Even as large language models (LLMs) have made their way into almost every new mainstream product, some industry sectors have been slow to adopt AI. Risk management is one of them. Fortunately, that is starting to change.
According to a 2023 Deloitte study, only 1.3% of insurance companies had invested in AI. But data from this year indicates a shift is underway. In Conning’s 2024 survey, 77% of respondents indicated that they are in some stage of adopting AI somewhere within their value chain. This may sound a bit nebulous — some stage, somewhere — but it represents a sizable jump from the 61% of respondents the prior year. Additionally, 67% of insurance companies disclosed they are piloting LLMs.
We also found evidence of change with our own survey, one specifically tailored to risk management. We polled 1,000 risk managers and almost half indicated that they expect to adopt generative AI (GenAI) tools within the next three years. These increases collectively speak to the potential for AI to affect our industry.
Before we look at where we could go with AI, let’s look at the challenges that have slowed adoption in risk management.
See also: Providing Coverage for AI May Be Huge Opportunity
What’s Holding Us Back?
Based on our years of work developing AI systems for insurance and researching the needs within organizations, we’ve identified a few key issues:
- At the organizational level: Change is hard, and many companies struggle with integrating AI technologies into their existing infrastructure. Risk management systems often involve complex legacy systems that are not easily compatible with outside tools. They also may not have a clear line of sight into exactly what they want AI to accomplish or how to measure it. Seamless integration requires a lot of forethought and the right partner to ease the transition and ensure that the system is doing what an organization wants or needs.
- At the IT level: The technical complexity of AI tools requires specialized knowledge and skills. In risk management, as with many sectors, there can be a shortage of in-house expertise to manage and leverage new technologies effectively. Companies need to carefully consider their hiring strategies to account for this or perhaps look into outsourcing options to achieve the best results.
- At the adjuster or claims rep level: One of the big impediments has been that many people still don’t know exactly what AI does, how it works, or what its limitations are. Couple this lack of knowledge with mainstream messages about privacy concerns or AI taking people’s jobs, and hesitancy is understandable. Comprehensive education and training programs would help overcome the hurdle in terms of grasping AI’s potential to make their work easier and more efficient while staying within regulatory guidelines. Workers will also become more comfortable as they increasingly use models like ChatGPT and Gemini in their personal lives. They will see that AI is not intended to replace humans but rather augment their capabilities, arming them with unprecedented insights they can use alongside their own experience and judgment for the best outcomes.
Why Do We Want AI?
With so many considerations, it raises the question: What exactly can AI do for risk management that is so great? A lot! So much, in fact, that the technology can no longer be ignored. Here are some of the biggest and most successful active use cases we see:
- Claims processing: AI is being used to automate tasks like reviewing medical records and legal documents, streamlining the claims process, and reducing errors. Not only is it making claims adjusters more efficient, but it is also removing the drudgery and manual processes of their work so they can concentrate on what matters most.
- Fraud detection: We all know fraud is a big problem in claims. AI algorithms can analyze historical data to identify patterns that spot potentially fraudulent items, alerting adjusters and triggering a human review. Identifying and eliminating fraud, especially early in the claims process, can save organizations millions of dollars each year.
- Reserving: On the flip side, some claims simply require more. AI systems can predict potential cost overruns for these complex cases. This allows risk managers to adjust reserves and prioritize cases accordingly so claims don’t run up any more or last any longer than necessary.
- Underwriting: AI can also be leveraged to analyze vast amounts of patient data — far more than a human could possibly consume — to create more accurate risk profiles almost instantly. With better risk profiles, costs go down. This leads to fairer and more competitive pricing for healthcare policies, which is, in turn, an enormous benefit to patients.
- Knowledge transfer: It’s no secret that risk managers and adjusters are aging out. There will be a massive talent drain when they retire, taking institutional knowledge with them. AI-powered tools can capture their expertise, preserving that invaluable knowledge for future generations.
See also: Cautionary Tales on AI
The Road Ahead
Embracing AI presents an opportunity to advance our industry through improved efficiency, reduced costs, and ultimately, better patient care. These outcomes are worth the effort spent to overcome challenges. The time is right to move the industry forward and to usher in a modern era of risk management.
