Dan Schuleman is Co-Founder and CEO of Qumis, an AI-powered knowledge platform built by insurance coverage experts to supercharge complex claims workflows. Dan was interviewed by Andrew Daniels, Co-Founder and President at CrashBay and Founder and Managing Director at InsurTech Ohio.
Can you share the pain points of working in legal and the current state of coverage analysis in claims and how it's adjudicated?
“Reflecting on my past as a coverage attorney, it's not just the legal aspect but also the work done by claims adjusters and teams in commercial liability lines. Their daily tasks are akin to practicing law because they interpret insurance policies—essentially contracts—against a set of facts and make decisions based on that. While they consult legal counsel for specific legal questions, the expertise required is quite similar. The main pain point lies in the outdated, paper-based processes that have been in place for decades.
The necessity to match facts to an insurance policy in the context of a claim has existed since the inception of insurance. Unfortunately, innovation has been slow to penetrate claims processing. Traditional paper-based processes lead to manual workflows, which rely heavily on individual expertise and knowledge confined to documents on a desktop. This often involves manually using Microsoft Word to type, copy and paste from other documents and boilerplates.
If issues arise, consulting legal counsel can cause significant delays. Many companies rely on email blasts to seek advice, further slowing down processes and reducing precision. This can also result in substantial risks and adverse outcomes for insurance companies. Legal frameworks often work against insurance companies, and consumer protection laws can impose liabilities exceeding policy limits if companies make mistakes—worst-case scenarios that I’ve witnessed firsthand.
For instance, I once litigated a case involving roof damage at a commercial property. The adjuster failed to send the coverage letter on time, violating a statutory requirement. This oversight led to a bad faith claim, resulting in significant liability for the insurance company. Claims adjusters are often overwhelmed, and even minor errors can lead to millions of dollars in liability. The reliance on individual expertise creates manual, unsophisticated operations with high stakes. That’s why we're developing solutions to address these issues.”
What are your thoughts on the use of AI and large language models in this space?
“Large language models (LLMs) are particularly well-suited for comparing facts and performing first-level reasoning, which is essential in interpreting insurance policies. These policies are designed to respond to specific facts, making them ideal for LLMs. For instance, if a policy covers an occurrence, and an occurrence is defined in certain terms, LLMs can efficiently interpret this.
We’re leveraging LLMs to create models using our data sources, differentiating our product. Additionally, we collaborate with attorneys to benchmark results and ensure accuracy. Our goal is to assist claims adjusters by automating initial analysis and preparing summaries, similar to how a law clerk would assist. While our solution isn’t a replacement for human expertise, it significantly reduces time spent on manual tasks, allowing adjusters to focus on higher-level decision-making.
Our product also includes built-in citations, enabling users to verify information easily. By combining LLMs with human expertise, we aim to enhance accuracy and prevent errors that could lead to significant liabilities. This approach ensures trust and reliability in our product, crucial for adjusters who are experts in their field.”
What's your take on the impact of folks working in adjusting and claims adjudications?
“While there is some apprehension, I am optimistic. Complex claims will always require expertise, and our products are designed to complement, not replace, human skills. The industry is experiencing its ‘spreadsheet moment,’ similar to how Excel revolutionized accounting. Initially, accountants feared redundancy, but ultimately, Excel enhanced their roles, allowing them to perform more sophisticated tasks.
In insurance, we anticipate a similar shift. By automating tedious tasks, we can elevate the profession, enabling deeper understanding and more nuanced analysis of insurance policies. This transformation will lead to greater demand for expertise and more sophisticated work, enhancing the entire industry.”
Can you give us an overview of how you see this space changing over the next few years and the impact of those changes?
“Our mission is to accelerate the business of insurance by optimizing back-office processes in claims and brokering. We aim to eliminate manual work, allowing professionals to focus on strategic, high-level thinking. Just as accounting evolved with tools like Excel, insurance will benefit from automation and AI, enabling better decision-making and strategic analysis.
As these tools eliminate tedious tasks, professionals will have more time to think critically, leading to a fundamental enhancement of the commercial insurance industry. The shift towards strategic thinking will elevate the entire field, making it more efficient and effective.”
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