By: Ilana Jucha
Blog 1 minute read

3 Use Cases for AI in the Insurance Industry

November 27, 2022

Today, AI has become one of the hottest technological commodities in the digital space. As companies across all industries are clamoring to be at the forefront of innovation, they are turning to AI to help improve processes and operations, products and services, and consumer experiences. 

Globally, 35% of companies in 2022 have reported adopting AI, while 41% have stated that they are exploring how AI can be utilized within their organization. This number is astonishing when you think just how recently the term AI entered modern business discourse. It is clear that many business leaders see a massive strategic advantage in AI. 

While an industry that has traditionally been somewhat resistant to change, today, the Insurance sector is one of those eager industries embracing the AI revolution full-heartedly. Despite global challenges that have rocked the insurance sector over the past few years, such as the COVID-19 emergency, digital acceleration, AI adoption and monetary investment in technology have remained a high priority for insurers. 

Currently, 44% of insurance executives say their organizations are advanced in the use of AI and machine learning-based data analytics. While 65% of executives state that they plan to invest more than $10 million into AI in the next three years

Insurers recognize that AI is a game changer. The opportunities for AI are far-reaching, from optimizing the claims resolution process to improving fraud detection; AI has the potential to make a big difference on an insurer’s bottom line. 

To better understand how AI is applied in the sector, we break down three widespread use cases for AI in insurance and explore what impact these applications have on the industry.

Claims Management 

Within insurance operations, the process of Claims Management is arguably one of the most outdated functions. Many insurers still employ practices that have been commonplace over the past 100 years. 

Traditionally, each claim is processed and evaluated manually by one or more claims handlers. The claims handler is tasked with assessing dozens, if not hundreds, of documents at a time. Further, claims are evaluated based on an individual handler’s experience level, ranging from expert to novice. Ultimately, this results in a claims-handling process that is both time-consuming and subjective rather than objective. 

The process of underwriting in claim management is also staggered. A survey by Accenture noted that 40% of underwriters’ time is actually spent on non-core and administrative activities. Accenture further reports that this will represent an efficiency loss of up to $160 billion over the next five years. 

What is the impact of all of this when we factor in consumer behavior? Today, consumers are demanding greater speed and accuracy with service delivery. The insurance industry is not immune to this trend. As a result, consumers increasingly have more choices, and the greater level of service an insurance company can provide, the more likely they are to take the lion’s share of a crowded market. 

This is where AI and ML come in. 

AI technologies are helping innovative insurers optimize the claims processing system like no other technology has been able to. 

Instead of handlers wasting hours and days of their time shifting through a myriad of documents, AI can analyze these documents automatically, finding patterns and correlations unseen to the human eye. This means that claims can be resolved within minutes, whereas before, claims resolutions took days and even weeks. 

Steadily traditional insurers are adopting these technologies, but they will need to pick up the pace significantly as new startup insurance providers are gaining a significant foothold in the industry. In the near future, claims processes will become increasingly simplified online to meet the demands of tomorrow’s tech-savvy consumers. 

Fraud Detection 

It is estimated that insurance fraud accounts for up to $308.6 billion in losses per year in the US alone. This staggering number is sure to send shivers down any insurer’s spine. 

When insurance fraud happens, it is to the detriment of consumers; insurers often need to turn to increasing consumer premiums to offset such losses. 

Fraud in the insurance industry can happen in a myriad of ways. Claimants can exaggerate the value of a claim, make outright false claims, or, if they feel particularly lucky, they can straight-up duplicate claims. Unfortunately, the nuances of fraud often make it difficult for insurance handlers to detect. 

Take this interesting scenario: A clinic administrator provides fake x-rays showing a broken arm to patients to help them exaggerate on a claims application. A patient compensates the administrator for this x-ray and submits his application to his insurance company. Within the traditional claims process, it would be difficult for the claims handler to determine that this x-ray does not belong to the claimant. Nevertheless, if the insurance company employed an AI claims management tool, the AI algorithms would be able to analyze thousands of past claims and quickly determine that this particular x-ray has been used in the past, indicating that the claim is fraudulent.

AI gathers information across every customer touchpoint and profiles potentially fraudulent activity based on behavioral patterns and correlations. AI algorithms provide a claims score and a reason for this score to help handlers determine whether the claims are suspicious and therefore need further investigation. 

The potential monetary savings insurance can make by employing AI to detect fraud are immense. These savings come both to the insurer and the customers, providing value to everyone involved in the insurance chain. 

Claims Payments 

The integrity of payments to an insurance company is everything.

Traditional analytical-based payment systems use rule-based methods that drain hours of labor time. These processes often prolong turnaround times and can lead to false negatives. Gartner reports that between 3% to 7% of all claims are paid improperly.

The opportunities for AI and ML technologies within payment systems is evident. With AI, insurers can profile and benchmark claims’ paid amounts and predict the appropriate claim amount to be paid. In addition, the system also considers the associated suspicious scores and flags any abnormal payment issues.

With these systems, Insurers can move away from post-payment recovery to a prepayment cost avoidance model, ensuring that payouts are done so fairly. 


In the past, it could be argued that insurance companies have been somewhat behind the times when it comes to technology adoption. However, today that is all changing. Insurance companies are embracing innovation and disruption more than ever, focusing on adopting emerging technologies such as AI and ML to optimize their operations. 

For insurers, AI technologies can be harnessed across various functions, providing monetary value for the insurance company and its customers. Over the last five years, AI costs have decreased significantly, allowing insurers to gain greater value from these technologies. In this article, we have outlined just a few ways AI is transforming the insurance sector; however, the potential of AI in this industry is not limited here. At umAI, we believe AI has the power to transform the insurance sector as we know it.