3 key areas where AI is transforming insurance today

In the two years since generative artificial intelligence (genAI) burst onto the global stage and quickly found its way into the strategy playbooks of the world’s largest businesses, we’ve all heard a lot about the technology’s potential. Countless surveys and one-off product demos have touted a future vision where workflows are streamlined, customer interactions are more personalized, and manual tasks are automated. There have been fewer stories about how AI is being used right now, today, to transform critical business functions. But it is happening.

The insurance industry has been one of the first industries to fully embrace AI and quickly start finding ways to capitalize on its ability to parse vast databases of structured and unstructured data to surface meaningful information. That early adoption was due to the fact that insurance is a data-intensive business and, in addition to seeing the benefits AI could bring, most large insurers were already pretty far along on the cloud migration and data modernization efforts that are needed to support large-scale AI rollouts.

Over the course of our work together integrating AI into a wide range of insurance workflows over the last several months, we’ve been able to identify three key areas where AI is already having a major impact on everything from back-office operations to frontline customer support.

1 – Claims processing

The claims process is the single most important part of the insurance customer experience. It is also one of the most challenging functions to manage and it has historically been categorized by many in the industry as one of the biggest leaky buckets in the business. For example, personal lines auto insurers lose an estimated $30 billion each year due missing or erroneous underwriting information or other errors that occur in the claims process. In health insurance, some 15% of all claims submitted for reimbursement are initially denied, and more than half (52%) of those denied claims are eventually paid. Meanwhile, processing times keep getting longer, putting a strain on customers and adding costs for insurers.

By integrating AI into the claims workflow, it is now possible instantly fetch and reconcile necessary data from multiple systems inside the carrier along with external data sources, in many cases making it possible to auto-adjudicate a claim in seconds. In a pre-AI world, that process would have involved manually digging through policy notes, corroborating claims filings, and analyzing customer call logs and other data sets across half-a-dozen different systems before a decision could be made.

2 – Underwriting

The underwriting process is another sticky point in the insurance workflow that has been begging for innovation for years. In life insurance, for example, where the problem is particularly acute, the onboarding cycle for a new policy can be upwards of six weeks while agents and underwriters chase down documents, press customers for background information, and determine risk profiles.

With AI-powered tools, insurers can not only pull together all of this information far more quickly, they can also develop more personalized products that address the specific needs of individuals, versus relying on pre-determined, generic risk profiles. This creates opportunities to cover underinsured and underrepresented individuals who may otherwise have not met the necessary policy screening criteria.

3 – Fraud prevention

Fraud is another insurance industry pain point that’s being addressed more effectively with AI. Industry-wide, roughly 20% of all insurance claims are fraudulent at a cost of $308.6 billion annually. A big part of the problem with insurance fraud is that many of the historical best practices for managing it have been retroactive. Using a combination of audits and random screening, insurers have only had a piecemeal picture of their total fraud exposure. They’ve been forced to chase fraudulent claims only after they had already been paid.

Now, AI is being used to scour through a wide variety of data sources including claims histories, public records, Centers for Medicare and Medicaid Services (CMS) guidelines, medical newsletters, and regional regulatory data to quickly identify changes and update fraud detection algorithms in near-real-time.

A Better Customer Experience

While these improvements are largely centered on operational workflows, the end-result of all of them is a better customer experience.  For example, we recently had an exchange with a life insurance beneficiary that needed to file a claim for their loved one. Expecting a long, drawn-out process with lots of paperwork and chasing down documents, the beneficiary was shocked to find that we were able to instantly access funeral home records and other third-party data sources to make the process completely seamless. Their claim was then paid within two days.

These are precisely the types of emotionally charged complicated interactions insurers have with their clients every day. When we can use technology to make those human interactions more empathetic and deliver results instantly, we go a long way to fulfilling our customer promise and making people feel good in the process. In that way, AI is helping us to drive better human experiences.

To learn more about the work EXL is doing to build GenAI into enterprise insurance workflows, please visit here.

About the authors:
Munish Mahajan is senior vice president, insurance industry consulting and solutions at EXL. Siddharth Kuckreja is senior vice president and chief technology officer at TrueStage.