AI: at the Center of CLaiMS

Stephen Applebaum and Alan Demers?//?November 30, 2023

Exactly one year ago today, ChatGPT was officially introduced, sending the business world, pundits and laypersons alike into a frenzy.

It’s truly an understatement considering how many aspects of work, life and business are projected to be at risk or at a minimum, will be redefined to some degree. Conversations are nonstop about the incredible potential vs. man’s existential demise as AI is now considered much closer to parity with human thinking than previously thought possible. What happens once AI catches up to then surpasses human thinking is hard to fully imagine.

At a high-level AI encompasses; Machine Learning (ML), Deep Learning, Generative AI, Large Language Models (LLM) and the current favorite Generative Pre-Trained Transformer (GPT). For purposes of this article, we will not attempt to explain these further.

AI in Insurance

Insurance is no exception as new technology providers are sprouting up, or rather more commonly, solution providers are highlighting and underscoring their existing AI capabilities. ?The AI vendor community to the P&C industry is rapidly expanding and may generally be grouped by use case; Hyperautomation, Insights, Image and Language.

Some have been at the AI game for longer and may even fortuitously have “AI” in their brand identity. Others have been quick to point out their work has been surrounded by AI for years touting both expertise and subject knowledge. ?Even insurers themselves are experimenting, setting up AI safe zones, establishing so-called red and blue ocean strategies or simply creating AI best practices as a foundational starting point. It is doubtful that any insurance carrier board of directors or C-suites have not set some AI work in motion. And finally, insurance regulators are attempting to get ahead of things with proposed AI ethical standards but in reality, are in catch up mode.

The InsurTech movement has been eye-opening in so many different ways, whether raising the competition bar, a realization that innovating insurance is plain difficult or simply reassessing start-up valuations applying today’s hindsight perspective. ?Still, the insurance industry is in a better position because of the InsurTech wave and at the same time there are lessons learned. Famously, “technology in search of a problem” rarely is a successful approach. ?Carriers tend to think in terms of ROI and sound business principles when it comes to advancing technology projects. ?Those with the better ROI get prioritized higher. ?And, to have an ROI business case there must be a well-reasoned use-case better stated as a clear and compelling articulation of a real problem to be measurably solved. So far, generative AI for insurance feels more like as a technology seeking problems to solve.

In many ways, AI has been?literally?generalized into two letters since there are few AI experts with extensive knowledge and lots of business people with just basic AI knowledge. ?Consequently, components like machine learning, computer vision, large language models, and generative AI can easily get mashed up together. ?Fortunately, the experts are openly explaining the differences and providing the details on how this all works and the webinars and conference events are helping the cause. In the meantime, such AI expertise shortages only complicate insurers’ vision for clear use-cases, business purposes and ultimately ROI outcomes.

One of the unanimous AI forecasts is the obvious impact to employees and jobs. ?While insurance automation is not new, the prospects of applying AI to partially or completely replace humans quickly gained attention as an opportunity and better seen as a threat. ?More recently, these views have been tempered with the idea that AI will be better applied as a “co-pilot” for most insurance functions within underwriting, pricing, claims, sales and possibly others. Perhaps making things more palatable or a recognition of use-case immaturity but also at the risk of limiting AI’s tremendous power. This is not a knock on the co-pilot approach, rather an acknowledgement that the industry is in the crawl stage of?crawl, walk, run.

?

AI for Claims

Conventional wisdom is that AI lacks human emotion and empathy which are essential, especially among insurance customer and other people interactions. Claims might be the most human emotion demanding so the AI use-cases talked about today tend to call for AI tools aiding claim adjusters rather than doing the whole job. ?However, it is important to state that all of this chatter is still early-on and short-term minded. ?At the end of the day, ROI still dominates decision making and given the highly competitive P&C Insurance market fraught with current financial pressures, the balance between deployment of tools and automation of jobs will be put to a new and more rigorous tests.

AI for underwriting and claims emerge as the top use areas which makes sense. Large amounts of data are used to assess and price risk and similarly claims is all about gathering information and decisioning. ?Similarly, both functions are people based and are already pursuing automation agendas like low-touch and straight-through-processing.

Within the claim space, much of the Generative AI talk is heavily weighted around reviewing and summarizing records, such as medical billing or a demand package. ?The overarching wisdom is that claim handling is record and paper intensive. A common misnomer is that all claims are alike. ?This is further crystalized from claims insiders’ famous saying; “a claim is a claim” but is also misleading when applied broadly. Instead, high frequency/low severity claims differ greatly from the most complex claims that happen infrequently. ?Some 70 percent of auto claims, for example have minor to modest damage and no or only minor injuries with few documents to summarize.

Where Can/Should AI Be Applied Today?

The good news is that AI in claims is already successfully being embraced. Computer vision for total loss prediction and photo estimating is far-reaching. AI fraud models are helping carriers scan and alert anomalies for investigation. However, there is lots of skepticism and caution ahead. Even within the best AI claims examples there is a long way to go to reaching meaningful ROI. Likewise, trepidation around fairness, legal and regulatory pressures and data security when training models are valid concerns. Even so, there is room for more creative use-case thinking and the following is a small sample of possibilities.

Sort of an AI use-case wish list, free from prioritization and not exhaustive but widely appealing since these are among the most repetitive and demanding claim functions;

• Claim intake for assignment accuracy, reducing or eliminating reassignments
• Claim triage
• Fraud detection, especially organized fraud
• Categorization and severity
• Coverage guidance
• Comparative negligence determination – which party(s) are at fault and to what degree?
• Correspondence generation
• Injury and damage evaluation
• Settlement recommendations
• Notes analysis and summarization
• Case reserve and formula reserve setting or reserve portfolio management
• Regulatory compliance; in real-time
• Regulatory reporting; summarize validate, review and report
• Pending Claim management, prediction and prioritization

There certainly are risks to balance when it comes to the degree of co-piloting or people replacing in any use-case. ?While there is excitement for automated and AI powered claim customer services there is a natural dependence upon chatbot acceptance to overcome not to mention room for the connected claim ecosystem to become truly connected and coordinated in order to realize gains.

Moving ahead, carriers will need to apply additional filters when advancing use-cases. ?Generally speaking, InsurTech including AI falls into efficiency gain/expense reduction emphasis by automating process and reducing FTE. The elephant in the room today is insurer profitability from soaring indemnity costs in which there can be far greater influence from loss ratio improvement compared to loss adjustment expenses (LAE). Yet, the P&C industry has over-emphasized LAE reduction because of the simplicity in measuring operating costs.

Insurers will continue to buy vs. build AI through integration partners as a way forward. ?Solution providers will need to move closer to unravel high value use-cases. ?And the open challenge to insurers and AI solution providers is coming together to develop meaningful business cases including loss avoidance, mitigation and payout accuracy beyond efficiency gain.