Unlocking the Potential of Machine Learning in the Insurance Industry: Real-World Use Cases and Benefits

This article kicks off a new blog series in which we dive into the state of machine learning in different industries – starting with the insurance industry. Machine learning is transforming the insurance industry by providing new and powerful ways to analyze and manage risk. With the help of advanced algorithms and vast amounts of data, insurance companies can now make more accurate and efficient decisions about key areas of their business. In this article, we will explore some of the ways that insurers can use machine learning to improve their operations and better serve their customers.

Machine learning can greatly improve insurance services
Machine learning can significantly improve insurance services. Image generated using DALL-E 2 by OpenAI.

Five Machine Learning Use Cases in Insurance that add Real Value

There are many potential machine learning use cases in insurance. The specific use cases that are most important will depend on the specific needs and goals of the insurance company. However, some common use cases in insurance include the following:

  1. Underwriting: Machine learning can improve underwriting by automating the process and reducing the risk of human error.
  2. Fraud Detection: Machine learning can help detect fraudulent claims by identifying patterns and anomalies in data.
  3. Customer Segmentation: Machine learning can help insurers segment their customer base and develop targeted marketing strategies.
  4. Claim Processing: Machine learning can automate and accelerate the claims process, making it more efficient and accurate.
  5. Risk Modelling: Machine learning can help insurers model and predict risks more accurately, allowing them to make better-informed decisions.

1. Underwriting

Underwriting is the process of evaluating a potential insurance policy. The goal is to determine whether to accept or reject the risk associated with it. This involves assessing the applicant’s information, such as their age, health, and financial history, to determine whether they are likely to need to make a claim on the policy and how much that claim is likely to cost. The underwriter will use this information, along with other factors to determine the premium that should be charged for the policy.

Insurers can use machine learning to automate the underwriting process and make more accurate and efficient decisions about whether to accept or reject a potential insurance policy. They can train models on historical data to identify patterns and trends that are associated with different levels of risk. For example, an algorithm might learn that applicants with certain medical conditions or occupations are more likely to make claims on their policies. With improvements in NLP, algorithms get better at understanding textual information. For example, an algorithm may learn that certain policy terms are associated with higher or lower levels of risk. Once trained, the algorithm can evaluate new applicants and make predictions about their risk level. This can help insurers to make more informed decisions about whether to accept or reject the applicant and to set appropriate premiums.

machine learning has make underwriting processes more efficient
Insurers use machine learning can make underwriting processes more efficient. Image generated using DALL-E 2 by OpenAI.

2. Fraud Detection

Insurance fraud is the act of intentionally providing false or misleading information to an insurer. Fraud in insurance can take many forms. For example, customers may provide false information on a policy application. Or they may exaggerate the value or extent of a claim, or sage an accident or theft in order to make a claim. Another type of fraud is when service providers such as hospitals exaggerate the costs of treating a patient. For insurers, fraud is a serious problem, as it can lead to higher insurance premiums for all policyholders.

Insurers use machine learning to identify patterns and anomalies in insurance claims data that may indicate fraudulent activity. By analyzing large datasets of claims data, insurance companies can more effectively detect and prevent fraud.

Image generated using DALL-E 2 by OpenAI.

3. Customer Segmentation

Customer segmentation is the process of dividing a customer base into smaller groups with similar characteristics. This is often done so that a business can tailor its products or services to the specific needs of each group, and can also help a business to target its marketing efforts more effectively. For example, a clothing retailer might segment its customers by age, gender, income level, and location, and then offer different promotions or discounts to each segment in order to maximize sales. Customer segmentation can help businesses to understand their customers better and to provide more personalized and effective services.

Insurers can use machine learning to identify distinct groups of customers based on their characteristics and behaviors. This can be useful for insurance companies that want to target their marketing and sales efforts more effectively.

A recent relataly article describes how to implement automated customer segmentation in Python.

4. Claim Processing

Claim processing is the process of evaluating, investigating, and resolving insurance claims. It typically involves verifying that the claim is valid and covered under the terms of the policy, determining the amount of the payout, and issuing payment to the insured party. Claim processing can be done manually or with the aid of specialized software. The goal of claim processing is to ensure that valid claims are paid quickly and accurately and that any fraudulent claims are detected and denied.

Machine learning can be used to automate the claim processing process, which is the process of evaluating and paying out insurance claims. By using machine learning algorithms to analyze data about the claim and the policyholder, insurers can make more accurate and efficient claim processing decisions.

Image generated using DALL-E 2 by OpenAI.

5. Risk Modeling

Insurers can use machine learning to develop more accurate and sophisticated models of their risks. For example, models can assess the risk associated with insuring a particular individual or property. Insurers can use such models to make more informed decisions about the risks they are willing to take. One specific use case is crime prediction, which we have recently covered in a separate article. Insurers can determine the likelihood that a person or property will be a victim of a specific crime. Following this understanding, they can then adjust their offerings accordingly.

Risk modeling can also use satellite data. This data can include information on weather patterns, topography, land use, and other factors that can affect the risk of natural disasters, such as floods and hurricanes.

Insurers can use satellite data to create detailed maps of areas at risk of natural disasters. These maps can include information on the type of terrain, the density of vegetation, and the location of buildings and infrastructure. This information can be used to create models that predict the likelihood of damage from natural disasters in a particular area.

Insurers can also use the data to create more accurate and detailed flood and wind hazard maps. These maps can help insurers to determine the risk of insuring a particular property and can also help them to create more accurate pricing for policies. In addition, by using satellite data to monitor the changes of a given area, insurers can also detect if there is any new constructions or any new developments in the area that can affect the risk level of a certain property.

Machine learning, in combination with satellite data, allows a new level of risk modeling. Image generated using DALL-E 2 by OpenAI.

Why don’t we see more Adoption?

It’s generally not a secret that the insurance industry is not at the forefront of machine learning adoption. While some insurers have started to implement machine learning use cases successfully, many others are still struggling with this task. There are several hurdles that insurance companies may face when implementing machine learning, including the following:

  • Limited access to data: Machine learning algorithms require large amounts of data in order to learn and make accurate predictions. However, many insurance companies lack the necessary organization and IT that could enable access to the data they need. Often the data is scattered across different systems and formats, which makes it difficult for insurance companies to train and use machine learning algorithms effectively.
  • Regulatory constraints: The insurance industry is heavily regulated, and there are many rules and regulations that govern how data can be collected, used, and shared. These regulations make it difficult for insurance companies to use machine learning algorithms, as they may need to obtain consent from customers or follow other specific requirements in order to use certain types of data.
  • Lack of expertise: Machine learning is a complex and rapidly-evolving field, and it requires specialized knowledge and skills to implement effectively. Many insurance companies may not have in-house expertise in machine learning, and they may need to hire or train employees in order to use machine learning effectively.
  • Resistance to change: As with any new technology, there may be resistance to the use of machine learning within insurance companies. Some employees may be skeptical of the benefits of machine learning, or they may be concerned about potential job losses if certain tasks are automated. Overcoming this resistance can be a significant challenge for insurance companies that want to implement machine learning.

Outlook

Despite several hurdles, more insurance companies become aware of the potential benefits of machine learning. The insurance industry is likely to see more adoption of machine learning in the coming years.

Machine learning is likely to play a growing role in the insurance industry. First, machine learning algorithms are becoming increasingly accurate and easier to implement. Increased accessibility enables insurance companies to use these algorithms to make more informed decisions about key areas of their business. The fact that more and more insurers are now migrating to the cloud contributes to this development. Second, the availability of data is increasing. More data makes it easier for insurance companies to train and use machine learning algorithms. Finally, there is growing recognition of the potential benefits of machine learning, both among insurance companies and regulators. As insurers and regulators learn about machine learning, they will likely drive further adoption of this technology.

Sources and Further Reading

  1. C Hull (2021) Machine Learning in Business: An Introduction to the World of Data Science
  2. Dixon et. al. (2020) Machine Learning in Finance: From Theory to Practice

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Author

  • Florian Follonier

    Hi, I am Florian, a Zurich-based consultant for AI and Data. Since the completion of my Ph.D. in 2017, I have been working on the design and implementation of ML use cases in the Swiss financial sector. I started this blog in 2020 with the goal in mind to share my experiences and create a place where you can find key concepts of machine learning and materials that will allow you to kick-start your own Python projects.

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