Sixfold Content

In 2025, the cyber risk landscape is expected to become more complex with increasing threats driven by rising privacy violations, data breaches, the rise of AI, and external factors such as emerging regulations. According to Munich Re, the cyber insurance market has nearly tripled in size over the past five years, with global premiums projected to surpass $20 billion by 2025, up from nearly $15 billion in 2023, as reported by CyberSecurity Dive.

Reflecting the rapid market growth and emerging threats, Sixfold has seen increased demand from specialty insurers in the cyber sector and has successfully brought on several industry leaders as customers.  "In the near future, cyber policies will become as essential as General Liability or Property & Casualty coverage. Given the world we live in, this shift is inevitable. Cyber policies are poised to become the most specific and highly customized policies available" said Jane Tran, Co-founder & COO at Sixfold.

"In the near future, cyber policies will become as essential as General Liability or Property & Casualty coverage. Given the world we live in, this shift is inevitable. Cyber policies are poised to become the most specific and highly customized policies available"

Empowering Underwriters to Quickly Adapt to New Cyber Risks

As cyber risks grow, the pressure on underwriters to assess risks accurately and expedite the case review process continues to increase. Sixfold’s AI solution for cyber insurance addresses these challenges by securely ingesting each insurer’s underwriting guidelines and aggregating all necessary business information to quickly provide recommendations that align with the carrier’s risk appetite. This capability allows insurers to quickly adjust their risk strategies in response to new cyber threats.

“With Sixfold, insurers can synchronize their underwriting guidelines across the board and adapt quickly. For example, when a new malware threat is identified, you can instantly incorporate it into your risk criteria through Sixfold. This ensures that the entire cyber team factors it into their assessments immediately without needing to learn every detail or the threat or spending hours digging for the right information” said Alex Schmelkin, Founder & CEO of Sixfold.

Besides, effective cyber underwriting demands deep expertise in IT systems, cybersecurity measures, and industry developments. This need for specific expertise presents a significant talent issue for insurers, especially with 50% of the underwriting workforce set to retire by 2028. Sixfold bridges the knowledge gap by instantly providing underwriters with the specialized knowledge they need for accurate risk assessments. 

“Underwriters no longer need to be cyber experts; they can rely on Sixfold to spotlight the critical information needed for accurate underwriting decisions. Our platform simplifies the complex world of cyber risk and empowers underwriters to make more confident decisions, faster” said Jane Tran, Co-founder & COO at Sixfold.

Sixfold Partners with CyberCube to Enhance Cyber Risk Assessments

‍Sixfold has teamed up with CyberCube, the world’s leading analytics provider to quantify cyber risk. This integration of CyberCube's advanced cyber risk analytics with Sixfold's AI underwriting solution enables insurers to achieve faster and more accurate risk assessments. The partnership enhances underwriting efficiency, strengthens regulatory compliance, and offers highly tailored cyber insurance solutions, empowering insurers to stay ahead of the rapidly evolving cyber threat landscape. "The partnership between CyberCube’s comprehensive cyber data and Sixfold’s innovative risk assessment is setting a new standard for the future of underwriting, keeping insurers prepared for new challenges in determining accurate cyber policies.” said Ross Wirth, Head of Partnership and Ecosystem for CyberCube.

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"The partnership between CyberCube’s comprehensive cyber data and Sixfold’s innovative risk assessment is setting a new standard for the future of underwriting, keeping insurers prepared for new challenges in determining accurate cyber policies.”
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To see our Sixfold speeds up the cyber underwriting process join our upcoming live product demo.

This content was originally published on PR Web

With the rise of AI solutions in the Insurance market, questions around AI regulations and compliance are increasingly at the forefront. Key questions such as “What happens when we use data in the context of AI?” and “What are the key focus areas in the new regulations?” are top of mind for both consumers and industry leaders.

To address these topics, Sixfold’s founder and CEO, Alex Schmelkin, hosted the webinar “How to Secure Your AI Compliance Team’s Approval”. Joined by industry experts Jason D. Lapham, Deputy Commissioner for P&C Insurance for the State of Colorado, and Matt Kelly, Data Strategy & Security Counsel at Debevoise & Plimpton, the discussion provided essential insights into navigating AI regulations and compliance.

Here are the key insights from the session:

AI Regulation Developments: Colorado Leads the Way in the U.S

“There’s a requirement in almost any regulatory regime to protect consumer data. But now, what happens when we start using that data in AI? Are things different?” — Alex Schmelkin

Both nationally and globally, AI regulations are being implemented. In the U.S., Colorado became the first state to pass a law and implement regulations related to AI in the insurance sector. Jason Lapham explained that the key components of this legislation revolve around two major requirements:

1. Governance and Risk Management Frameworks: Companies must establish robust frameworks to manage the risks associated with AI and predictive models.
2. Quantitative Testing: Businesses must test their AI models to ensure that outcomes generated from non-traditional data sources (e.g., external consumer data) do not lead to unfairly discriminatory results. The legislation also mandates a stakeholder process prior to adopting rules.

Initially, the focus was on life insurance, as it played a critical role in shaping the legislative process. The first regulation, implementing Colorado’s Bill 169, adopted in late 2023, addressed governance and risk management. This regulation applies to life insurers across all practices, and the Regulatory Agency received the first reports this year from companies using predictive models and external consumer data sources.

So, what’s the next move for the first-moving state in terms of AI regulations? Colorado Division of Insurance is developing a framework for quantitative testing to help insurers assess whether their models produce unfairly discriminatory outcomes. Insurers are expected to take action if their models do lead to such outcomes.

Compliance Approach: Develop Governance Programs

“When we’re discussing with clients, we say focus on the operational risk side, and it will get you largely where you need to be for most regulations out there.” — Matt Kelly

With AI regulations differing across U.S. states and globally, companies face challenges. Matt Kelly described how his team at Debevoise & Plimpton navigate these challenges by building a framework that prioritizes managing operational risk related to technology. Their approach involves asking questions such as :

• What AI is being used?
• What risks are associated with its use?
• How is the company governing or mitigating those risks?

By focusing on these questions, companies can develop strong governance programs that align with most regulatory frameworks. Kelly advises clients to center their efforts on addressing operational risks, which takes them a long way toward compliance.

The Four Pillars of AI Compliance 

Across different AI regulatory regimes, four common themes emerge:

1. Transparency and Accountability: Companies must understand and clearly explain their AI processes. Transparency is a universal requirement.
2. Ethical and Fair Usage: Organizations must ensure their AI models do not introduce bias and must be able to demonstrate fairness.
3. Consumer Protection: In all regulatory contexts, protecting consumer data is essential. With AI, this extends to ensuring models do not misuse consumer information.
4. Governance Structure: Insurance companies are responsible for ensuring that they—and any third-party model providers—comply with AI regulations. While third-party providers play a role, carriers are ultimately accountable.

Matt Kelly emphasizes that insurers can navigate these four themes successfully by establishing the right frameworks and governance structures. 

Protection vs. Innovation: Striking the Right Balance 

“We tend not to look at innovation as a risk. We see it as aligned with protecting consumers when managed correctly.” — Matt Kelly

Balancing consumer protection with innovation is crucial for insurers. When done correctly, these goals align. Matt noted that the focus should be on leveraging technology to improve services without compromising consumer rights.

One major concern in insurance is unfair discrimination, particularly in how companies categorize risks using AI and consumer data. Regulators ask whether these categorizations are justified based on coverage or risk pool considerations, or whether they are unfairly based on unrelated characteristics. Aligning these concerns with technological innovation can lead to more accurate and fair coverage decisions while ensuring compliance with regulatory standards.

Want to learn more? 

Watch the full webinar recording and download Sixfold’s Responsible AI framework for Sixfold’s approach to safe AI usage. 

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Companies of all sizes are actively exploring how emerging AI technologies can overcome longstanding business challenges. Inevitably, they run up against the reality that weathered AI pros like myself have long known: AI ain’t easy.  Rather than going it alone, many businesses choose to partner with firms that specialize in building solutions with LLMs. The good news? There are a growing number of AI vendors to pick from, with more popping up all the time. The bad? Discerning if a vendor can deliver what you need isn’t always so straightforward.

It seems like everyone and their little cousin touts the ability to “wrap” custom applications around one of the big-name LLMs. If that’s all they bring to the table, they might help you address simple use cases, but probably won’t have the chops to build and manage complex solutions in heavily regulated industries like insurance. That’s a whole different thing.

So, how can you tell if a prospective vendor can meet your business's needs? In this blog post, I’ll explore some key areas along the AI value chain and propose some questions to ask so you can make an informed decision.

So, how can you tell if a prospective vendor can meet your business's needs? In this blog post, I’ll explore some key areas along the AI value chain and propose some questions to ask so you can make an informed decision.

Input preparation

What you put into your AI system is what you get out of it. Make sure a prospective vendor prioritizes clean data, stored & handled in a secure compliant manner.

You can think of data like a commodity that powered the previous century: oil. You don’t just dig some oil out of the ground and pour it into your gas tank. (Or, I guess you could, but you wouldn’t get far before your engines seized up.) Like oil, data requires multiple rounds of preparation before it can be used. 

The value of the output your AI produces is directly related to the quality of the input. Before moving forward with any prospective vendor, ensure they have the means—and indeed, the knowledge—to help you build compliant, secure data workflows from beginning to end. 

The value of the output your AI produces is directly related to the quality of the input. Before moving forward with any prospective vendor, ensure they have the means—and indeed, the knowledge—to help you build compliant, secure data workflows from beginning to end. 

Here are some points to consider to ensure this is a vendor for you.

Questions to consider: 

• ‍How will the data be collected?
  ‍Data must be carefully collected to protect privacy and prevent bias. Ensuring that data has been ethically obtained and correctly governed is a point of emphasis for regulators.‍
• How will the data be “cleaned”?
  ‍Data needs to be refined and structured in a way that an AI solution can use and interpret. Make sure a prospective vendor understands what types of data are appropriate for your use case and how to prepare it at scale.‍
• How will the data be transferred, stored, and secured?
  ‍When developing solutions for complex, highly regulated industries, proof of certification for things like SOC2 and HIPAA are table stakes. Additionally, you’ll want to verify that the vendor uses secure data transfer methods, such as encryption during transit and at rest, to prevent unauthorized access. Also, ensure they effectively track the status of the data over time via robust version control and data lineage systems.

Prompt development 

LLMs work best when you make it difficult for them to make mistakes. An AI vendor should understand how to craft prompts to generate business value. 

For an AI solution to generate value, it must surface useful information with as little human intermediation as possible. This is achieved by ensuring that every prompt to an LLM includes all guidance, data access, and guardrails necessary to generate a high-quality return. Things like: 

1. Industry-specific content to guide results
2. Phrasing that reflects informed insight into the domain 
3. Precise instructions on the structure of the result being sought 

Your vendor will need to demonstrate they understand the capabilities and limitations of AI and can provide insights on how to structure LLM conversations to extract maximum value. Here are some points to review with a prospective partner to ensure they have the means—and better yet, a history—of value-oriented prompt engineering.

Questions to consider: ‍

• ‍How do they build prompts, and what domain-specific knowledge do they have?
  ‍Technical acumen is one thing, but does the vendor understand the specific needs of your industry? It’s one thing to ask an LLM to plan out a fun afternoon at the beach, it’s another thing to have it understand if, for example, family-owned restaurants align with a home insurer's risk appetite, or not. ‍
• What methods are used to select material included in the context window?
  ‍You should understand the vendor’s criteria for selecting contextually relevant information and how they ensure this information is timely and accurate. Ask what processes they use to filter and prioritize the most pertinent data for inclusion in prompts.‍
• How often, and in what ways are prompts updated over time? Are these changes tracked?
  ‍Learn about their schedule for reviewing and updating prompts to keep them aligned with the latest industry trends and data. Ensure they have a system for tracking changes to prompts, including version history and impact analysis, to maintain transparency and continuous improvement.‍
• What methods are used to evaluate the results of prompts, and to compare the results to prior versions when changes are made?
  ‍Ask about their evaluation metrics and benchmarks for assessing prompt performance, including accuracy, relevance, and consistency. Understand their process for A/B testing new prompt versions and how they compare the results to previous versions to ensure improvements.

Output control

Non-deterministic AI systems act in unpredictable ways. A quality vendor should know how to measure misaligned behaviors, as well as how to address them.  
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The value of the output your AI produces is directly related to the quality of the input. Before moving forward with any prospective vendor, ensure they have the means—and indeed, the knowledge—to help you build compliant, secure data workflows from beginning to end.  Ask an LLM the same question 10 times and you might get 10 different responses. The goal is to generate 10 accurate, useful answers. Achieving this requires putting as much care into reviewing the system’s output as you do into preparing the input. 

Ask an LLM the same question 10 times and you might get 10 different responses. The goal is to generate 10 accurate, useful answers. Achieving this requires putting as much care into reviewing the system’s output as you do into preparing the input. 

Continuous monitoring and tweaking are necessary to adapt your system to accommodate new data and evolving requirements. Here are some questions to explore when evaluating a vendor’s approach to scaled output control.

Questions to consider: 

• ‍What evals will you run?
  ‍Inquire about their evaluation frameworks, including both automated and manual assessments, to ensure outputs meet quality standards. Learn about the specific metrics they use to evaluate outputs, such as precision, recall, and F1 score, as well as checks for hallucinations and biases.‍
• What role will human experts play in this process?
  ‍Verify that human subject matter experts are involved in reviewing and validating AI outputs to ensure they are contextually appropriate and accurate. Ask about their process for incorporating expert feedback into continuous improvement cycles for the AI system.‍
• How often will you review overall results, and what metrics will you use to guide refinement and improvement?
  ‍Get a handle on their schedule for regular reviews and audits of AI outputs to ensure ongoing quality and relevance. Inquire about the key performance indicators (KPIs) and metrics they use to monitor and refine the AI system, such as user satisfaction scores, error rates, and feedback loops.

Transparency 

Not only does visibility allow you to properly evaluate an AI’s performance, it’s increasingly required by regulators as a means to address system bias.

Transparency is crucial for every step from data preparation to prompt development and output review. You cannot evaluate what you cannot see. To maintain the highest possible standards, every AI vendor should be prepared to provide a window into every step under their control. 

Questions to consider: 

• ‍Can you provide clear documentation of your processes and methods?
  ‍Ensure that the vendor offers comprehensive and understandable documentation covering all aspects of their AI processes, from data collection to output generation. Ask for examples of their documentation to assess its clarity and completeness.‍
• Can you demonstrate every point at which they interact with an LLM, and provide a complete trail of what information was exchanged?
  ‍Verify that the vendor maintains detailed logs and records of interactions with the LLM, including data inputs, prompts, and outputs. Ensure they can provide audit trails that detail the flow of information through their systems, which is crucial for regulatory compliance and troubleshooting.‍
• Will you provide a routine report about their evaluations and measuring for potential bias?
  ‍Inquire about their regular reporting practices, including how often they produce reports on AI performance, bias detection, and mitigation efforts. Ask to see examples of these reports to evaluate their thoroughness and transparency in addressing potential biases and other issues.

At Sixfold, we’ve created a Responsible AI framework for prospects and customers to showcase our ongoing transparency work.

In Summary

AI has the potential to overcome challenges that have been holding businesses back for decades. If you haven’t started your AI journey, now’s the time to get started. Partnering with an AI vendor can help you identify use cases ripe for transformation and provide the skills to get you there.

I hope that this checklist helped you identify which vendor has the right combination of technical know-how, industry expertise, and regulatory awareness to get your business where it needs to be.

This post was originally published on Linkedin

I’m just going to say it: I don’t care how accomplished your team is, they just won’t be able to build a proprietary horizontal LLM to compete, feature-wise, with the GPTs, Geminis, and Claudes of the world. 

Your team may, however, have it in them to build a vertical AI solution to execute specific high-level underwriting tasks. Their solution will probably incorporate one (or even several) aforementioned foundational models complemented with additional components, purpose-made for your specific use case. 

If you haven’t investigated advanced AI for your underwriting tech stack, you’re already behind. The question for carriers has long since moved on from “should we implement?” to “what’s the best way forward?” Some might think it preferable to build a proprietary AI solution using internal resources.

Many larger enterprises are certainly going to take on that substantial challenge. But is this strategy right for your organization? Here are four questions to consider before taking that leap:

1. Do you know what a quality AI-powered solution looks like?

You know how to measure the success of, say, a proprietary Java-powered microservice or web portal. But do you know what metrics to use for a non-deterministic AI solution? It’s a whole new thing.

LLMs are flexible and amazing, but they’re also unpredictable and can get things wrong (even when the end user did everything right). Developing non-deterministic systems requires an evolution in thinking about usefulness and quality control. It means getting acquainted with new concepts like “error tolerance.” 

If you’ve worked with traditional digital systems, you know that when a problem arises, it’s almost always attributable to human error somewhere along the line. LLMs, on the other hand, can do weird stuff when they’re working properly. Ask an LLM the same question 10 times in a row and you’ll get 10 different answers. The key with these solutions isn’t robotic repetition, it’s making sure they provide 10 useful answers. 

Ask an LLM the same question 10 times in a row and you’ll get 10 different answers. The key with these solutions isn’t robotic repetition, it’s making sure they provide 10 useful answers. 

Not only must you anticipate some amount of unpredictability with LLMs, you have to build out an infrastructure to mitigate their impact. This could mean building in extra layers of validation to detect errors. Or perhaps by giving human users the ability to spot errors and give feedback to the system. In some cases, it might mean that we live with some amount of "spoilage," i.e., accepting bad results from time to time.

This is new territory, I know. Are you ready for it? Almost as importantly—would you know how to communicate this new paradigm to the stakeholders who matter?

2. Are you prepared for a relentless pace of change?

Due to LLM’s inherent newness, few engineers or product managers have experience shepherding a vertical AI to market. That means your team must learn to deal with both structured and unstructured data when engaging with LLMs. It means learning the latest prompt design strategies to ensure you're providing consistently accurate answers (and indeed, defining what “accuracy” even means in a non-deterministic system). And it means occasionally having to re-learn it all over again after the next great AI innovation drops.  And a new AI innovation is always about to drop.

Developing cutting-edge vertical AI in 2024 is very different than it was in 2023 and I can promise you, it will be different in 2025.

Developing cutting-edge vertical AI in 2024 is very different than it was in 2023 and I can promise you, it will be different in 2025. Technology moves fast, and at this moment of peak-buzz AI, you have to be prepared for changes to come at your team weekly, if not daily. 

Last year, for example, we were a LangChain shop, as was pretty much everyone else attempting to address big challenges with LLMs. Fast-forward one year and we—and many players in this space—concluded that LangChain just isn’t for production and moved on to building scalable pipelines directly with generative AI primitives. That meant rebuilding some key features from scratch while adding resiliency and scale.

Determination is paramount in the face of rapid change. Are you prepared to hard-pivot a project you’ve been pushing along for months because the ecosystem has irrevocably changed with a new model release, new technique, or newly proposed regulation? Are you prepared to explain the necessity of these sea changes to your team and stakeholders?

3. Are you up on today’s AI regulations? How about tomorrow’s?

There’s a lot of talk in the public discourse about the potential negative impacts of scaled automation. As a result, regulatory bodies at all levels of government have drafted rules for how AI can be implemented, many of which single out consequential sectors such as insurance. 

Technological acumen is crucial, but it could all be rendered meaningless if it doesn’t comply with regulatory requirements. Do you have the infrastructure in place to keep on top of this evolving patchwork of global regulations?

To navigate these choppy waters, you need a team in place to make sure you’re complying with today’s rules, and prepared for tomorrow’s.

What’s better? Getting your team in the conversation with the rule-makers, and help inform the rule sets as they take shape.

4. Can you compete for AI talent?

You have an amazing dev team. They’re driven and passionate, and great colleagues too. I’m sure they could launch a top-notch mini-site in just a few weeks. But have they designed an LLM-powered AI solution before? 

If not, you’ll need to find yourself some AI experts.

That means competing for talent in a limited pool of AI engineers and paying top dollar for it to keep pace with MAMAA-caliber compensation packages.

That means competing for talent in a limited pool of AI engineers (Reuters reports a 50% skills gap in AI roles) and paying top dollar for it to keep pace with MAMAA-caliber compensation packages.

This pool becomes even smaller when looking for talent experienced with building systems for highly regulated industries in general, let alone insurance in particular.

Did you answer “no” to any question above?

I don’t know where you’ll land when it comes to building your vertical AI solution. If the go-it-alone path seems treacherous, then you can always partner with a team that’s been leading the way in the emerging LLM-powered AI for insurance.

I’m not a salesman, I’m a techie, but I can tell you we do great work and our team would love to talk through what you have in mind.

This blog post was originally posted on LinkedIn

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

Just 12 months ago, we introduced Sixfold and its vision to the world. Today, we’re thrilled to announce a $15 million Series A investment led by Salesforce Ventures with participation from Scale Venture Partners and our initial seed investors Bessemer Venture Partners and Crystal Venture Partners.

With these additional resources, Sixfold will expand our already exceptional team of engineers to further enhance our products and accelerate our R&D efforts. On the business & operations side, we will broaden our offerings and grow our footprint beyond North America to the United Kingdom and European Union. 

Most importantly, this new capital injection will advance our mission to overcome today’s most pressing underwriting challenges, not by iteration, but by building a new end-to-end risk analysis paradigm. That’s a big statement, I know. But we didn’t pick the name “Sixfold” out of modesty.

Year one of building insurance’s most consequential tech

In our inaugural blog post, I pledged that Sixfold would “focus on one of the most intractable challenges in insurance: the inefficiency of underwriting” and our team has backed up this promise with a year’s worth of accomplishments. 

We’ve developed a patent-pending AI that rapidly translates carriers’ unique underwriting guidelines into digital risk models, regardless of what form those guidelines take. This is Sixfold’s superpower, but far from our only bit of AI magic.

Leveraging 10 proprietary models, our platform surfaces appetite-aligned risk signals from disparate sources and independently “connects the dots” to generate natural language summarizations and recommendations. All our hard engineering work has resulted in unquestionable business value. In just one year, Sixfold has boosted underwriting capacity for our customers by a factor of 10, accelerated data collection by 2,000x, sliced submission-to-quote cycles from hours (sometimes weeks) to mere minutes, and pushed our platform accuracy to an industry-leading 94% so our customers can precisely assign NAICS/SIC codes at scale.

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Sixfold is uniquely posed for continued growth with top-tier global partners in the year to come. We were named a winner of The 2024 Zurich Innovation Championship, the industry’s largest open innovation contest. As one of only 9 winners selected from more than 3,000 global applicants, our team will participate in the Championship’s elite accelerator program to develop a new commercial underwriting solution.

Our Innovation Championship win was a powerful validation of Sixfold’s approach from a storied industry leader, and it wasn’t even the only one in 2024—in March, Sixfold was selected for the exclusive Lloyd’s Lab accelerator program (out of their largest-ever pool of applicants). Our team is currently working with Lloyd’s to develop innovative solutions for the world’s leading insurance and reinsurance marketplace, which will be invaluable as Sixfold expands into the UK and beyond.

Writing the next chapter in underwriting

Over the past year, we’ve grown from a team of three to a staff of 18, with more additions to come. In the near term, we’re focused on bringing on seasoned tech leaders to guide our continued success, as exemplified by the recent addition of Ian P. Cook, PhD, Sixfold’s first full-time head of AI. By adding select talent to our elite team, there’s no underwriting challenge we won’t overcome. 

I’m ridiculously proud of what this team has accomplished and excited about everything we will accomplish utilizing this latest fund round as a jumping-off point. 

I want to thank Laura Rowson, Nowi Kallen, and the rest of the Salesforce Ventures team for seeing the unique potential of our vision, or as Laura generously put it in our press release, for seeing Sixfold “as a company capable of transforming the insurance industry.” We’re beyond excited to work with Salesforce Ventures to accelerate our growth and materialize those changes.

And, of course, a huge thank you to Alex Niehenke at Scale Venture Partners and for continued support from our initial backers Charles Birnbaum & Jeremy Levine at Bessemer Venture Partners and Jonathan Crystal & Stephen McGovern at Crystal Venture Partners. 

If you haven’t yet had a chance to see what our platform can accomplish, there’s no better time to get started. Reach out for a personalized demo. We can’t wait to show you the future of underwriting.

This article was originally posted on LinkedIn