healthcare to financial services to manufacturing and transportation, the world's biggest industries are going through digital transformation. For sure, the insurance sector doesn’t lag behind in this case.
As consumer expectations are growing bigger in terms of efficiency and customer service, we see the development of so-called digital insurance. This is an umbrella term that includes various innovative technologies that are disrupting the insurance industry and changing the way it operates. According to Deloitte, AI/ML is the main technology that insurers are ready to invest in. In 2022 alone, 74% of insurance leaders are expecting a significant increase in their spending on this technology.
And since more insurance executives believe their business model is ripe for enhancement with machine learning, it’s time to talk about what’s in there for insurance and insurtech companies.
A brief overview of machine learning
When it comes to machine learning (ML), we’re basically talking about extracting knowledge from data. According to IBM, ML can be defined as,
A subfield of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate how humans learn, gradually improving its accuracy.
Deriving from historical data, an ML algorithm can learn on its own, make predictions and conclusions without being explicitly programmed. So an ML model generates the results as accurately as possible, and it’s important it was fed with enough quality data.
There is a substantial difference between AI and ML, yet the two concepts are frequently confused in business. As implied by its definition, ML is a branch of artificial intelligence. While both belong to computer science, AI is a broader concept than ML that is designed to simulate human thinking capability and behavior. From a business perspective, both AI and ML can help you conduct business operations better, with more accuracy and lower spending.
Another critical concept here is deep learning. Alike, it’s a subset of ML that handles algorithms inspired by the structure and function of the human brain. The major difference between the two lies in how the data gets presented to the machine. While traditional ML algorithms rely on structured data, deep learning can deal with massive amounts of both structured and unstructured information working based on the multiple layers of artificial neural networks.
We can describe the structure of the simplest neural network as follows. The input data goes into the input layer. Then, hidden layers are helping to discover any hidden features from the info, and the output layer finally provides the results.
Read the post covering the experience of our data engineers at the Hack4Retail Hackathon as they applied a deep learning approach in solving the third task of the hackathon.
The value of machine learning in insurance
Insurance is known for its high regulation and reluctance towards change and new technologies. Customers all over the world complain about lots of paperwork and bureaucracy when it comes to filing a claim or signing a new policy.
That being said, the insurance industry has become highly digitalized in recent years. And AI/ML technology played a prominent part in this digital transformation. The graph below illustrates the growth of the AI/ML market in insurance in different geographical regions from 2017 to 2024.
As we observe a global push towards digital insurance via the use of machine learning, let’s dive deeper into how ML is contributing to the insurance sector.
Underwriting is an important part of insurers’ daily work when a company calculates the risks of insuring someone’s business, assets, or life and picks up the right price for this deal. So, ML technology can increase underwriters’ efficiency in a range of ways:
Automating submission triaging
Streamlining submission processing
Assessing risks more accurately
Predicting potential failure rates and other operational risks
Optimizing rates for premiums
Improving coverage recommendations
Read more about intelligent underwriting, its core use cases, and benefits in the post.
Another part of the agent’s working day is spent handling insurance claims. This includes different stages, from claims registration to investigation, adjustment, and settlement. Machine learning in insurance can speed up and automate these processes. Hence, it reduces the time spent on claims processing and improves customer experience.
How ML can optimize claims processing?
By smart claims triaging
By automatic and distant damage evaluation
By ML-powered auto-adjudication
By automatic detection of fraudulent claims
Fraud detection is probably the most challenging task in insurance. Businesses lose billions of dollars annually because of fraudulent claims. And while they’re thinking about how to deal with fraud, malefactors are using this time to make up new fraudulent schemes and patterns.
Machine learning is, though, very useful in insurance fraud detection. It helps insurers detect fake claims (and process almost any type of data, from structured to unstructured). Similarly, an ML-based solution allows insurers to detect duplicate claims, and we’re not speaking about “exact matches” only but more complex cases too. Add here also the frequent fraudulent cases of upcoding in medical bills and overstated repair costs in auto insurance.
Customer churn prediction
Detecting early churn signals and recognizing at-risk customers provide insurers with an exclusive opportunity to prevent customers from leaving the company before it actually happens. With an ML model, a company can track at-risk customers based on their behavioral patterns and predict the churn rate. So, it can reduce it and stay competitive in the market.
Insurance companies can also consider implementing an ML-based insurance lead management system. And this will bring their lead management to a fully new level. By extracting valuable insights from lead data, ML helps insurers with lead analysis and classification. So, the company could choose quality leads and not waste time on those with low conversion rates.
Moreover, a powerful insurance lead management system can help you calculate the sales threshold by analyzing various important factors, such as lead revert time, link clicks, or web visits. When the lead approaches the industry benchmark, your agents will know it’s worth a cold call or a message.
Here are two interesting case studies describing how we used ML to improve cold calling effectiveness and optimize lead scoring in insurance.
It’s no surprise that personalization works the best in insurance marketing. Customers in insurance (as anywhere else) would like the services to match their specific needs and requirements.
As expected, ML can help insurers deliver highly personalized and relevant experiences. ML algorithms extract valuable insights from the data in demographics, customer behaviors, attitudes, interactions, and lifestyle details, which insurers can then exploit in their marketing initiatives:
Personalized offers and policies
Customer segmentation proves to be a critical method of marketing in insurance since different customers have various expectations for insurance services. ML algorithms can come in handy in this case, too.
Analyzing important data, such as customer income, age, gender, and locations, ML classifies customers into different groups. It can also seek patterns based on more complex variables, such as behaviors or personal preferences. Using these conclusions, an insurer then develops specific attitudes or uses marketing strategies to target different customer segments.
Another interesting use case of ML in insurance is document-based digitalization with optical character recognition (OCR). This technology allows recognizing handwritten and printed texts, which helps insurers process documents faster and solve operational inefficiencies. Besides, ML-powered data extraction allows insurers to forget about manual re-typing. When paired with computer vision, it can accurately render every single pixel and translate the information if needed.
In insurance, OCR is a big finding when it comes to customer onboarding and claims management, helping to extract the info about the customer in the blink of an eye. Tokio Marine uses an ML-based OCR service to handle claims. The system has allowed the company to reduce human error by 80% and processing time by 50%.
ML-based pricing models have been gaining popularity recently in insurance as a great way to optimize pricing policies. Their major benefit is an opportunity to include a variety of variables in the premium, with different levels of impact. ML algorithms also process more sources and channels (even alternative ones, such as previous claims or social media) when defining pricing. The insurer can never achieve the same effect with manual price optimization. Finally, an ML-based pricing model allows insurance companies to adjust premiums dynamically, for example, by reacting timely to market fluctuations.
See a related reading in case you want to go deeper into ML use cases in insurance along with its real-world examples.
Machine learning in various insurance domains
While we covered applications of ML in insurance generally, it’s also interesting to explore how ML is improving the insurer’s bottom line and the customer experience in specific domains.
This is probably the most progressive domain in the insurance business. So, it’s no wonder that health insurance exploits disruptive technologies and machine learning a lot. ML systems help health insurance companies identify at-risk individuals and decrease rising costs in the field.
Since an ML model can look at and extract patterns from millions of data points at once, it also helps with custom recommendations and insurance packages to create a robust user experience in the domain. With the help of ML, Accolade offers personalized insurance services and helps patients choose the most relevant and cost-effective health coverage. Now the company reportedly has over 1.1 million customers.
Property and casualty insurance
Personalized customer experience, as well as improved level of customer satisfaction, also explains the use of ML solutions in property and casualty insurance (P&C). Statistics show that 84% of insurance organizations have introduced or are planning to introduce ML for this purpose. Based on customer historical data, ML algorithms help P&C insurance carriers target customers with more personalized plans tailored to their needs and not to waste time on prospects with low conversion rates.
Another common use case in P&C insurance includes computer vision. This technology allows analyzing property quickly, accurately, and without extra effort when the claim is filed. The investigator doesn’t need to go to the field. Besides, the insurer can suggest upselling when it notices new structures added to the property, such as a garage or a swimming pool.
The standard use cases of ML in auto insurance include claim processing, intelligent underwriting, and fraud detection. Still, we can also mention here more interesting applications, such as the use of ML and computer vision for damage estimation, calculating repair costs, and analyzing the impact of the accident on the driver’s future premiums.
For example, Liberty Mutual insurance company is using an ML-based mobile app, where its customers involved in car accidents can upload photos of their damaged car right at the crash site. Trained with thousands of car accident images, the ML model will automatically calculate repair costs. In real-time, the customer can also settle a claim using the same app.
Many businesses that underwrite life insurance find ML algorithms useful in risk prediction and optimizing prices. In this scenario, the technology has a high potential to automate and speed up the evaluation process. It also has access to wider data sources and can base its predictions on more variables, such as mortality statistics in the region. A life insurance application is treated as a standard supervised learning problem, where
The application acts as a data point
The information (or data columns) provided by the customer are the features of this data point
The risk rating of the customer is the output the insurer is aiming to predict
Aside from underwriting, life insurers can apply ML in churn prediction, personalized marketing, and lead management.
Steps to implement ML in the insurance business
1. Define your business purpose
A good idea for getting started with ML is to consider what business tasks you need it for. In this guide, we have mentioned nine use cases of machine learning in insurance. Go through them and think about which are relevant to your company specifically. Then, prioritize them based on your biggest pain points and those that are only nice to have.
Try to be as specific in your objectives as possible. You can also seek the consultation of professional data engineers who have industry expertise in insurance. For instance, we as Intelliarts always start from business understanding before moving to any custom solution development. So, we can help you understand the roadblocks in your insurance business efficiency and those processes that can be optimized with the help of ML.
2. Understand and prepare data
Getting quality and enough data is a top priority for building a reliable ML solution. Under the supervision of expert data scientists, you identify and understand the data you own now or need to collect in the future.
Most insurance companies have repositories of existing data (think about your historical claims data or policy data). Even if you don’t have enough raw data, a professional ML team can assist you with data collection, advising you on the best types of data, formats, or ways of storage. Data engineers can also back up your business with synthetic data if needed.
After data collection, data scientists would also need to invest time in data exploration to find patterns in the data, investigate relationships between variables, and determine how these will affect the outcome. Data visualization can be really helpful at this stage. Also, data cleansing and validation, as well as data structuring, may be needed to process the data the most efficiently.
3. Choose and train the ML model
This is when the ML model is actually being built. Data scientists need to explore different ML algorithms and choose the ones most suitable for your business problem. Then, these algorithms are trained — the model is fed with training datasets, the results are evaluated, and engineers fine-tune the model parameters until they get high accuracy.
4. Test the ML model
Testing is a critical step in building an ML-powered solution. Here data engineers under the guidance of your insurance company check whether the ML system works as planned and whether the results are satisfying enough (maybe the model needs more training).
5. Put the ML solution into production
When the ML model is finally ready, data engineers will help you deploy the solution. Ideally, the team should also assist you with model monitoring so you could track the results without diving deeper into the technical details. With decent model monitoring, your insurance company will also know when the model needs retraining in case you get new datasets.
What’s the future of ML in insurance?
Insurance is a highly competitive industry, especially now when many companies are operating online. A good way for your insurance business to stand out in the competition is to use the benefits of innovative technologies and machine learning, in particular.
ML algorithms have a good chance to transform the frustrating experience of insurers’ customers from inefficient and time-consuming services to automatic, fast, and more affordable ones. The insurers of the future (and you can become one of them easily) are getting value from the abundant insurance data they’re sitting on and are using ML to:
Increase underwriters’ efficiency
Handle claims faster and in a more productive way
Detect fraudsters in a few minutes
Predict potential churners and take measures to retain customers
Analyze leads more effectively and choose the most promising ones
Extract valuable insights about their customers to apply personalized marketing tactics
Segment customers to target them better
Extract data easily
Optimize premiums and adjust them dynamically to market changes
An in-depth study on applying ML in insurance businesses
Now that you have read this article, you know how machine learning can foster the digital transformation of the insurance business. However, if you plan to introduce this particular technology in your company, you might find it useful to read a more in-depth study on building data-driven and ML-powered solutions for the Insurance industry.
Our white paper Machine Learning for Insurance Business: Using Data to Foster Innovation will provide you with more tips on how to find a practical application for your company's data, optimize insurance processes, and solve different sorts of business challenges you may face.
Are you interested in introducing machine learning into your insurance business model? Or maybe you’re just exploring the available options? Reach out to our team of professional data engineers who can use their vast industry experience to give comprehensive answers to any questions about ML in insurance.