Insurance underwriting isn’t for the weak. It’s a dizzyingly complex undertaking that requires connecting data points across disparate sources to support consequential decisions—all while meeting modern expectations for speed, accuracy, and compliance.
The role has grown exponentially more challenging as technology has become more ubiquitous, stretching our information-rich digital trails ever longer.
Over the past two decades, various vendors have developed Intelligent Data Processing (IDP) tools to manage all this information by automating the extraction, ingestion, and structuring of data at scale. These tools have been widely adopted by carriers, but fall short of today’s mounting data challenges–in fact, they’re exasperating them.
McKinsey estimates that underwriters spend 30-to-40% of their time on rote administrative tasks “such as rekeying data or manually executing analysis.” These were the types of tasks that IDPs were supposed to automate and make more efficient—but that’s not what’s happening. In a recent Accenture survey, 64% of underwriters reported that today’s tech either makes no difference or increases their workload.
Automated data extraction was, until recently, the only way to tame the information deluge. New technologies have paved the way for a better, more seamless approach. Emerging LLM-powered AI represents a new paradigm that eliminates extraction chokepoints, reduces the burden on overtaxed underwriters, and accelerates decisioning.
Generative AI in insurance changes everything
Traditional IDPs were designed to exhaustively extract every piece of data–no matter how irrelevant or repetitive—so that it can be structured into a centralized database and passed along to overloaded human underwriters to query and scrutinize. The more complex and document-laden a process (e.g., loss run reports with intricate hierarchical ordering of nested sets), the more odious the inefficiencies and the more work tossed onto underwriters’ plates.
Insurance solutions touting the “most efficient” or “fastest” data extraction are about as meaningful in 2023 as boasting the “highest print-quality” fax machine. Comprehensive extraction is a relic of a fading technological paradigm. The industry is rightly turning to next-gen AI technologiesto free underwriters from repetitive data work (which is better handled by machines anyway) so they can focus on building value and closing deals.
Sixfold uses state-of-the-art LLMs to synthesize information across multiple sources and generate summaries in plain language for underwriter review. No processing power is misspent on redundant extraction; underwriters’ valuable time is no longer wasted sorting through virtual buckets of well-structured (but context-free) data.
When processing a life insurance application, traditional IDPs will, for example, extract each mention of the applicant having diabetes, even if it appears across dozens of documents. Unlike AI-powered platforms, IDPs are incapable of discerning meaning from data—underwriters are still required to connect the dots. Sixfold skips the needless chronicling of data points and independently generates clear summations of relevant throughlines (e.g., “The applicant was diagnosed with type 2 diabetes 12 years ago and it’s being properly managed with insulin and diet”), thus freeing underwriters to forgo the data work and render decisions faster.
Sixfold brings the power of advanced AI to underwriting
In effect, Sixfold provides underwriters with a virtual army of researchers, data processors, and writers who know precisely what information is needed to render decisions quickly (and just as importantly, what isn’t).
It’s already having a huge impact. With Sixfold, companies are accelerating submission-to-quote cycles by as much as 43%, clearing backlogged queues, and massively increasing GWP per underwriter.
Even better? It’s far easier to get up and running with Sixfold than a traditional IDP. These older systems required huge investments in time and resources to train their ML models on an organization’s unique needs. Sixfold, on the other hand, can be easily—and quickly—configured to match the appetite and needs of specific carriers and programs. It’s more-or-less ready to go out-of-the-box (or out of the virtual SaaS box).
AI is reshaping insurance before our eyes
The marketplace is littered with the remnants of corporate behemoths that misread the technological tea leaves—and in today’s world, giants fall fast. Consider how, in just one decade, Yahoo slid from the world’s most popular website to near-irrelevance. Or how Kodak only took eight years to complete its journey from top-five global brand to ejection from the Dow Jones. Or how, in a mere six years, Blockbuster leaped from its 9,000-plus-location peak into bankruptcy.
The takeaway: Past performance will not save you. New technological paradigms can seemingly come out of nowhere to reward leaders who had an eye on the future—and expose those who didn’t.
I’m confident that 2023 will be remembered as an inflection point for generative AI. The way insurance is handled moving forward will be a radical departure from the past. There’s now a clear industry-wide divide between those pursuing iteration and those seeking transformation. Which side do you want to be on?
The past decade saw more than its fair share of insurtech solutions promising to harness the power of “AI.” Many of these tools use hard-to-train algorithms powered by technologies that are years—if not decades—old. These legacy underwriting tools may inject some process efficiencies but don’t address the fact that insurers are struggling more than ever to expand capacity and grow Gross Written Premiums (GWPs) per underwriter.
A recent Accenture surveyfound that underwriters spend 40% of their time on administrative tasks—that’s a full two days of their work week. Inboxes are flooded with more submissions than ever, but by some estimates, underwriters are only able to respond to 10%.
These aren’t challenges that companies can simply spend their way around; they require a fundamentally new approach. At Sixfold, we believe ascendent technologies, like LLM-powered generative AI, will lead the way. By moving beyond legacy solutions, carriers can take on today’s most pressing underwriting challenges–and the challenges on the horizon.
Ingesting and synthesizing data from disparate sources at scale
In the connected-everything world, insurers have access to more data than ever. This is a blessing and a challenge. On one hand, it guides decisioning and improves outcomes. On the other hand, there’s so much data from so many disparate sources, that it’s impossible to process efficiently.
Underwriters often find themselves sorting through hundreds of pages of documents for a single application. This limits capacity and squeezes GWP per underwriter. The only way to overcome these chokepoints without massively expanding headcount (and dinging already precarious expense ratios) is by using sophisticated AI tools to automate complex business tasks at scale.
With Sixfold’s state-of-the-art underwriting AI, insurers can seamlessly integrate structured and unstructured data from multiple disparate sources. The platform reflects each company's unique risk appetite, so it automatically surfaces relevant information to accelerate UW decisioning.
Say it in plain language
Sixfold uses LLM-powered generative AI (the same tech behind ChatGPT, Bard, etc.) to summarize findings to underwriters in plain language, not spreadsheets.
The platform, in effect, gives every underwriter their own virtual research team to build detailed reports on every application. Sixfold even generates coverage recommendations based on the company’s UW format. Compare this to legacy AI tools, which merely repackage information into number-heavy spreadsheets and dashboards, inevitably requiring additional inspection and contextualization from underwriters.
Even better? Plain language summations expand the underwriting talent pool by de-emphasizing technical and computational skillsets that are better handled by machines anyway. This is a crucial break from legacy tools, as insurers are now forced to compete for limited underwriting talentagainst private-equity-backed firms, insurtechs, MGAs, and other nontraditional insurance companies.
Opacity in insurance is no longer an option
Sixfold was designed with transparency at its core because that’s what today’s customers expect and increasingly what regulators demand. The platform provides full sourcing and lineage of all underwriting decisions with clear semantic summaries, i.e. no more “black boxes.”
Customers accept automation as part of the modern digital landscape, but that acceptance comes with expectations of transparency, particularly when there are unexpected outcomes. Legacy solutions make it difficult—if not impossible—for insurers to provide customers with the clarity they deserve. Disappointed customers and diminished brand reputation, however, aren’t the only negative outcomes the industry needs to be mindful of.
As scaled automation becomes more ubiquitous, so have the calls for greater transparency from regulators. At all levels of government, there are movements to counter the influence of potential bias through increased transparency and accountability—particularly in crucial areas like insurance.
The marketplace has long since moved on from “because the algorithm said so,” and insurers must employ tools to reflect those changes.
Beyond legacy AI
We’re not the first to automate underwriting tasks using “AI,” but we’re the first to fundamentally reimagine the underwriting role using state-of-the-art LLM tech to generate business value. Customers are using our platform to accelerate submission-to-quote cycles by as much as 43% and massively increase their GWP per underwriter.
The role of theunderwriter is evolving, and the industry needs a new generation of tools to match. This is why we created Sixfold.
Over the past year, the world has had the opportunity to experiment with new LLM-powered generative AI platforms and discover how they might overcome longstanding business challenges. At Sixfold, saw the potential—and necessity—of applying gen AI in insurance underwriting.
Today, too many underwriters are overwhelmed with high-volume (but not necessarily high-value) tasks. They’re charged with collecting and synthesizing complex data from disparate sources, while subsequently acting as the key coordination points with agents and brokers.
Underwriters have exponentially more responsibilities but have been given only incrementally improved tools (at best). As a result, companies are hitting the inevitable limits of manual processes.
According to a recent Accenture survey, underwriters spend 40% of their time on administrative activities—nearly half their work lives. Their inboxes are flooded with submissions, but by some estimations, underwriters can only respond to 10%. This reduced capacity leads to lost business and inevitably places downward pressure on Gross Written Premiums (GWPs).
We started Sixfold because we saw the opportunity for gen AI to free underwriters from that growing administrative weight. Think about it: what would you accomplish if you suddenly had 40% of your work-life back? Even better: what if the tasks you no longer had to do were the ones you liked least? All that excess administrative work is… work.
The role of underwriter has long since moved past just risk selection and pricing. As McKinseyframes it: modern underwriting requires “a comprehensive set of capabilities across hard and soft skills, qualitative judgments about future industry performance.” Machines handle numberwork and repetitive tasks better anyway, so let them have it and free humans to generate value using their subjective, uniquely human skill sets.
Underwriting should be a creative, multifaceted, and dare I say, even… joyful endeavor. I believe gen AI will play a key role in this regard by empowering underwriters to focus on what they love most: making deals and closing business.
Generative AI, your new underwriting assistant
With gen AI, every underwriter can have their own virtual team of researchers and administrative assistants who know exactly what information the “boss” needs.
Guided by decades of collective industry experience, our platform collects and synthesizes data from third-party and proprietary sources, spots patterns, and summarizes risk in the insurer’s UW format—all using clear natural language.
The platform ingests and models each company's unique risk appetite, so it can surface relevant information and accelerate UW decisioning. Sixfold highlights application inconsistencies for additional underwriter review—for example, if a case falls within a potentially higher risk category, the platform pinpoints the precise data points that require closer evaluation.
With Sixfold, companies are accelerating submission-to-quote cycles by as much as 43%, clearing backlogged queues, and massively increasing GWP per underwriter. As for the underwriters themselves, they’re embracing the platform once they see the opportunity to move faster, be more productive, and make more money.
AI can’t replace underwriters, but it can amplify their potential
Across industries, there’s anxiety around potential disruptions gen AI will have on the labor market. I don’t see it that way. I view this technology as continuing the long technological tradition of freeing humans from mundane work.
Word processing software, for example, didn’t replace editors and writers—it allowed them to work faster while leaving the icky carbon paper, whiteout, and typewriter ink behind. All the while, the number of writers, editors, and communication workers continues to grow. Similarly, accounting software didn’t remove accountants, it just removed the need for accountants to be calculators and as a result, they could be more creative and specialized. Indeed, there’s currently a nationwideshortage of accountants and they’re earning more than ever.
I see gen AI having a similar impact on insurance by emphasizing the creative and specialized side of the underwriting role, which can make them more satisfied, productive, and successful. Better yet, more successful underwriters have knock-on benefits down the value chain to agents and brokers (who get yes/no answers quicker) and the organization as a whole (through greatly improved GWP).
I believe there’s never been a better, or more joyfultime to be an underwriter.