August 11, 2026
How insurtechs can scale the business with AI?
Data transformation is rapidly reshaping the insurance sector. Emerging capabilities like artificial intelligence, telematics, automation, and machine learning have transformed nearly every aspect of the business, with insurtech companies at the forefront of this rapid technological progression.
Traditional players are leveraging AI solutions for the financial sector to drive innovation in fraud detection, underwriting, claims processing, hyper-personalization, and value-based service delivery. With both sectors promising the same level of customer experience and service delivery, insurtech companies have to scale if they are to stay in business.
However, unlike traditional players, who have to revolutionize their entire insurance value chain to adopt these new technologies, insurtech companies are founded on the principle of data-driven service delivery, which puts them at a significant advantage. Investors seem to be taking note:
Source: McKinsey’s 2025 analysis
A sign that investors are backing insurtechs with proven, scalable models rather than early-stage bets alone.
But let’s start with the beginning.
KEY TAKEAWAYS
- Insurtech is reshaping insurance through AI, machine learning, IoT, telematics, and automation. Global venture capital investment has returned to about $1.5 billion per quarter, while Series C+ funding rounds increased from 100 in 2023 to 150 in 2024.
- Traditional insurers and insurtechs have complementary strengths: incumbents provide large customer bases and industry infrastructure, while startups contribute data-driven technology and faster innovation. Partnerships can improve fraud detection, personalized pricing, claims processing, and access to products such as bite-sized insurance.
- AI’s key insurance applications include fraud detection, risk assessment, automated underwriting, document and claims processing, appeals automation, and customer-service chatbots. These technologies can reduce manual work, improve risk evaluation, and accelerate customer-facing processes.
- The most advanced implementations are also moving beyond simple chatbots toward agentic AI — systems that can carry out multi-step workflows (like an entire claims intake and initial triage) with less human handoff at each stage.
- Scaling AI remains difficult across the industry: only 7% of insurers have deployed AI enterprise-wide, while roughly two-thirds remain at the pilot stage. About 70% of scaling obstacles are attributed to organizational, people, and process issues, rather than technological limitations.
- Future insurtech growth will depend increasingly on profitability, scalable operating models, AI governance, focused market strategies, and underserved risks such as climate and cyber. At least two-thirds of global climate risk remains uninsured, creating significant opportunities for technology-enabled underwriting and new insurance products.
What are insurtechs?
Insurtech refers to the technological innovations employed by the insurance industry to improve service delivery and customer service efficiency. It requires adopting and implementing cutting-edge technology like artificial intelligence, big data, the internet of things (IoT), and machine learning, among others.
The insurance industry’s unique capability to take advantage of innovative and disruptive technology has captured the interest of venture capitalists who are injecting billions of dollars into the sector. This isn’t a passing trend:
Source: McKinsey’s 2025 analysis
These collaborations are expected to intensify as the insurance industry continues to collaborate with the tech space.
Up until recently, the use of modern technologies in the insurance sector was limited to customer support and insurance claims.
But, as consumer demands for more personalized and accessible services increase, traditional players have also had to up their game, thus giving rise to numerous partnerships and collaborations between them and insurtech startups.
What this looks like in practice: Lemonade — a publicly traded insurtech — reports its AI assistant “Maya” generating insurance quotes in under 90 seconds, and its claims assistant “Jim” processing straightforward payouts in as little as 3 seconds without human involvement, alongside a customer base that passed 3.1 million policyholders in early 2026.
It’s a useful illustration of the gap between “we use AI chatbots” and what a mature, AI-native claims pipeline can actually do at the point of customer interaction.
How are traditional players and insurtech startups collaborating?
Insurtech startups have big ideas and technology that could revolutionize the insurance sector. Unfortunately, they lack a large-enough customer base to scale. Conversely, traditional players have a wide customer base, which demands faster, more efficient, and personalized services, which they cannot provide without leveraging technological solutions. By working together, these two competitors can bring innovative and financially inclusive insurance products, which are necessary to stay on par with the digital wave.
Here are a few ways in which traditional players can incorporate and collaborate with insurtech:
Implementing artificial intelligence
Insurance companies lose up to $80 billion a year to fraudulent claims. Their biggest challenge is processing legitimate claims faster while rejecting fraudulent claims. Most traditional players use Robotic Process Automation (RPA) 4 to achieve this.
Unfortunately, RPA comes with a few drawbacks since you have to train the system to execute a process. While this might work for repetitive tasks, unique cases must be handled by human employees, which can significantly strain a company in terms of manpower and time efficiency.
Insurtechs bring artificial intelligence to the table. This revolutionary technology can find erroneous information provided by insureds and detect discrepancies in fraudulent claims. Additionally, since AI processes are data-centric, the system can create its own logic and take action based on the data provided. AI is also easier to implement than RPA since it can process unstructured data.
Fraud detection itself is also getting harder in a specific new way: a March 2026 Verisk study found that 98% of insurance companies reported AI-based photo-editing tools driving digital fraud, while only 32% of insurers expressed high confidence in their ability to detect AI-manipulated claim photos. This is prompting a new category of AI-vs-AI tooling: the same generative models that can create a convincing fake damage photo are increasingly being matched by AI systems trained specifically to detect that manipulation.
Read More
The same shift from generic rules to granular, data-driven decisions is reshaping pricing, not just fraud detection — insurers are moving from group-based premiums to individualized rates based on real behavioral and contextual data. See how in The Influence of AI on Insurance Pricing.
Offering bite-sized insurance
Most millennials are uninsured. 55% of the total uninsured millennial population say that they simply can’t afford it. Insurtech companies have discovered that a wide majority of this demographic is interested in bite-sized insurance. By successfully selling bite-sized insurance for specific requirements like travel and vector-borne diseases, insurtech companies are selling insurance to a demographic that was otherwise unreachable by traditional players.
Bite-sized insurance policies are easy to buy and require much less paperwork compared to traditional insurance premiums, making them even more attractive to younger prospective customers. By partnering with insurtech, traditional players can also cash in on this goldmine. Their combined resources can also lead to a wider product offering, with insurance for all sorts of requirements.
How can small insurance agencies compete with insurtechs? 5 practical strategies
Small, traditional agencies often assume insurtechs have an unbeatable head start on AI.
Source: BCG’s 2025 research
Scaling AI is hard for everyone, which means small agencies that move deliberately can close more of the gap than the “insurtechs have already won” narrative suggests.
1. Partner instead of build. Small agencies don’t need to build proprietary AI to compete — most of the value insurtechs offer is now available through white-label and API-first tools that plug into an existing book of business without a multi-year engineering investment.
2. Focus on a narrow, defensible niche. As the bite-sized insurance trend above shows, insurtechs often optimize for scale and broad appeal. A small agency’s edge is depth: specializing in a specific line, region, or customer segment that’s too small for an insurtech to prioritize, but large enough to sustain a focused local business.
3. Adopt AI for the highest-friction task first. Trying to modernize every process at once spreads a small budget too thin. Identify the single workflow — often quote turnaround or claims status updates — causing the most customer frustration, and automate that first before expanding further.
4. Lean on data you already have. Insurtechs compete on breadth of data; small agencies can compete on depth of local relationships — renewal history, claims context, and direct customer conversations that a purely digital competitor doesn’t have access to.
5. Track a small set of metrics that prove ROI fast. With less runway than a venture-backed insurtech, small agencies need to demonstrate results quickly. Retention rate, average claim response time, and cost per acquisition are enough to prove a pilot is working before expanding it.
AI trends in insurtech that can boost growth
Artificial intelligence is not a new concept in insurance. Traditional players are already using AI in marketing automation, customer support through chatbots, and retention management systems. However, AI’s greatest value to insurance lies in automating core processes such as profiling and underwriting.
Here are a few ways in which insurtechs implement AI to automate key processes and ultimately improve their business model:
Read More
Automating a process is only half the opportunity — the other half is using that same data to make each claim feel individually handled. See how unifying customer data across transactions, IoT signals, and behavior enables this in Why Insurers Should Focus on Hyper-Personalized Claims.
Scaling beyond pilots: why most AI initiatives stall
Fraud detection, risk assessment, and automated underwriting (covered below) are the use cases insurtechs talk about most — but implementing one pilot successfully is not the same as scaling AI across the business. According to BCG, roughly 70% of scaling obstacles come from people, process, and organizational factors, not from the technology itself; only about 10% trace back to genuine model limitations. Separately, Genpact’s research found that 85% of AI use cases in insurance are still driven bottom-up by individual business units, which tends to produce isolated wins rather than company-wide capability.
For insurtechs, the practical takeaway is that scaling AI successfully requires the same operating discipline as any other core capability: clear ownership, cross-functional buy-in, and a small number of prioritized use cases tied to specific business outcomes — rather than a portfolio of disconnected pilots.
Fraud detection
A meaningful share of all insurance claims involve some form of fraud. By implementing AI and machine learning algorithms, insurtech companies can detect suspicious activity in insurance claims in real-time. This is a much-needed cost-saving solution for insurtech companies who need the extra capital to scale and gain a competitive advantage against legacy players.
Risk assessment
Historically, insurance companies have relied on information provided by applicants to access risk. The biggest problem with this model of risk assessment is that applicants may be dishonest or make mistakes. The result is inaccurate risk assessment, which could result in high-risk applicants getting lower premiums and vice versa.
Insurtech employs various technologies, including AI, machine learning, and natural language processing, to pore through abstract sources of information such as SEC filings, yelp reviews, and social media posts. By pulling pertinent information from numerous sources, insurance companies can get better assessments of an applicant’s potential risk.
Insurance customers are continuously looking for more affordable insurance coverage. Therefore, any insurance company looking to stay competitive must offer affordable premiums. By collaborating with tech-focused insurtech companies, traditional players can get more accurate risk assessments, which, in turn, can help them make more appropriate and affordable insurance premiums.
Automatic underwriting
Traditionally, claims processing was a painstaking process, requiring a lot of manpower and stacks of paperwork. Through automation, this process has become significantly more efficient, with much of the manual work in image review and damage estimation increasingly handled by AI-based tools rather than by underwriting staff alone.
Let’s take the auto insurance sector, for example. Traditionally, the company had to send an insurance agent who would visit the customer, take photographs of the damaged vehicle, and estimate the damages. But now, insurtechs implement AI-based algorithms like image-recognition software to analyze pictures taken by the customer, thus automating the underwriting process altogether.
This is also where naming specific vendors matters, since “AI-based algorithms” covers a wide range of actual implementations. AIG has built a generative AI-assisted underwriting tool for excess and surplus claims processing in partnership with Anthropic and Palantir — a concrete example of what an underwriting-specific foundation model partnership looks like at a large carrier. Underwriting turnaround times reported for standard risk profiles at some insurers have reportedly dropped from around three days to as little as three minutes — though as with any dramatic before/after figure, it’s worth asking a specific insurer for their own numbers rather than assuming an industry-wide average applies to a given book of business.
Customer service
Customers prefer working with insurance companies that offer consistently positive experiences.
Source: Genpact’s research
This is a reminder that AI-driven efficiency gains for the business don’t automatically translate into visible improvements for the customer.
Insurtech company websites feature AI tools like chatbots that can guide a customer through numerous queries in real-time and without any human intervention. Say, for example, a customer needs to access their account. In this case, a chatbot could assist them right from the insurer’s website. Proper implementation of these features could potentially resolve customer issues instantly. Although human intervention is still required for complex concerns, leveraging AI chatbots can make the process a lot easier, not to mention the cost-saving benefits of hiring fewer employees.
Benefits of AI implementation in the insurance sector
The insurance sector is beset with numerous risks, which mostly revolve around assessing different possibilities when determining premium pricing. By leveraging AI, insurance companies can overcome most of these challenges and reap other benefits as well.
Here are some notable benefits of implementing AI in the insurance sector:
Applications processing and insurance underwriting
Application processing typically requires extracting and analyzing information from a large volume of documents. While doing it manually still gets the job done, it is prone to errors and takes too long. By leveraging AI technologies like document capture technologies, insurance companies can extract relevant data automatically from applicants’ documents, thus significantly accelerating the application process with fewer errors. Faster application processing also leads to increased customer satisfaction.
Claims processing
Claims processing is one of the most sensitive areas in the insurance business. Even a slight mistake could result in significant losses, especially where there is fraud. Claims processing typically involves multiple tasks, including review, investigation, and adjustment. Depending on the validity of the claim, an insurance company can either deny or move on to remittance.
Like application processing, claims processing involves a large volume of documents, which, if reviewed manually, could result in errors. Additionally, the process is barred by continuously changing regulations and varying data formats, which cannot be processed by RPA technologies. By leveraging AI capabilities, insurance companies can eliminate human error and keep up with insurance regulations.
This regulatory dimension is becoming more prominent industry-wide:
Source: Deloitte’s 2025 research
For insurtechs, building governance and documentation into claims and underwriting AI from the start — rather than retrofitting it later — is increasingly a competitive requirement, not just a compliance checkbox.
Appeals processing
After claims are processed, denied claims may result in appeals, which can increasingly be automated through AI and machine learning technologies.
Other emerging AI-powered technologies like hyper-automation can automate end-to-end processes such as claims processing, redaction, and appeals processing.
How will insurtechs accelerate the next wave of growth in the insurance industry?
Drive profitability
Raising capital and acquiring a customer is just one part of the huddle. After all this, insurtech companies still have to drive a path to profitability. Most insurtech startups get stuck at this stage. However, insurtech startups with their own differentiated technologies and processes manage to tap into new revenue-generating opportunities.
Despite the numerous venture capital investments hitting the insurtech sector, investors are still less-inclined to make pure growth bets. Insurtech companies that demonstrate solid economics and a clearly laid out path to profitability have a significantly higher chance of getting investment capital.
One of the clearest paths to that kind of differentiated growth is underinsured risk. McKinsey estimates that at least two-thirds of global climate risk remains uninsured, and emerging risk categories like cyber and climate together represent less than $200 billion of the industry’s $4.1 trillion in total premiums — a large, underserved space that rewards insurtechs willing to build AI-driven underwriting models for risks traditional carriers are still reluctant to price.
Refresh the strategy
Based on what we can see, most insurtechs will undoubtedly scale in their core markets. When they reach their peak, most companies will naturally set sights on new markets that can spur horizontal growth opportunities.
At this point, these companies will need a full strategy refresh, which will require a substantial amount of analytical rigor and external perceptiveness. The primary goal here will be to create a road map for an ambitious growth trajectory and identify strategic M&A opportunities and enablers to achieve their growth targets.
Insurtechs with a clearly defined playing field where they can win and double down on specific customer segments will have the highest probability of success. With a clearly laid out strategy, insurtechs can introduce their model to new markets and geographies and drive growth tremendously.
Final thoughts
Insurtech companies are giving traditional players a run for their money. As the insurance industry shifts to personalized service delivery and automation, tech-driven insurance companies that leverage AI capabilities have a higher probability of scaling upwards. The changing market dynamics have fueled various partnerships between insurtech and traditional players who want to stay on top of their game.
Do you want to know how insurtechs can implement AI and scale their business? Our AI consulting services help insurtechs and traditional insurers alike turn these use cases into working systems — drop us a line to talk through your specific challenge.
References
- McKinsey & Company. “AI meets insurtechs: How firms can close the global protection gap.” URL: https://www.mckinsey.com/industries/financial-services/our-insights/insurance-blog/ai-meets-insurtechs-how-firms-can-close-the-global-protection-gap. Accessed Aug 11, 2026.
- BCG. “Insurance Leads AI Adoption. Now Is the Time to Scale.” URL: https://www.bcg.com/publications/2025/insurance-leads-ai-adoption-now-time-to-scale. Accessed Aug 11, 2026.
- Deloitte. “Scaling Gen AI in Insurance.” URL: https://www.deloitte.com/us/en/insights/industry/financial-services/scaling-gen-ai-insurance.html. Accessed Aug 11, 2026.
- Genpact. “How to Scale AI and Build Trust in Insurance.” URL: https://www.genpact.com/content/dam/genpact/web/documents/How-to-scale-ai-and-build-trust-in-insurance_Genpact-research.pdf. Accessed Aug 11, 2026.
- Gartner. “Robotic Process Automation.” URL: https://www.gartner.com/en/information-technology. Accessed Aug 11, 2026.
- Fierce Healthcare. “Millennials Forgo Health Insurance Because They’re Uninformed.” URL: https://www.fiercehealthcare.com/payer/millennials-forgo-health-insurance-because-they-re-uninformed.
- IndexBox. “How Lemonade Uses AI to Disrupt the Insurance Industry.” URL: https://www.indexbox.io/blog/how-lemonade-uses-ai-to-disrupt-the-insurance-industry/. Accessed Aug 13, 2026.
- SG Analytics. “AI in Insurance 2026: Underwriting, Claims and Fraud.” URL: https://www.sganalytics.com/blog/ai-insurance-underwriting-claims-fraud/. Accessed Aug 13, 2026.
- Vantage Point. “Insurtech Trends 2026: How AI Is Transforming Claims and Underwriting.” URL: https://vantagepoint.io/blog/sf/insights/insurtech-trends-2026-ai-claims-underwriting. Accessed Aug 13, 2026.
- actuary.info. “AI Regulation in Insurance 2026: The NAIC Model Bulletin, State Adoption, and the Federal Preemption Battle.” URL: https://actuary.info/insights/ai-regulation-insurance-naic-2026. Accessed Aug 13, 2026.
- DeepInspect. “Insurance AI Pricing Under the EU AI Act and NAIC Bulletin.” URL: https://www.deepinspect.ai/blog/industries-insurance. Accessed Aug 13, 2026.
This article was updated on Aug 11, 2026 and now includes named, externally documented examples (Lemonade’s AI-driven quoting and claims assistants, AIG’s underwriting partnership with Anthropic and Palantir) to ground previously industry-wide statistics in concrete cases. It also adds coverage of AI-generated fraud (AI-edited claim photos and detection challenges), an updated note on NAIC Model Bulletin state adoption alongside the EU AI Act’s “high-risk” classification for insurance AI, and a brief mention of agentic AI as a category beyond simple chatbots.
FAQ
How can small insurance agencies compete with insurtechs?
Small agencies don’t need to out-invest insurtechs to compete effectively. The most practical approach is partnering with existing AI-powered tools instead of building proprietary technology, focusing on a narrow and defensible niche, automating the single highest-friction task first, and leaning on local customer relationships and data that insurtechs typically don’t have access to. Tracking a small set of clear metrics — retention, response time, cost per acquisition — helps prove ROI quickly on a limited budget.
What is the biggest challenge insurtechs face when scaling AI?
According to BCG’s 2025 research, only about 7% of insurers have successfully scaled AI enterprise-wide, while roughly two-thirds remain stuck in pilot projects. The primary obstacles are organizational rather than technical — around 70% of scaling difficulties come from people, process, and change-management factors, not from AI model limitations.
Do insurtechs actually have an advantage over traditional insurance agencies?
Insurtechs benefit from data-driven infrastructure built from day one, which makes implementing AI faster than for traditional players who must modernize legacy systems. However, this advantage is narrower than it appears: most insurtechs also struggle to scale AI beyond initial pilots, and traditional agencies can offset the gap with niche focus, local data, and faster, more targeted AI adoption.
What AI use cases deliver the fastest results for insurtechs?
Fraud detection, automated underwriting, and AI-driven customer service (chatbots handling routine queries) tend to show measurable results fastest, since they target well-defined, repetitive processes with clear before/after metrics. Broader transformation initiatives, like enabling self-service analytics across the business, typically take longer to show ROI.
Why is AI governance becoming more important for insurtechs?
Regulatory attention on AI in insurance is increasing: 19 U.S. states have adopted the NAIC’s Model Bulletin on AI governance since December 2023. Building documentation, oversight, and bias-testing into underwriting and claims AI from the start is increasingly necessary to stay compliant across jurisdictions, not just a best practice.
What is "agentic AI" and how is it different from the chatbots insurtechs already use?
A chatbot typically answers a question or routes a request — it responds within a single, narrow interaction. Agentic AI refers to systems designed to carry out a multi-step task with less human intervention at each stage — for example, receiving a first notice of loss, gathering the necessary supporting documents, checking them against policy terms, and preparing a claim for either automatic approval or human review, all within one workflow rather than several separate handoffs.
This matters for insurtechs specifically because it changes what “automation” means in practice. A chatbot reduces the effort of asking a question; an agentic system can reduce the number of times a claim or application needs to be picked up and re-evaluated by a different person or system altogether. It’s a meaningfully different (and generally less mature, more carefully governed) category than the chatbot-based automation most of this article discusses.
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