You’ve seen it happen before. New entrants arrive and disrupt whole industries that once seemed impervious to change. Google and Amazon jump to mind, but let’s not forget how Progressive disrupted auto insurance by adding a new data source—actual driving behavior—and analyzing its associated risks. While personal auto insurance led the analytics transformation, commercial insurers are successfully applying big data and advanced analytics to underwriting and claims management for small- and medium-sized enterprises.
Medical professional liability (MPL) insurance has fallen behind other commercial insurance lines in leveraging data, analytics, and technology to improve underwriting efficiency, rating accuracy, and customer experience. In addition to lagging analytics, challenges include rapidly evolving medical care and escalating settlements. These factors lead to a lack of profitability within the industry. AM Best forecasts that underlying challenges will persist throughout 2024 despite some rate hardening.
The industry’s struggles have not gone unnoticed by investors. An emerging threat to profitability is growing competitive pressure as new entrants bring advanced analytics to the market. One example of a new entrant to the market is Indigo, which is backed by large venture capital firms. Indigo differentiates itself from traditional MPL insurers through an AI-powered underwriting process that it promotes as lowering costs and providing individual pricing.
Drawing on lessons learned in the successful transformation of P&C insurance, a potential path forward exists for the industry to leverage data and advanced analytics, focusing on AI. This approach includes understanding the value-adds of AI, determining use cases for AI and big data, and finally, implementing a more personal, physician-activity-based approach to underwriting.
Medical liability insurance is well-positioned for success. Industry rating agencies, including AM Best, cite the industry's strong capital position. In addition, the industry boasts experienced and talented human capital. New entrants underscore that the industry is attractive and ripe for change. It’s time to roll up our sleeves and make it happen.
Identifying AI Value-Add
Leading insurers have been applying deep learning and numbers-based AI to develop insights for more than five years. According to the latest report from ReSource Pro on 2024 AI investments and strategies for commercial lines, commercial carriers’ top three high-value use cases for AI focus on the underwriting function: analysis, submission process, and triage/prioritization. What’s new is the application of generative AI to augment human decisions.
Leveraging Advanced Analytics and AI
The most effective use of advanced analytics, including AI, begins with a pressing customer need. For insurers, the driving need could be a more accurate rating, improved underwriting results, or actionable risk management. Like the broader insurance industry, MPL presents unique and sustainable opportunities, or use cases, to apply generative AI. The starting point for an effective use case is pinpointing an issue that can be defined as a problem to solve that has measurable results.
Leading consultants, including Deloitte, BCG, and McKinsey, recommend a proven roadmap to succeed with generative AI:
- Start with a clear technology strategy and AI-friendly talent.
- Engage your ecosystem of advisers and partners to create a first-mover advantage and experiment with new solutions.
- Identify use cases that build value through improved productivity, growth, and new business models.
- Prioritize use cases that extend your competitive advantage by targeting areas of the organization that will benefit the most from automation. It’s okay to begin by launching smaller-scale use cases.
- Build the capability to estimate generative AI’s true costs and returns by measuring results for generative AI use cases.
Focusing on the Customer Use Case
There’s an emerging consensus that use cases are critical to a successful generative AI roadmap. Global technology leader Amazon Web Services is investing heavily in generative AI to avoid being commoditized as a platform provider. According to AWS executive Ruba Borno, the one thing that will make generative AI “move from hype and interesting right now to fascinating and relevant is the customer use case.”
To optimize generative AI in your organization through use cases, start by considering these questions:
- Where is the risk in my provider portfolio?
- How does it compare to other healthcare systems and physician specialties in the region…the state…and the country?
- Working together, which risks can we help prevent from happening in the first place?
Essentially, by analyzing physician activity and its associated risk, you get a much more reliable indicator of potential risk in a similar way to how tracking driving behavior in personal auto policies indicates potential risk.
A Final Word
The competitive advantages for early adopters are well documented. Insurance leaders in commercial and personal lines have demonstrated the value of early adoption of advanced analytics: better risk selection, outcomes, and reinsurance terms.
Adopting advanced analytics will accelerate as new entrants challenge MPL underwriting. Incumbent providers can’t afford to wait on the sidelines—it’s time to lead the way.