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Today’s LLM-based AI solutions boast powerful capabilities that just three years ago were only found in science fiction. Modern AIs, driven by advances in machine learning & computational methods inspired by the human brain, continuously gain new capabilities from the data they encounter, enabling them with previously unattainable potential.

However, when it comes to operating within complex, highly regulated sectors like insurance, not any ol’ AI solution will do. In this post, I want to explore why carriers are turning to a new generation of vertical AIs purpose-built to address the industry’s unique needs and challenges.

Horizontal solutions only leverage the Internet’s surface

“Horizontal” LLM-based chatbots (e.g., Open AI’s ChatGPT or Anthropic’s Claude) are competent at a wide range of tasks, but you’d never trust them to execute a consequential insurance underwriting workflow.

Well, I mean, you could. But your underwriters would still need to engage in dozens (or even hundreds) of rounds of prompts, follow-ups, and clarifications to surface the information they need—all of which would require close review & scrutinization for accuracy, compliance, and hallucinations. They'd need to invest time in sorting through pages of answers to find important facts, correlate & de-dupe information, build timelines, and draw relevant connections. After which, they'd have to relate all of these processed facts back to their risk appetite to evaluate the quality of the risk.

Horizontal, multi-use AI solutions deliver little—if any—operational efficiencies for a complex enterprise use case like underwriting. The industry needs something more from its AI.

How vertical solutions overcome the data dilemma

One key area where general-purpose LLM chatbots and wrappers crucially fall short is lack of access to specialized data. A LLM’s “knowledge” can only run as deep as the data it’s been trained on. With a horizontal solution, you’ve generally been limited by what’s publicly available online. To echo a common observation: these models offer “the average of the internet.” They might be perfectly helpful in, say, planning out a keto-friendly dinner for two, but much less so when it comes to assessing risk signals on insurance applications.

In order to be useful in underwriting, a generative AI solution must have been — as table stakes —trained on informative but isolated datasets such as loss histories. Even anonymized versions of these datasets aren’t available for AI training purposes (they can’t even be purchased).

To access this invaluable cloistered data, an AI vendor must cultivate relationships with specialized data gatekeepers and arrive at a precise alignment on use and security. It’d be impractical for a horizontal AI provider to address every possible enterprise niche. To pry these data doors open, you need highly specialized vendors with a singular industry focus.

Vertical solutions: a partnership of insurance nerds and tech geeks

Beyond special data access, a vertical AI solution is designed to address the highly specific needs of its sector. The complexity and regulations inherent to insurance underwriting require a team that is as well-versed in emerging tech as they are in long standing carrier challenges. 

A vertical AI solution likely incorporates a medley of intelligent tools under a single platform umbrella. A foundational LLM, for example, may be tapped for specific functions (e.g., summarization), but higher-level capabilities can only be achieved when the LLM is partnered with purpose-built functionality dedicated to specific tasks (e.g., external data APIs, vector stores, etc.) The solution’s precise structure must be guided by experts with an intimate understanding of today’s industry challenges—and an eye on the ones soon to be in effect.

Keep your eye to the verizon

Horizontal AI solutions are amazing, but they fall short in core underwriting tasks due to their shallow expansiveness; lack of access to specialized data; and ultimately the fact that they’re just one building block that must be partnered with industry-specific capabilities to deliver value to carriers. 

This article was originally posted on LinkedIn

We had an opportunity to chat with Sixfold's Co-founder and COO Jane Tran about the company’s amazing first year and the vision for the years to come, as well as her career journey, giving back, and tips on running a fast-paced AI startup.

What was your first job and how did that influence your career?

My very first job ever was as a cashier at an Italian deli around the corner from my parents’ house.

I think everyone should do a service job because it emphasizes the importance of good customer service like how to treat people. I also learned how to be quick because it's New York. New Yorkers don’t like to wait for their Bacon, Egg, and Cheese.

Walk us through your career journey from the Italian deli to co-founder and COO at  Sixfold.

I’ve had a good mix of enterprise and startup experience. I started my career at JP Morgan as part of their rotational analyst program. One of my last rotations was with the Turnaround & Process Improvement team for the Chief Information Officer—that’s where I fell in love with tech. I worked on really cool projects like improving the eDiscovery process and improving data governance. I had a blast. 

After that, I spent several years at Marsh and MetLife working with the CIOs on different strategy and planning projects before I decided to give startups to go. I was on the founding team at Unqork where I was Head of Solutions before becoming COO.

When I decided to leave Unqork, I kept in contact with Alex [Schmelkin, founding team at Unqork, and co-founder of Sixfold]. When he came up with the idea for what would become Sixfold, he asked me to join him to get this idea off the ground.

What does the name Sixfold mean and who came up with it?

So, all kudos due to Alex’s daughter Nina Schmelkin for that! She was doing a project for school around patterns. A sixfold pattern is considered one of the most interesting, naturally occurring patterns—snowflakes are a sixfold pattern. And so, when it came to choosing a name for the company, we were thinking about AI and the role that patterns play, and “Sixfold” seemed like an ideal fit.

Sixfold just turned one year old. How would you describe year one?

A ton of fun! We’re building really tangible use cases using cutting-edge tech. This first year has reinforced the importance of anchoring your work in first principles. AI is obviously super hot and evolving at warp speed, but we can't ignore the things that support great software development and great user experiences. That meant getting that foundation and discipline in place while at the same time making room for extensive R&D and a ton of iterations. 

We learned a lot. We tweaked a bunch. And I think we found product market fit. Our early customers are already starting to see value, and I’m really excited to see where that grows this year.

Do you have any notable “wow” moments from the first year?

Yeah, when we delivered our first end-to-end underwriting pilots and heard the underwriters say “we were able to complete this task in a fifth of the time.” I particularly love hearing how much they trust the tool.

Hearing that first positive user feedback feels like a major achievement! And, obviously, the recent closing of our Series A funding round.

Who are your role models and how did they influence your career? 

My parents. My mom runs a small business in kitchen supplies with her siblings — it was one of those things where they just sort of fell into it. They knew they could offer a really good product and create a fit within the market. They understood what customers wanted and knew that they could manufacture it. So they just went for it. That takes a lot of bravery. On the flip side, my dad hung wallpaper for a living. He's retired now, but he had that hard work ethic and true care for his craft. He developed a reputation for excellence and really worked his way up.

I think the combination of entrepreneurship, work ethic, and quality very much influences who I am.

A common conversation for startups is balancing “the need for speed” with employee happiness. How do you build that balance into the company culture? 

Unfortunately, I don't have a magic formula. I would say that from the get-go, Sixfold’s three founders — Brian, Alex, and myself — anchored ourselves on our Mission and a handful of operating principles like putting the customer first and being direct while being kind. We try to surround ourselves with people who share those principles so that naturally becomes the culture of the company. 

The three of us really care about who works for us and how we all work together. Sometimes we may not have the best balance, but we always strive to be better. As founders, we care a lot about our work, but we also care deeply about family, friends, and life outside work. We understand that people who work with us have the same need for a balanced life outside work.

Do you have any tips when it comes to hiring?

Instinct is a huge part of it. It’s also helpful to have a great HR team in place — kudos to Marie [Sixfold’s HR Business Partner] for doing a lot of the initial groundwork, so by the time a candidate gets to me, they fit a lot of our criteria for that role. From there, a lot of it just comes down to just instinct. Ask yourself if they'll fit within the culture of this company.

Tell us about your mentoring work for different startups and organizations.

I'm on the board of directors and co-chair for an organization called Womankind. It helps survivors of gender-based violence in New York, with a focus on the AAPI community. They've been around for more than 40 years. They started with a single hotline for the NYC area, but have expanded to serve thousands of women every year across the US as one of the few true end-to-end organizations. So families get to stay together. They get legal help. They get job placement. Their kids have a safe place to be while they're figuring out, you know, the next steps. I'm really proud to be part of that organization. 

I've also mentored and advised a few other early-stage startups that I think are doing something new and interesting. This also helps me to understand what else is out there in the ecosystem. I’ve mostly been focused on B2B enterprise throughout my career, so I like learning about retail or other sectors.

I love being around people who are building interesting things. It’s super fun and it can be super informational too.

What are your top work tools that you feel like you couldn’t do without?

I don't do a lot of the things that “productivity hackers” do. I would say Apple Notes and Google Tasks are central to my workday. I keep extensive notes on my meetings— so that’s a lot of Apple notes. And then, for the important things with a deadline, I'll set up a Google task or calendar reminder. And that's how I organize.

What are you excited about for Sixfold’s second year? 

This year will be about continuing to mature our product and getting a lot of new customer use cases live.

We're working with a lot of great people and a lot of great customers. I’m looking forward to showcasing what we can do within this market and at this fidelity — a year ago, I don't think anyone would have thought that we’d be able to do what we’re doing, so I can’t wait to see what the next year or two will bring!

Want to work with Jane and the rest of the Sixfold team? Check out our career page. 

I’m thrilled to announce that Sixfold has been named a winner of this year’s Zurich Innovation Championship! Since 2018, Zurich Insurance Group has overseen an annual “collaboration program” with select startups from around the world to develop new offers and services for their customers. The yearly Championship has rapidly expanded to become the industry’s largest open innovation contest with thousands of applicants from over 30 countries resulting in more than 50 active initiatives implemented through Zurich Insurance’s global business. 

‍Celebrated for Pioneering Innovation

Sixfold was only one of nine teams selected out of a pool of more than 3,000 global applicants to take part in the accelerator portion of the Championship. Our team will take on the “Commercial Insurance” challenge to engineer technical solutions that “improve transparency and accountability, enhance risk management capabilities, and foster sustainability transition through a culture of trust and innovation.”

For the next four months, our team will collaborate with leaders from Zurich North America (ZNA) to build AI-powered risk analysis & summarization solutions to augment underwriter workflows through the automation of high-volume (but not necessarily high-value) tasks.

Improving Insurance Efficiencies

Unlike traditional accelerator programs, which focus on product development, the Innovation Championship aims to accelerate the adoption of a new solution within Zurich Insurance’s global business. We’re thankful to the leadership at Zurich Insurance for believing in Sixfold’s mission and our team, and we look forward to building an amazing solution that results in improved efficiencies that benefit Zurich’s customers, insurance brokers, and other stakeholders. 

This collaboration comes only two months after Sixfold’s selection to participate in the 12th cohort of Lloyd’s exclusive accelerator program, Lloyd’s Labs, which kicked off in April and we will be demoing in July. 

We’ve only just begun Sixfold’s second year — and it’s turning out to be a BIG one!

This article was originally posted on Linkedin

We talked with Sixfold’s latest hire, Head of AI/ML Ian P. Cook, PhD about his career journey, how emerging technology will overcome long-standing industry challenges, and his new role as Sixfold’s data science leader.

Welcome to the team, Ian! Walk us through your career journey up to this point.

I caught the bug for quantitative work when I was in grad school studying Public Policy at the University of Chicago. After graduation, I worked in various policy analysis roles, including at the RAND Corporation as well as doing work for all the major defense agencies and other federal orgs.

While doing policy work, I was simultaneously pursuing my PhD at the University of Pittsburgh for Political Science. My trusty “dad joke” is that I wasn’t smart enough to do grad school just once. As part of my research, I taught myself Python and found that my skills in econometrics translated well to the then-exploding field of data science. After I received my degree, I worked with startups and went from building tech products to building tech teams as Chief Data Scientist with a GovTech company and Chief Technical Officer for a business analytics SaaS.

Are there any tech projects you’re particularly proud of?  

One of my favorite projects was a matching & recommendation tool for patients. A significant predictor of poor health outcomes is missing doctor appointments, but as it turns out people don’t just “miss” them, they avoid them when they’re not happy with the style or approach of a doctor or practice. This was a problem that me and my team believed could be engineered around. 

I oversaw the team building the machine learning functionality for a web tool that assessed both patient preferences and the style of healthcare providers and then turned that into a kind of Match.com for healthcare. Not only was it fun to build, but I’m particularly proud to know that it helped keep people getting the care they need.

Why Sixfold?

Sixfold immediately piqued my interest. The company is attacking a clear and sizable pain point for a well-defined customer (anyone with startup experience will tell you that’s not always the case). Plus, they’re doing it with what I see as generation-defining tech—LLMs are amazing in their own right, and having the opportunity to put them to practical use is an exciting opportunity. After meeting with the team and the leadership, I knew that this was where I wanted the next step of my career to be. Excited to get to work with an amazing crew—tip of the hat to Stewart, Drew, and the whole engineering team!

Everyone’s talking about LLM-powered generative AI these days. From your perspective, what are the most intriguing possibilities and potential risks of this emerging generation of tech?

I sit somewhere in the middle between the extremes of the AI discourse: I don’t think AI will give rise to a post-human apocalypse, but I also don’t believe we can just sit back and toss every hard problem at an AI and implement whatever solution pops out. 

We’re going to see these tools accelerate transformation across every industry. In most cases, that will ultimately be a good thing. However, without clear intention behind how they’re applied and oversight into how they’re trained and deployed, there’s a real risk for these tools to cause harm—unintentional or otherwise. Part of the attraction to Sixfold was their emphasis on applying AI responsibly.

As someone with a strong data science background, what does it mean for data to be useful, not just accessible?

Access is an aspect of keeping data well-controlled—like security measures and access control for personally identifiable information, health records, financial information, and other sensitive material. 

For data to be useful, it has to address real problems and it has to have been corralled in a thoughtful, purposeful manner. Usefulness requires someone to understand the question the data is meant to help answer and to be aware of potential biases—both statistical and human-generated—which might limit the applicability of the data.

How do you see your role as the AI/ML leader at Sixfold?

I see my chief responsibility as empowering underwriters to do their best work ever by augmenting Sixfold’s product with AI-powered tools. Achieving that means supporting the people who are developing, testing, and deploying those tools. Some days that might mean coordinating priorities and ensuring everyone has the information and resources to deliver. Some days it might mean being chest-deep in the code myself. And some days, I’m sure it’ll be a little of both. 

What do you see as the challenges of implementing AI in insurance vs other industries?

I’ll admit to being relatively new to the insurance industry. That said, even a n00b like myself understands that it comes with unique challenges like complying with regulations across multiple levels of government; implementing stringent processes to handle and distribute the personal, closely-held data of both individuals and corporations; and making a convincing argument for change in a well-established industry where many are content using tools and methods that’ve been around for decades.

As a seasoned technologist, do you think there are types of tasks that will always be better suited for humans, rather than machines? 

AI is going to take on the tasks that slow us down. I like to think of it as a bionic-like tool that augments and improves human performance.

It’ll free us up to focus on the most important—and frankly most meaningful and rewarding—parts of our jobs.

I’ll also add this: the better the machines get, the more we’re going to lean on philosophy, the most thoroughly human of disciplines. Discussions about LLMs are loaded with terms like “reasoning,” “thought,” and “knowledge,” which philosophers have been wrestling with for centuries. I’m reminded of the discourse in my philosophy courses around intention and will, which are completely distinct from the mechanistic processes in deep learning architectures. Philosophy is often derided as a field with little practicality, but as a technologist, I see it becoming more practical by the day.

How do you keep up with the latest developments in your field?

A lot of reading! There are tons of great newsletters that cover the field. Ben’s Bites is fantastic, but there are tons of great ones across Medium, Substack, and Beehiiv. I’m also a fan of podcasts like AI Daily Brief, The Cognitive Revolution, and Talking Machines. I like listening to those while doing chores, walking my dog, driving, etc. 

Keeping up with the latest research is always a challenge—it was an issue even back in my PhD days because there were always new papers coming out. Part of me feels lucky to have gone through that back then because now AI is moving at warp speed and it’s even harder to keep up. But I’ve learned to master the art of “informed skimming,” which means quickly reviewing summaries, conclusions, and writeups to find key terms that will tell you if the paper is relevant to the problem area you’re taking on.

What tools do you rely on the most for your work?

I’m a devoted fan of Pycharm for coding. I’ve tried switching to VS Code and other flashy new IDEs when they come up, but I always go back to Pycharm. For note-taking, I stick to Apple Notes. There’s a whole world of “second brain”/knowledge management tools out there for taking notes, but I have to refrain from those (anyone familiar with the word “Zettelkasten” knows the depths of that particular rabbit hole).

I learned during grad school that the more extensible a tool is, the more time I waste fiddling with configurations. Tweaking color themes is not “optimizing my workflow,” no matter how many times I repeat it to myself.

What is the best thing a person can do who wants to pursue a career in AI/ML?

Some requirements are hard to skip over: a decent amount of math, and enough knowledge to turn that math into code.

But you don’t have to be a genius at either one. Learn some matrix math, and then play with those matrices and see how you can apply them in predictive software. Then learn a little more, and implement a little more. The key to mastering any skill set is repetition and perseverance. To parrot that old quote about how one becomes a writer: write.

More specifically, I think there are three things everyone who wants to work in this field needs to know: SQL, Git, and enough of one programming language to be productive. SQL is the language of data: getting it, moving it, storing it, everything—if you can’t get at the data, it’s going to be hard to trust that you can work with it. Git proves that you understand versioning, reproducibility, and collaboration. When it comes to becoming thoroughly fluent in at least one programming language, I’ll sidestep the religious wars about which language is best or most useful and just say that the important thing is becoming really productive in at least one language.

Any fun tech projects that you're working on at the moment (non-Sixfold-related)?

I’m a fly fisher in my spare time, and I use a fly fishing app called onWater Fish. I reached out to the app’s dev team and learned they needed some ML-like support. So I pitched in to try out some new ideas. We’ve been successful in implementing some cool in-app computer vision work that anglers can use to record their catches (and brag to friends) all with one picture. It’s been a great way to apply my skills to a personal passion of mine, which has been truly rewarding. 

How can people follow your work?

I’m on LinkedIn, and try to post regularly on the practical application of AI, the future of work, and whatever else where I might have a useful take. For other social media, I can usually be found by searching @ianpcook.

Want to join Ian and the rest of the Sixfold team on our mission to transform insurance underwriting with AI? Check out our career page. 

Last month, the European Parliament passed the EU Artificial Intelligence Act, a sweeping regulatory framework scheduled to go into effect in 2025.

The Act categorizes AI systems into four risk tiers—Unacceptable, High, Limited, and Minimal—based on the sensitivity of the data the systems handle and the crucialness of the use case.

It specifically carves out guidelines for AI in insurance, placing “AI systems intended to be used for risk assessment and pricing in [...] life and health insurance” in the “High-risk” tier, which means they must continually satisfy specific conditions around security, transparency, auditability, and human oversight. 

The Act’s passage is reflective of an emerging acknowledgment that AI must be paired with rules guiding its impact and development—and it's far from just an EU thing. Last week, the UK and the US signed a first-of-its-kind bilateral agreement to develop “robust” methods for evaluating the safety of AI tools and the systems that underpin them. 

I fully expect to see additional frameworks following the EU, UK, and US’s lead, particularly within vital sectors such as life insurance. Safety, governance, and transparency are no longer lofty, optional aspirations for AI providers, they are inherent—and increasingly enforceable—facets of the emerging business landscape.

Please be skeptical of your tech vendors

When a carrier integrates a vendor into their tech stack, they’re outsourcing a certain amount of risk management to that vendor. That’s no small responsibility and one we at Sixfold take very seriously. 

We’ve taken on the continuous work of keeping our technology compliant with evolving rules and expectations, so you don’t have to. That message, I’ve found, doesn’t always land immediately. Tech leaders have an inherent “filter” for vendor claims that is appropriate and understandable (I too have years of experience overseeing sprawling enterprise tech stacks and attempting to separate marketing from “the meat”). We expect—indeed, we want—customers to question our claims and check our work. As my co-founder and COO Jane Tran put it during a panel discussion at ITI EU 2024:

“As a carrier, you should be skeptical towards new technology solutions. Our work as a vendor is to make you confident that we have thought about all the risks for you already.” 

Today, confidence-building has extended to ensuring customers and partners that our platform complies with emerging AI rules around the world—including ones that are still being written.

Balancing AI underwriting and transparency 

When we launched last year, there was lots of buzz about the potential of AI, along with lots of talk about its potential downside. We didn’t need to hire pricey consultants to know that AI regulations would be coming soon. 

Early on, we actively engaged with US regulators to understand their thinking and offer our insights to them as AI experts. From these conversations, we learned that the chief issue was the scaling out of bias and the impact of AI hallucinations on consequential decisions.

With these concerns in mind, we proactively designed our platform with baked-in transparency to mitigate the influence of human bias, while also installing mechanisms to eliminate hallucinations and elevate privacy. Each Sixfold customer operates within an isolated, single-tenant environment, and end-user data is never persisted in the LLM-powered Gen AI layer so information remains protected and secure. We were implementing enterprise AI guardrails before it was cool.

I’ve often found customers and prospects are surprised when I share with them how prepared our platform is for the evolving patchwork of global AI regulations. I’m not sure what their conversations with other companies are like, but I sense the relief when they learn how Sixfold was built from the get-go to comply with the new way of things–even before they were a thing.

I’m beyond excited to announce that Sixfold has been officially selected to take part in the 12th cohort of Lloyd’s InsurTech accelerator program, Lloyd’s Lab. 

Lloyd’s Lab was recently recognized as a top-25 European start-up hub by the Financial Times and Statista and ranked the very top insurance-focused accelerator out of 19 countries and +2,000 organizations.

The program will give our team the opportunity to collaborate with Lloyd’s mentors to develop innovative solutions for the world’s leading insurance and reinsurance marketplace. Over the course of the 10-week “fast-track, fast fail” program kicking off in late April, our team will build, test, and iterate innovative solutions that “challenge how we do things and help the Lloyd’s market better serve its customers.”

Sixfold was one of just 22 insurtechs invited to travel to London to take part in Lloyd’s “Pitch Day” event. This year saw the largest-ever application pool for the program—Pitch Day invitees had to be culled down from more than 250 applications submitted by insurtechs spanning 33 different counties. 

For our pitch, I showcased how much we have built to serve underwriters in the past 10 months to 1,000-plus virtual and in-person attendees from across the Lloyd’s market ecosystem. We were selected into the program as one of only 12 teams by a panel of market leaders, mentors, and the Lab team, and we were the only underwriting solution to be accepted into the program! 

Members of Sixfold’s product and design teams will spend 10 weeks working out of the iconic Lloyd’s building in London. We will focus our efforts on accelerating and optimizing triage and risk appetite match capabilities within the scale of Lloyd’s markets. 

We’ll demo the fruits of our accelerator labor to the entire Lloyd’s market in early July—stay tuned for details!

Lloyd’s Lab unique approach to fostering global innovation is helping to tackle some of the world’s biggest insurance challenges. Progressing the Lab’s mission of supporting innovative insurance solutions across the globe, this latest cohort focuses on developing solutions to some of the biggest risks faced by businesses and communities in the Americas such as challenges arising from natural hazard prediction to risks associated with cybersecurity.

Thank you to Lloyd’s for believing in our vision and helping us bring it to fruition. We can’t wait to show the entire Lloyd’s market—and the rest of the world—what we have in store.

This article was originally posted on Linkedin