How GenAI will change the risk equation for insurance companies

When a massive container ship veered off course and rammed Baltimore’s Francis Scott Key Bridge in late March, the entire U.S. East Coast felt the emotional shock of the collapse — and business impact. The disaster has set off red flags regarding port vulnerability and aging infrastructure. It’s also shaping up to have a significant impact on the industry with record shipping  losses, costing insurers upward of $4 billion.

Misallocation of underwriters’ unique skills
Insurance is a tricky business, and underwriters perhaps have the most complex task of all. In evaluating and assuming risk for third parties, it is the underwriters who set premiums for insurers and guarantee payment to these customers when catastrophe strikes.

Unfortunately, underwriters spend the majority of their time, as much as 70%, on activities not core to underwriting work, like wrangling incomplete applications, data collection and preparation, and administrative and sales tasks. The result is significant with lost productivity, longer application processing times and limited numbers of applications processed, poor customer experience, higher chances of error, pricing issues, suboptimal loss ratios and business lost to competitors. The field is ripe for correction and it begins with Generative AI.

How Generative AI can help
Generative AI (GenAI) offers a whole new range of capabilities to the insurance industry. Its ability to process natural language allows it to understand documents, like a human. Large language models (LLMs) can read huge volumes of information and be fine-tuned to accurately respond to queries.

Consider, for example, how GenAI might be applied to workers’ compensation insurance underwriting in the U.S., a liability insurance for businesses to pay injured workers for their losses. All 50 states have their own underwriting guidelines for this heavily regulated form of insurance. A multi-state entity like McDonalds needs to insure its workers in case of an accident across 50 sets of rules. One state will have a 350-page guidelines document, and another 1,000 pages. Underwriters must comply with all the rules of all the states.

With the traditional AI tools, underwriters can automate some data processing tasks such as organizing and managing these guideline documents. They can perform basic searches and sort information. However, traditional AI capacity is limited to handling structured data and simple text processing without deep understanding. That left the bulk of comprehension and application of these rules to the underwriters.

This is where GenAI assists as it deeply understands the unstructured data and context of each state’s guidelines. It can sort through all those rule sets, data and documentation at lightning-fast speed, and, when trained well, yield evaluation output with a high degree of accuracy.  Also, it can track any change to the state-specific guidelines and ensure the underwriters comply with the latest guidelines.

A key tool, but not a replacement for human judgment
There is no doubt that GenAI augments underwriters’ capability. Yet, it isn’t equipped to tackle the more nuanced aspects of underwriting that require judgment and ethical thinking — that’s still the job of human underwriters.

Besides, GenAI is not all smooth sailing. From confidence-shaking inaccuracies to amplifying biases and introducing fresh cybersecurity concerns, the risks are real and, in some cases, still unknown. So, what is the best move? Keep the human touch in the mix. Relying solely on AI for decisions in insurance — whether it’s new business, underwriting or claims — might be tempting, but risky.

Care must be taken to ensure an underwriting GenAI tool is properly trained on specialized data and leverages reinforcement learning from human feedback (RLHF), retrieval augmented generation (RAG), and other techniques, to dramatically reduce inaccuracies, hallucinations and reflections of human biases. It should give the same answer for the identical circumstances every time.

By introducing GenAI gradually, starting with small, controlled projects, insurance companies can stay ahead of potential pitfalls, fine-tuning their approach as they learn. This careful, human-guided strategy clears the way for a safer, AI-enhanced future in the industry. 

A win for the insurer and the insured
With GenAI, both the insurance company and the insured benefit from a realistic pricing decision, but one based on the most wide-ranging, relevant data possible. With the best GenAI solutions, underwriters will have access to reliable and explainable results. By comparing volumes of like cases, patterns of activity and similar factors across insurance scenarios, underwriters can make the most accurate and ethical predictive analyses.

Within a few years, it is expected most insurance companies will have GenAI underwriter support. Initial insurance quotes, even for commercial buildings, will be accessible online at the click of a few buttons. When a commercial customer submits all the necessary documents, within seconds those documents will be processed, and GenAI will output a quote with eloquence and the capacity to answer complex questions.

Underwriters are the heart of any insurance company, and they’ll remain there, but in the AI era, they’ll have an even greater impact on the industry, advancing it faster than ever before.

See more:AI is a top priority but preparedness lags for insurersUsing AI to streamline workers’ compensation settlements