Artificial Intelligence and Machine Learning
in Insurance Sector
As rapid technological advances reshape the insurance landscape, carriers are encouraged to adopt technologies to enhance customer service, create better solutions for operational efficiency, and build ever more accurate underwriting models. Artificial Intelligence (AI) and particularly Machine Learning (ML) hold a promise in this regard as it has proven to be successful in multiple disciplines including ecommerce, predictive maintenance, election forecast, and drug discovery.
Figure 1 shows the 2019 global AI market share by economic sector. It is worth noting the absence of the insurance sector as a pioneering industry adopting AI in its operations like other industries that invested in the technology with various degrees of success.
Figure 1. Global AI market share, by end use, 2019 (%) (1)
The increasing adoption of AI/ML on the global scene is a testimony of the critical role of this technology in helping industries decrease costs and increase revenues. Figure 2 shows forecast of AI global market size in the next 5 years.
Figure 2. Forecast of global AI market size from 2018 to 2025 (2)
Other sources are even more generous in their forecasts as they estimate that the global AI market size is expected to grow at a compound annual growth rate of 42.2% from 2020 to 2027 and will reach a whopping $733.7B by 2027 (3)
To have a better appreciation of the AI technology impact on various business sectors, consider figure 3 that depicts results from a poll of 1,872 enterprises worldwide indicating cost decreases and revenue increases from AI by function. Enterprises report that AI drives revenue in sales and marketing while reducing costs in supply chain management and manufacturing functions.
Figure 3. Results from a poll of 1,872 enterprises worldwide indicating cost decreases and revenue increases from AI by function (4)
Marketing and sales include use cases of customer service analytics, customer segmentation, channel management, prediction of likelihood to buy, pricing and promotion, closed-loop marketing, marketing-budget allocation, churn reduction and next product to buy. Product and service development include use cases of product feature optimization, product development-cycle optimization, creation of new AI-based enhancements, and creation of new AI-based products.
2. Potential for Machine Learning in Insurance Value chain
It is believed that most insurance companies process only a small percentage of the data they have access to. Such data is mostly structured and housed in traditional databases. Analyzing unstructured data to extract valuable insights and trends requires advanced data science techniques focused on AI. ML as a major class of AI, has proven its efficiency across many industries in extracting useful information from both structured and unstructured data to drive business decisions and discover valuable insights that otherwise go unnoticed. For instance, ML-based analytics of insurance data can be used across the value chain to understand and evaluate risk, efficiently process claims, and predict customer behavior with a great accuracy. Other applications of insurance industry can also benefit from the deployment of ML including exposure analysis, underwriting risk analysis, intelligent document process, submission process, pricing, risk appetite, subrogation, litigation, and fraud identification.
According to a surveyed group of Insurers asked where they see AI adding value to their businesses in terms of understanding and managing risk, the majority indicated the cases of exposure analysis, underwriting risk analysis, and submission process. Figure 4 shows the potential value of ML in understanding and managing risk. Such a survey resulted in classifying insurance applications in three classes: i) high value class of applications where 52-77% of Insurers indicated that such an application represents a potential top AI value for them, ii) medium value class of applications where 20-47% of Insurers indicated that such an application represents a potential top AI value for them, and iii) low value class of applications where only 7-13% of Insurers indicated that such an application represents a potential top AI value for them.
Figure 4. Potential AI value for understanding and managing risk. The percentage value on front of each category indicates the percentage of insurers who responded that such a category is a top value area for them where AI can add value (5)
3. Example of applications poised to benefit from ML
It is a new reality that machines will take over many of human jobs starting with customer service where chatbots and similar gadgets built on ML-based Natural Language Processing (NLP) techniques will handle initial interactions with the customer to identify his/her intent and to address customer concerns or call for human help if needed. The case of other applications potentially benefitting from ML is even stronger as ML-based algorithms are well poised to handle unstructured data where classical algorithms fail to extract needed information and insights. Following, are a few applications of the insurance industry poised to benefit from advances made in ML.
3. 1. Insurance advice
Customer satisfaction is expected to be higher when ML algorithms are deployed to provide personalized services and recommendations for insurance products that are best for that specific customer based on his/her profile and previous behaviors of other consumers who share similar experiences and personalized information.
ML is capable of scavenging profiles of thousands and thousands of consumers to extract personalized insights and recommendations. Efficient ML techniques such as clustering and classification can be deployed to give advices that will most certainly work for a given customer using tailored tools and products. For example, ML-based clustering techniques can learn that a given customer is classified with a specific group of consumers of a certain age bracket, gender, geographic location…etc. As such, such customer is most likely interested on a new insurance product based on the known responses and preferences of other consumers belonging to the same cluster.
3. 2. Claims Processing
Automating claims processing is a great feat of the Insurance industry that ML can help achieve on many levels. ML is a powerful technique that can enable building efficient predictive models to help insurers better understand claims costs and process pain points that need to be addressed on a timely manner. These insights and efficient predictive models can help a carrier save millions of dollars in claim costs while at the same time increase customer satisfaction through fast settlement, pointed probes, and more efficient case administration. Those predictive models can help insurers in their plans and forecasts by budgeting accurate figures for funding allocation to claim reserves.
Deployment of computer vision to automatically scan documents using ML-based OCR (Optical Character Recognition) and NLP to interpret document content including handwritten claims can significantly reduce the document input load. Another benefit of using ML-based system instead of a human to handle claims is the protection of customer privacy.
Using ML to automatically process claims reduces input time, eliminates human error, and provides fast and stress-free claims settlements.
3. 3. Fraud prevention
The FBI estimates that more insurance companies lose more than $40 billion per year due to insurance fraud (non-health insurance) costing the average U.S. family between $400 and $700 per year in the form of increased premiums (6). There are many kinds of insurance fraud including premium diversion, fee churning, and asset diversion, among others. Premium diversion is the most common type of insurance fraud involving insurance agents i) failing to send premiums to the underwriter and instead keeping the money for themselves and ii) selling insurance without license. In fee churning, a series of intermediaries take commissions by registering the same customer multiple times leading to the payment of multiple commissions on the same customer until there is no money to pay outstanding claims. The company left to pay claims is often a shell company made initially to fail. Asset diversion is mainly the theft of insurance company assets, particularly during a merger or an acquisition. False or exaggerated claims by policyholders is another source of insurance fraud that costs the industry billions of dollars.
In addition to those types of frauds, there are other kinds of insurance frauds that need to be addressed by insurance companies. ML is a powerful tool that can help in this regard by identifying potential fraudulent claims faster and more accurately. ML is efficient in analyzing unstructured data and extracting hidden trends and turning points to identify potential fraud and expose its methods.
ML-based algorithms are known by identifying hidden and meaningful trends and patterns in complex data while at the same time minimizing false alarms. In addition, those algorithms learn more over time and get better as more data and more information become more available and thus learn how to perform those detection tasks better over time. Once the ML model has been trained on big enough data it can properly with high confidence detect and extract abnormal schemes including fraudulent behaviors.
4. Concluding thoughts
This short analysis pointed out the importance of ML for the insurance business. Applications such as insurance advice, claims processing, and fraud detection are poised to benefit tremendously from ML. The potential of applying AI in various aspects of the insurance industry is so broad. As such, insurers need to be thorough and focused on their exploration of the technology. As insurers consider and evaluate ML for their business operations, they should consider developing proofs of concept, testing derived ML benefits, and extending deployments as they become more educated and successful in exploring this game-changing technology.