The life insurance underwriting landscape is undergoing rapid transformation. Emerging data sources, advanced technologies, and a growing push toward automation are reshaping how risk is assessed. At the same time, medical breakthroughs in early detection, diagnosis, and treatment are improving health outcomes, while rising rates of obesity and diabetes introduce new challenges.

Our Q&A series explores this evolving environment, spotlighting the innovators driving change.

In this edition, we speak with Adnan Haque (AH), founder and team leader of alitheia, Munich Re’s rapid risk assessment and decisioning platform. Adnan leads a multidisciplinary team of data scientists, machine learning engineers, and underwriting specialists who are building cutting-edge tools to enhance both instant decisioning and human review. Their work is redefining what it means to “underwrite” life insurance.

What are some of the key market trends that excite you about where things might be headed?

AH: One of the trends I'm most optimistic about is the adoption of electronic health records, or EHRs. I believe EHRs accelerate two significant improvements in how we assess risk: 

1. Rely on data first, disclosures second. By prioritizing data over self-reported information, we minimize the risk of omissions and inaccuracies that can occur with disclosures.
2. Increase automation rates without impacting mortality cost. EHRs will underpin the next significant increase in automation rates by providing readily accessible and standardized health information. This streamlines operations, reduces application-to-issue times, and enhances customer experience  ̶  all without compromising our mortality assumptions.

Another trend I'm excited about is the rapid development happening in AI. It will impact every part of the insurance value chain, from how we engage with customers to how we manage claims.

What can we do as an industry to react to or stay ahead of these trends?

AH: Ensure you actively engage in evaluating new tools and data sources to understand their impact. Some of these pilots/proof of concepts can be done with very limited resources. I'd also recommend being thoughtful about where we choose to build versus partner. We should build where we can have a strategic competitive advantage, and it directly contributes to our core competency, or if we have very niche requirements. In most other scenarios, we should buy or partner.

What are some of the major challenges you see that the industry is facing or will face in the near term? 

AH: Cybersecurity risk is a major challenge I see the industry facing over the near term. According to Security Magazine, a cyber-attack occurred every 39 seconds in 2023. As life insurers, we have a duty to protect the sensitive personal data that individuals entrust to us. At the same time, insurers’ core systems can be dated, sometimes as old as 50 years, and are less able to adequately protect that data.

Are there any blind spots that worry you in underwriting today or in the life insurance market in general?

AH: While we have many controls around misrepresentation, I've always worried the industry was a few blog posts away from a significant increase in misrepresentation rates. For example, a post with tips about gaming insurance applications could go viral, and third-party data would likely not catch every instance. 

If so, what can we do to fix those blind spots? 

AH: As data sources and our ability to leverage them improve, this risk will naturally be mitigated. In the interim, we should closely monitor our misrepresentation rates through post-issue audits and other tools.

What does it mean to you to “get the basics right” and why would that be important in underwriting and risk assessment? 

AH: To me, getting the basics right means getting the right team together to address the problem at hand. Everything stems from people and how they come together to achieve a goal. As underwriting and risk assessment become increasingly multidisciplinary, it means having underwriters, actuaries, data scientists, and engineers all working in tandem. We’ve written about this topic in more detail here.

Munich Re’s Future of Underwriting Forum Q&A series explores the rapidly evolving risk assessment landscape with industry experts. View the full series here.

Sponsored by ITL Partner: Munich Re

The integration of artificial intelligence (AI) and intelligent decision-making now prevails across the insurance industry. From streamlining underwriting processes to personalizing customer experiences and accelerating claim settlements, AI holds the promise of a more efficient, responsive, and ultimately, more valuable industry.

However, the true potential of this technological revolution cannot be fully realized without a parallel evolution in leadership, one that embraces diversity and recognizes the indispensable contributions of women. As a woman working at the forefront of insurtech, I have witnessed firsthand the transformative power of diverse perspectives, particularly when it comes to navigating the complexities and ethical considerations inherent in AI adoption.

While women make up approximately 59% of the overall insurance workforce, their representation in leadership roles is far lower. A 2024 report by the Triple-I blog states that only about 22% of those in the C-suite are women.

Fostering more female leadership within life insurance is not merely a matter of equity but a strategic imperative for driving truly intelligent innovation and ensuring the long-term success and societal benefit of these powerful technologies.

Mitigating Bias in Algorithmic Systems

One of the most critical areas where women leaders bring a unique and vital perspective to the AI-driven life insurance industry is in mitigating inherent biases within algorithmic systems. AI models are, at their core, reflections of the data they are trained on. If this data is skewed or incomplete, the resulting algorithms can perpetuate and even amplify existing societal biases, potentially leading to unfair or discriminatory outcomes in areas like risk assessment and premium pricing.

Women leaders, with their heightened awareness of equity and inclusion issues, are more likely to critically examine the data sets used to train AI models, ask the challenging questions about representation and fairness, and advocate for the implementation of rigorous ethical frameworks and governance structures. Their focus on ensuring that AI is developed and deployed responsibly is crucial for building trust with a diverse customer base and avoiding the pitfalls of algorithmic bias that could undermine the integrity of the industry.

By championing inclusive data practices and demanding transparency in AI decision-making, women leaders can help steer the industry toward a future where technology serves all segments of the population equitably.

Enhancing Customer-Centricity and Empathy

Beyond the important work of bias mitigation, women leaders also excel at fostering a more customer-centric approach to technological innovation. While AI offers the potential for unprecedented efficiency, it is essential to remember that life insurance is ultimately about people – their security, their families, and their futures.

Women, often recognized for their strong emotional intelligence and empathetic leadership styles, can ensure that the adoption of AI enhances, rather than detracts from, the human element of the insurance experience. They are adept at understanding customer needs and pain points and can advocate for the design of AI-powered tools that prioritize user experience, accessibility, and trust.

Earlier in life, my husband died unexpectedly, and my children and I were left to pick up the pieces without having life insurance in place. My experiences have shaped my approach to this industry, and I look at each and every customer through this type of lens to ensure each family has an opportunity to make the right choice for their individual needs.

Whether it's ensuring that AI-driven chatbots offer genuinely helpful and empathetic support or that automated underwriting processes are transparent and easy to understand, women leaders can champion a vision of technology that empowers and supports customers throughout their journey. Their focus on relationship building and clear communication ensures that the industry's technological advancements translate into tangible benefits and a more positive experience for policyholders.

Driving Innovation and Collaboration

Furthermore, the infusion of more women into leadership roles naturally fosters greater innovation and collaboration within life insurance organizations. Diverse teams, by their very nature, bring a wider range of perspectives, experiences, and problem-solving approaches to the table.

Women leaders often excel at creating inclusive environments where different viewpoints are valued and where interdisciplinary teams can collaborate effectively. The successful integration of AI requires a confluence of technical expertise, actuarial science, underwriting knowledge, and a deep understanding of customer behavior. Women leaders, with their collaborative leadership styles, can break down silos between departments and encourage the cross-functional communication necessary to harness the full potential of AI.

By challenging the status quo and promoting creative thinking, they can drive the development of novel AI-powered solutions that address unmet customer needs and propel the industry forward in unexpected and important ways. Moreover, a visible commitment to gender diversity in leadership serves as a powerful magnet for attracting a more diverse talent pool, including the next generation of data scientists, engineers, and business innovators who will be essential for fueling future technological advancements.

Today's life insurance industry realizes the transformative power of AI that is poised to reshape its operations and customer interactions. To truly capitalize on this opportunity and ensure a future that is both technologically advanced and ethically sound, the industry must cultivate and elevate women leaders.

Their unique perspectives, their focus on mitigating bias and enhancing customer empathy, and their collaborative approach to innovation are not merely supplementary benefits, but essential ingredients for success. By embracing gender diversity at the highest levels, the life insurance industry can harness the full potential of AI to create a more inclusive, efficient, and ultimately more human-centered future for all.

In specialty insurance, software alone can't ensure efficiency, accurate decision-making, and steady business growth. The systems that win are those that deftly balance automation with human touch.

Specialty lines are not a natural fit for wide automation. Their complex perils, bespoke coverage terms, and one-off claim scenarios by design demand human judgment and oversight. For years, carriers have been striving to move standardizable parts of their workflows to digital rails. But the fact that we're still having deep conversations about automating specialty insurance indicates that out-of-the-box tools haven't totally succeeded and that few reusable best practices for custom buildups exist.

Yet the pressure to automate is mounting. In a landscape where artificial intelligence (AI) developments are outpacing specialty operating models and exposures are evolving faster than line-specific product backlog, you can no longer afford to rely heavily on manual routines. Insurers are aware of the gaps and are rushing to seize the opportunities unlocked by new technology, with 74% placing digital transformation highest on their 2025 strategic agenda. For  many chief information officers and chief technology officers I work with, the question today isn't whether to automate, but how to do this right, without compromising critical aspects and waiting another three to five years to see meaningful results.

From my experience with specialty insurers globally, I've learned four dangerous automation pitfalls — often invisible from the outset and costly to escape from. Whether you're deploying point tools or setting out a broad transformation, here are the mistakes you'll want to avoid.

Mistake 1. Underestimating Software Flexibility Needs

Flexibility is the foundational requirement for specialty insurance automation systems. Yet this is often the first thing compromised in the pursuit of fast go-lives and cheap prebuilt workflows. The results are sadly consistent: quick but short-lived automation benefits, high cost of change, and frustrated people who have to revert to manual routines.

The primary reason to prioritize flexibility is that you don't yet know what you'll need to automate next. New risks, products, distribution models, technology — all of these will entail changes to your operational rules and software. Think of how fast-evolving cyber exposures made basic IT hygiene checks that were sufficient three years ago — and the actuarial frameworks layered on them — somewhat obsolete. Or how recent shifts in climate patterns and the rise of parametric models have changed underwriting and payouts in marine, aviation, and agricultural lines. Any legacy systems that failed to adapt basically lost their edge.

The need for flexibility becomes even more urgent when you consider regulatory volatility. An automation system must rapidly accommodate every evolving rule, from jurisdiction-specific Anti-Money Laundering and Countering the Financing of Terrorism (AML/CFT) demands to new reporting standards in specialty finance lines. If changes require extensive coding, you risk violating compliance before fixes in your solution even materialize.

While you can't foresee everything, you can employ an automation solution that assumes change is coming and allows iterative enhancements. Flexible software will let you not just upgrade what you already do but amplify innovation and future-proof your operations.

Here's what securing that flexibility means in practice:

• Hardcoding is out of the question. Business users, not just IT teams, should be able to modify automation logic on the fly. For example, in one aviation insurance software project, an underwriting engine was built where risk engineers could adjust, test, and deploy risk rating and quoting rules mid-cycle without involving developers. This capability ensured quick response to regulatory shifts and emerging risk factors that rapidly altered customers' exposure profiles.

• If you're building a custom solution — like many organizations do, precisely because off-the-shelf tools fail to accommodate specialty insurance nuances — prioritize modular architecture. It can be service-oriented architecture, microservices, or a modular monolith. Modularity will let you design automation around isolated functions (quoting, binding, policy servicing, and so on), evolve each component independently, and easily add new features. Lack of modular architecture may stall feature rollouts for months, which is a quick route to losing a competitive edge in specialty's fast-shifting tech landscape.

• Your automation system must not only connect to data sources you currently use for know your customer processes, underwriting, and claim adjudication but also support smooth integration with new and emerging ones. This is becoming critical as alternative feeds like Internet of Things-enabled data from sensors, drones, and satellites take on a central role in specialty risk assessment. For custom systems, interoperability can be maximized via an application programming interface (API)-first approach, where you plan software around integrations from the ground up. The resulting solution is modular and easy to evolve by design. If you're implementing a ready-made tool, make sure it offers built-in extensibility for future integrations.

Mistake 2. Overrelying on Intelligent Automation

Some of the insurers I talked to believe that AI will soon unlock full automation for specialty products. The rise of generative and agentic AI gave even more hope that we can have specialty automated in a straight-through way, much like personal lines. Studies claim that 99% of insurers around the world are already investing or planning to invest in GenAI, even though 60% of firms haven't yet developed a sharp return on investment model for the technology.

AI does have clear, high-value use cases in specialty insurance. It offers five to 50 times speed gains in data-rich, high-volume scenarios that require quick action, like application processing or claim validation and triaging. Machine learning-supported analytics have proven more than 95% accuracy in dynamically mapping risks, predicting financial performance, and surfacing exposures across complex specialty portfolios. Generative AI came to automate the most time-intensive routines, such as reviewing 100-page submissions, compiling insights, crafting bespoke documents, and delivering basic customer advice. Frontrunners report more than twofold growth in employee productivity and an up to 6% increase in revenue.

But I have to disappoint you: AI, however powerful, can't automate specialty insurance end-to-end.

The reason is the inherent limitations of the tech's predictive, reasoning, and creative scope. Intelligent models draw on historical and current data, meaning that however crafty they are at extrapolating patterns and ideating, they cannot anticipate unprecedented risks and effectively navigate unique context. This makes AI unfeasible as an autonomous decision-maker in novel, low-data environments and "gray zones" with special arrangements — the specialty lines' everyday settings.

Take specialty claim adjudication in multi-tiered areas like aviation liability or marine cargo. AI tools could automatically process claim evidence, spot forged submissions, summarize investigation results, and suggest optimal settlement paths. But human judgement, negotiation, and context-aware decision making remain critical for accurate settlement.

When it comes to underwriting, AI may struggle to predict and score exposures that fall outside historical precedent, like the new energy technology's risks in environmental liability or war-on-terror exclusions in political risk coverage. Similarly, for actuarial modeling, no model can reliably replace expert judgment in niche or emerging segments (think intellectual property insurance or space launch coverage), where actuarial baselines don't yet exist. By trusting specialty actuarial and underwriting decisions to your intelligent solution, you may take more risk than opportunity.

When it comes to generative AI, data analytics experts highlight that it's really strong at summarizing information, reasoning, and concluding. At the same time, the non-deterministic structure of generative AI algorithms makes it hard to achieve repeatable results. In practice, it means that tasks like synthesizing underwriting summaries or claims reports may yield slightly different outputs each time, even when the input data remains unchanged. Combining generative AI with machine learning and independent assessment models drastically enhances consistency, but you'll still need a human in the loop to validate the insights.

Not to mention that AI-fueled automation doesn't guarantee value and, in some scenarios, may lose on cost-benefit to traditional approaches. For one aviation insurer, an underwriting system built on rule-based engines and statistical algorithms, with no AI involved to score risks or compose quotes, delivered accuracy comparable to intelligent models but with better transparency, flexibility, and at a lower cost. The resulting software succeeded because it precisely matched the real business needs.

Mistake 3. Treating Data as a Secondary Success Factor

Too often, insurers approach automation as a purely software play and treat the data that fuels digital operations as a secondary aspect. But like any bad fuel, poor data corrodes everything it touches. And it's inherently easy to misfuel your engine in specialty insurance, where data doesn't come from plug-and-play sources and is rarely standardized.

For advanced analytics and AI, the stakes are even higher. If the data used for model training is inconsistent or lacks depth, the AI solution may miss critical risk signals, overlook fraud, and reinforce biases. In agentic AI, anchoring models on proprietary expertise can become a huge competitive advantage, but that's only possible if your knowledge is well-organized and accessible at scale.

What do you actually need to build strong data foundations?

• Data discipline starts with a robust data governance strategy, which includes defining secure, controlled pipelines and clear standards for data quality, storage, and processing tiers. You also need to map which data can support which decisions, at what levels, and under what conditions. The map lays the basis for a resilient data architecture where new data sources can be seamlessly onboarded into a standardized, governed framework.

• Checking data for duplicates, missing values, and formatting errors and enriching it at ingestion is a must to ensure accurate entries. Data engineers use data integration tools like Azure Data Factory and AWS Glue to automate validation and cleansing routines at scale.

• Consider intelligent image analysis and natural language processing tools to automatically extract and normalize data from unstructured inputs. There are pre-built options, but custom pipelines and algorithms trained specifically on specialty insurance concepts would offer more accurate parsing and classification for your niche use cases.

• To maintain consistent data formats, apply profile and document templates, enforce standard taxonomies for specialty insurance risk classes, and define unified data inputs and outputs across automated processing workflows. 

• Implement centralized, scalable data storages to avoid data silos that hamper automation accuracy and speed. Dedicated repositories benefit specialty insurers from both data accessibility and cost standpoints. For example, you might employ a cloud data lake to store raw risk feeds and insured documents and use a data warehouse for structured data like policy records and claim histories.

• Use database indexing. It is crucial for smooth structured data retrieval operations. In one recent engagement, a managing general agent faced errors and delays in automated report generation. Optimizing and indexing the software's underlying database eliminated accuracy issues and introduced up to 75 times quicker reporting. For another specialty insurance client, database restructuring doubled claim processing efficiency..

The quality of third-party data that feeds automation — think your satellite imagery for agricultural products or telematics for fleet lines — matters a lot. Prioritize reputable data vendors who provide up-to-date datasets backed by clear documentation and offer flexible data integration options (APIs, direct feeds). Also, check for contingency: The vendor must maintain robust backup mechanisms to prevent data delivery disruptions.

You also need the ability to trace every data point used for automation to its origin. This helps you establish auditable data-driven workflows, quickly isolate errors, and achieve explainability in specialty AI models. Popular data integration platforms provide go-to lineage capabilities and allow building tailored metadata logging components into data pipelines.

Mistake 4. Neglecting the Human Take

Specialty insurers often pride themselves on human expertise, and rightly so. Which makes it all the more surprising to me why some embark on automation projects with minimal input from the people who actually carry the knowledge: actuarial, underwriting, and claim experts.

Without involving domain subject matter experts, you risk missing workflow specifics, subtle productivity blockers, non-apparent risk factors, and other contextual nuances critical for a winning automation system design. As a specialty insurance IT consultant, I know firsthand that no automation vendor can intuit these things. Worse, software planned out of touch with business users can lose its credibility at the door, which will inevitably hamper adoption.

I've seen the most impressive automation outcomes where organizations brought subject matter experts in from the start. For example, in one portal development project for a specialty managing general agent, teams worked directly with the underwriters and claims specialists whose data entry routines were to be partially moved to the customer and broker side to alleviate the workload. They involved them in planning portal features, validating automation rules, and testing ready components. Doing this helped optimize portal design, secure logic accuracy, and, most importantly, ensure the delivered functionality eliminates the teams' real operating issues. The result was both more efficient servicing workflows and higher satisfaction of the managing general agent's customers.

Change management is another area where employee participation brings much value. Before any software is rolled out, you need to map, challenge, and optimize every business process subject to automation. Otherwise, you're just automating friction. Engaging specialty insurance teams will help you surface hidden bottlenecks and design workflows for higher efficiency in real-world and digital settings. This move also fosters ownership and enhances employee trust in technology, which is critical for adoption.

Automation is no longer optional in specialty insurance, but it's not a cure-all either. The organizations that thrive will be the ones that treat automation as part of a broad business rewiring, align technology with real operational needs, and respect the irreplaceable value of human expertise. Getting all that right from day one will help you position for efficiency and sustainable growth despite increasing domain complexity.

Contributors: Stacy Dubovik, financial technology researcher, ScienceSoft; Alex Bekker, AI & data management expert, ScienceSoft.

In today's workplace, supporting employee well-being requires more than reactive benefits and one-size-fits-all coverage. With financial stress on the rise and employees increasingly worried about healthcare affordability and long-term security, employers must adopt a more proactive, comprehensive approach to benefits communication. A key opportunity lies in one of the most misunderstood benefits to help employees: life insurance.

Too often, life insurance is seen strictly as a death benefit. However, many modern life policies include living benefits that support employees while they're still alive. These can include accelerated death benefit riders (ADBRs), cash value accumulation in permanent policies, and waiver of premium riders. These features offer financial protection during critical life events—such as illness, disability, or unexpected expenses—and can significantly enhance financial wellness and peace of mind.

Employees Need More Than a Death Payout

According to Morgan Stanley's 2025 "State of the Workplace" study, 66% of employees report that financial stress hurts their work, up 9% from the previous year. HR leaders are noticing, too—83% say financial strain among employees is harming productivity. At the same time, younger workers are entering the workforce without adequate savings or a long-term plan. A 2025 MetLife study found that 60% of Gen Z women and 45% of men feel unprepared to manage their long-term financial future.

Even as the job market has stabilized, employee expectations have changed, and many now look for the right benefits options for long-term security. That's where living benefits can bridge the gap—if HR teams know how to explain them effectively.

Demystifying Living Benefits

Accelerated death benefit riders (ADBRs) are one of the most important, yet under-communicated, features in life policies. These riders allow employees diagnosed with terminal or serious illnesses to access a portion of their life insurance payout while still alive. Funds can be used for medical bills, rent, or family caregiving—all at a time when financial relief is needed most. With healthcare premiums soaring, ADBRs provide necessary liquidity for employees facing devastating diagnoses like cancer or heart disease.

Permanent life policies offer another layer of protection through cash value accumulation, which can be tapped as a tax-deferred loan or withdrawal. Whether used for emergency car repairs or education expenses, this cash value acts as a personal safety net—especially important given that 37% of Americans can't afford a $400 emergency.

The waiver of premium rider is another lesser-known but significant benefit. If an employee becomes disabled and can't work, this rider ensures their life insurance coverage remains active without requiring premium payments.

Why Educating on Living Benefits Matters for Employers

The employer benefits go far beyond compliance or checkbox offerings. When employees understand the full value of their life insurance policy, they feel more financially secure and supported. 

MetLife's 2025 Employee Benefits Trends study found that employees who feel "holistically well" are 67% more likely to be productive and 56% more likely to stay with their employer long-term. Meanwhile, HR Executive reports that 72% of organizations now view financial wellness as a core part of their HR strategy, linking it directly to talent retention and workforce equity.

When employees feel holistically supported—financially, emotionally, and physically—they are more likely to be productive, engaged, and loyal to their organization. Increasingly, companies are recognizing that financial wellness plays a central role in workforce well-being and are incorporating it into broader organizational strategies. By prioritizing benefits that support employees' everyday lives, employers can foster a stronger culture of care while also strengthening talent retention and equity across the workforce.

How to Educate Employees Year-Round

Despite the power of these features, most employees still don't understand them—largely due to how benefits are communicated. Too many HR teams rely on one-time enrollment guides filled with insurance jargon. A better approach requires a year-round, multi-channel education strategy.

First, go beyond the brochure. Use webinars, brief videos, and even real-life (anonymized) stories of employees who benefited from living riders. This makes the concept more relatable. Segment messages by life stage: Younger workers may be interested in cash value savings, while older employees may focus more on critical illness coverage.

Simplify the language. Replace terms like "accelerated death benefit rider" with phrases like "get money early from your policy if you get very sick." Consider monthly reminders or Q&A office hours where benefits administrators walk employees through what's available and when to use it.

Keep it proactive and personal. Personalized benefits statements or calculators can help employees understand exactly what's included in their coverage. Moreover, don't just educate during open enrollment—life events happen year-round, and communications should reflect that.

Turning a Traditional Benefit Into a Strategic Asset

At a time when employee loyalty is fragile and financial wellness is directly tied to job satisfaction, HR leaders must view benefits education as part of their larger workforce strategy. Life insurance is no longer just a "what if" policy—it's an active tool that can offer relief during life's hardest moments.

Educating employees on living benefits helps reimagine life insurance from a static document into a dynamic financial wellness tool. It empowers workers to make smarter decisions, relieves burdens when it matters most, and reinforces a culture of care and trust. In short, living benefits aren't just a feature—they're a reflection of the value employers place on their people, in life and in legacy.

Homeownership has long been considered a cornerstone of the American dream. But for many Gen Zers, record-high housing costs, economic instability, and rising insurance premiums are now making that dream feel more like a financial gamble.

According to 2025 data, 65% of Gen Z policyholders say they're likely to downgrade or reduce their home insurance coverage to save money. At the same time, our data shows that 69% of consumers are increasingly worried about property damage, particularly in high-risk areas. The result is a growing contradiction: more concern about loss, paired with less protection against it.

As more young homeowners consider opting for minimal coverage, they may be overlooking the long-term risks they're exposing themselves to. From weather-related disasters to everyday property damage, one unexpected event can wipe out years of savings and investments or put homeowners into significant debt.

To help this new generation of homeowners, the insurance industry must build trust with young consumers, helping them understand how and why cutting corners on coverage opens them up to serious long-term risks.

The Hidden Cost of Cutting Corners in Home Insurance

The contradiction between property damage concerns and young homeowners looking to reduce coverage reveals a potentially dangerous gap between perceived risk and actual preparedness. And the stakes are high.

With the National Oceanic and Atmospheric Administration (NOAA) predicting an above-normal hurricane season in 2025, and climate change fueling more severe wildfires, floods, and storms, property damage is an ever-increasing probability for many homeowners.

Even minor events can cause major damage, and without proper coverage, homeowners bear the full cost. For Gen Z homeowners, many of whom are already navigating first-time property ownership costs, a single uncovered incident could derail their financial stability. The illusion of short-term savings can quickly become a long-term financial setback.

The Trust Deficit: Why Gen Z Is Likely to Reduce Coverage

Trust, or the lack of it, is at the heart of the problem.

DocuSketch's research shows that nearly half of Americans (45%) don't fully trust insurance brokers to act in their best interest when selecting a home insurance plan. Many consumers are relying on brokers out of necessity, not loyalty. They need help navigating complex policies but often feel the experience is transactional or unclear.

This persistent trust gap between policyholders and insurance providers can be tied to a lack of transparency around policy and claims processes as a whole. Over half (54%) of consumers believe insurance companies aren't upfront about how claims are calculated. And when policyholders can't easily understand what's covered or how their protection works, it's no surprise that some begin to view insurance as an expendable line item, especially as they navigate tight budgets.

But the issue isn't just about product literacy. It's about relationships. Gen Z doesn't just want coverage; they want to be supported. They want to know exactly what they're paying for, how it protects them, and why it matters. Most importantly, they want to feel like the insurer is on their side, not just collecting premiums.

If the industry doesn't modernize communication and rebuild trust with Gen Z and future generations, this cycle of underinsurance and skepticism will only grow.

Bridging the Trust Gap: A Call to Action for Insurers

Fortunately, bringing the trust issue with homeowners to light presents an opportunity for meaningful improvement.

Insurance brokers who are willing to break from the traditional approach and instead become educators and advocates can fill a critical gap. In a market where insurance feels like a commodity, relationship-building becomes a competitive edge.

By taking the time to explain how coverage works, what's included in a premium policy, and how claims are assessed (without fear mongering) brokers can transform the perception of brokers from middlemen to trusted advisors.

Education, however, isn't enough on its own. Real-time transparency throughout the claims process requires both smarter communication and modern tools. 

Documentation technology can play a critical role. By creating a single source of truth that gives both insurers and policyholders access to real-time information, insurance professionals can minimize misunderstanding, reduce friction, and foster trust throughout the claims process.

The Future Belongs to Trusted Advisors

Young homeowners are driving a shift in how the insurance industry engages with consumers, and their expectations are clear: honesty, clarity, and meaningful engagement. As premiums continue to rise, trust and transparency are no longer optional—they're essential.

The future of insurance isn't about selling policies or processing claims, it's about earning trust. Those who act now to build that trust will be well-positioned to help young homeowners select the coverage they need and be a leader in this shifting landscape.

Let's be honest — staying productive at work isn't easy. Between scattered tools, endless pings, remote meetings, and the pressure to always "do more," it's no wonder employees feel like they're spinning plates all day.

But here's the thing: AI isn't just the buzzword tech companies throw around any more. It's starting to do something — and one of the places where it's having the biggest, though somewhat under-the-radar, impact is in productivity tools.

I didn't realize how much had changed until a few months ago, when I switched to a new project collaboration platform. Suddenly, deadlines weren't slipping through the cracks. Recurring tasks were automatically sorted. And I had this smart little assistant suggesting when to follow up with team members or flagging potential bottlenecks before they became a mess. It wasn't magic — it was just AI, baked right into the software.

From Nuisance to Navigator

We're not talking about sci-fi-level tech here. Most of the AI tools making a difference today are subtle. They help you prioritize tasks, summarize long meeting transcripts, track productivity patterns, and even check in on team morale through sentiment analysis.

This is the kind of tech that works best when it's invisible — almost like a co-worker who doesn't interrupt but quietly keeps things moving behind the scenes.

Tools like Notion, ClickUp, Slack, and Microsoft Teams are starting to integrate AI assistants that learn from how teams work. Some of them are now writing meeting notes on your behalf or surfacing action items from emails so you don't forget them. They don't replace people — they just cut the clutter.

Catching Up to the Hype

If you're wondering whether this is just another tech fad, the numbers say otherwise.

I recently read a market report by Roots Analysis that put things into perspective. According to their estimates, AI in the drug discovery sector alone is projected to grow from $1.8 billion in 2024 to $2.9 billion in 2025 and reach $13.4 billion by 2035. Although that's a different domain, the logic applies across industries: AI isn't slowing down. The adoption curve is steep — and workplace tools are very much part of that story.

Startups building AI-powered productivity tools are getting serious funding. Enterprise software giants are acquiring niche players. And small teams, like mine, are actually seeing the benefits without needing to overhaul everything.

What's the Catch?

Look, it's not all smooth sailing. Some tools overpromise and underdeliver. Others feel like they're spying on you — constantly analyzing your keystrokes or monitoring your online presence. The line between helpful and invasive is thin, and every company has to draw it for themselves.

There's also the very real challenge of adoption. People get stuck in their routines. The fanciest AI dashboard means nothing if your team still uses spreadsheets and sticky notes.

And let's not forget training. AI tools are only as good as the data they work with. If your internal processes are a mess, no bot is going to magically fix that. You've got to invest in both the tech and the team.

Something else I've noticed — and maybe you've felt this, too — is that these tools are slowly changing how teams talk to each other. When the software already knows who's responsible for what, there's less chaos in team chats. We don't have to ask, "Hey, did anyone follow up on that?" because the tool gently reminds the right person. It builds this quiet accountability that doesn't feel forced. That, to me, is where AI really earns its place — not in the flashy features but in making teams feel more aligned without needing to micromanage.

Where We Go From Here

The best AI tools I've used aren't the flashiest ones — they're the ones that free time, reduce repetitive tasks, and let me focus on actual thinking work. Whether it's suggesting a better way to structure a report or reminding me I haven't checked in on a teammate in a while, those little nudges add up.

We're probably just at the beginning of what AI-driven productivity looks like. In five years, we may look back at these early tools and laugh — but we'll also remember that this was the moment the future of work quietly started changing.

If you're still on the fence about using AI tools, I get it. But try one — just one — and give it a month. You might be surprised at how much space it frees up in your workday, and in your mind.

For decades, property and casualty (P&C) insurance has operated from a retrospective standpoint. Carriers were not in the prediction business; they were in the reporting business.

Quote-to-bind ratios, combined ratios, loss trend analysis, and claims severity are all processes carried out in hindsight. Disruptive forces, such as climate instability and litigation risk, add to the existing volatility and underscore what appears to be an unbearable disadvantage of a retrospective approach.

Simply put, it's time to reconsider the past. Predictive accuracy and real-time responsiveness aren't options, they are the requirements for competitive advantage.

Turning Data From a Scorekeeper Into a Decision-Maker

We are witnessing a systemic shift. The most ambitious carriers are moving from using analytics as a reporting layer to using it as an operating layer. That's right. They are integrating analytics directly into workflows for essential decision-making.

The measures insurers are incorporating include:

• Pricing risk using dynamic behavioral signals and other external data
• Creating models that provide alerts to underwriters on outlier claims before they escalate
• Shifting claims and other data to a real-time decision engine instead of a passive dashboard/scoresheet

This is not about the use of dashboards. It is all about decisions. It is not a technology story; it is a capability story.

According to McKinsey, AI and advanced analytics generated over €1.2 trillion in global business value in 2023, with insurance being one of the largest beneficiaries. Likewise, Willis Towers Watson found that two-thirds of insurers used predictive analytics, which improved underwriting accuracy, while nearly 60% reported measurable improvements in profitability.

Insight That Lands Where It Matters Most

Most insurers have developed dashboards; yet frontline teams, including underwriters, claims handlers, and fraud analysts, still make decisions with little to no insight into what's relevant across time and space.

Consider this conundrum. The quote-to-bind average for a nationwide carrier means very little if the underwriter is located in Florida during hurricane season, and a claims manager resolving wildfire losses in California requires entirely distinct signals from a pricing analyst in New York.

Predictive models now enable:

• Fraud detection, based on anomalies in claims associated with repair shops and third-party vendors
• Real-time detection of inconsistencies between social media and vendor activities
• Task-specific customization for investigators, managers, or data scientists who each have distinct interfaces but the same data

The insight matters the same way as the measure: it must reach the right person, at the right time, in the right context.

Data Without Accountability Is Just Noise

After analytics has switched from role indifference to role specification, the next step is alignment: discerning noise from signals that lead to outcomes.

Most carriers measure numerous indicators, including loss ratios, the number of claims, and cycle times. Very few connect these measures to an operational decision.

For example:

A regional carrier saw a drop in the quote-to-bind in two ZIP codes. The issue wasn't pricing. The obstructive piece? A lagging user interface that delayed submissions at peak quoting due to complexity. A fast interface adjustment recovered two weeks of quotes.

What it did:

• Loss cost trend segmented by line of business enabled pricing teams to respond more quickly to inflation
• Claims frequency and severity, segmented by peril and geography, surfaced evolving risk clusters
• Renewal lift segmented by service interactions raised flags that were correlated to potential customer retention

What has changed in the examples above? Accountability. When the quote-to-bind dropped 15% in commercial auto, who owned those metrics? What playbook would they follow? What would trigger escalation?

Analytics maturity is not just about measuring more metrics. That's where insurance business process management (BPM) services can make a difference. They don't work as outsourced labor but as structured, accountable extensions of the operating model, creating a closed-loop system in which insight drives expedient and measurable action.

Integrating Analytics Along the Insurance Value Chain

Analytics is shifting from being the bystander to being embedded in the P&C operating model. P&C stakeholders are increasingly using analytics to inform all links along the value chain.

Underwriting:

• AI-driven workflows ingest behavioral, telematics, weather, and macroeconomic data
• The quality of risk selection can improve without compromising or delaying the speed of quoting

Pricing:

• Behavioral pricing models use real-time indicators on driving behaviors, or the usage of a property
• Pricing accuracy has improved with increasingly individualized risk alignment

Claims:

• Anomaly detection becomes integrated into workflows
• Low-risk claims get fast-tracked; edge cases are flagged early

Customer outcomes improve when cycle time is reduced, and leakage due to fraud awareness is diminished.

What is the connective tissue of these examples? It's not software, it's the intelligence at the point of decision. Information should not slow the process of quoting, escalating, or renewing; information should influence it.

Conclusion: Smart Data, Smarter Decisions

The future of P&C insurance will not be claimed by the carrier with the most dashboards but by the carrier with the fastest and smartest decisions.

As risks rise, margins become thinner and capital becomes tight in an emergency, the move from hindsight to foresight has become an existential imperative.

This is not about better reporting. It is about:

• Embedding intelligence into every decision point
• Helping teams act on the data, rather than be buried beneath it
• Pulling together systems that tie metrics to outcomes, not just insight to charts

Because at the end of the day, the winning edge is not more data, it is better decisions, made faster than the competition.

In a business environment increasingly digital, price-driven and not the least bit commoditized, carriers and MGAs are looking for advantages to help grow their businesses and remain competitive. Data has often been the answer but contextualizing both where to look and how to interpret it has always seemed a little fuzzy once that data needs to be harvested for business development rather than operational improvements or insights. 

Understanding what market intelligence insights can be gleaned from available data can provide a road map for where to look for new business, yet this is often the missing piece of many how-to articles. So, let's consider several ways to grow where the data tells you to grow.

What's in a Niche?

There are countless underserved niche markets to be leveraged. For instance, if an MGA or carrier is writing cyber, contractors or commercial auto, the most effective way to identify potential agency partners is to filter through to those who not only specialize in those spaces but also those looking to grow their books of business.

Start with directories or rented lists that can be refined by niche or state. In addition to independent agents affiliated with various networks, there are countless other resources available that track unaffiliated independent insurance agencies, as well. Armed with those lists, filters can be applied to search for particular classes of business. When searching, it's best to try to identify agents with high commercial premiums who also may not have strong market access. Consider both standard industrial classification codes and job titles to help you better target both the right agencies and the right decision makers.

Next, look for geographic whitespace. These are states or counties where market competition is scarce and additional insurance options would be welcome.

Finally, consider reaching out to newly acquired insurance agencies, especially those funded by private equity. Most of these acquisitions were undertaken with a single outcome in mind: growth. These agencies may be looking to add new niche programs that exceed their existing networks or resources. For example, there might be hundreds of agencies in Texas and Florida that write insurance for towing companies, but there may be a healthy percentage of those agencies that lack current or reliable MGA relationships. This can be a great starting point.

Real Opportunities Happen in Real Time

As the global business environment leans hard into AI and other digital solutions, speed has become table stakes for insurers. Applying real-time data, carriers and MGAs can:

• Trigger outreach based on behavior
• Shift sales strategy as trends emerge
• Auto-score submissions to identify what should be fast-tracked to underwriting and what needs more information

Listen to What the Data Says

Your customer relationship management (CRM) system is a treasure trove of business growth opportunities. Consider what is being declined the most or where brokers are frustrated. Use this data to create content that will help them sell better. Personalize your outbound outreach based on agency type and historical interactions. Conduct a series of tests of subject lines, advertising copy, digital marketing messaging and other calls to action to better understand what is capturing the attention of the independent agents you most want to engage.

Importantly, don't try to be all things to all people. In insurance, it's often better to go deep than wide. To do so, make sure to understand and target individual agencies looking to grow rather than only targeting networks that may have broader access to resources and available partners. The good news is that most CRMs will help you personalize your outreach at scale, reducing the legwork necessary to provide personalized messaging and engagement. That same CRM should also be able to provide critical response data so carriers and MGAs can pivot quickly when it's clear a specific approach or message isn't working.

Armed With Data, Work to Sound Human

Many independent agents may be new to insurance. Even if they are seasoned veterans, most have found through their experience that insurance jargon—whether using it or receiving it—isn't helpful. These agents are trying to grow their business, and investing time and effort to sift through fuzzy carrier or MGA marketing language distracts from their growth efforts. Moreover, some of the language used in insurance marketing materials is both generic and unhelpful. Countless MGAs say they are tech-enabled. This conveys neither helpful information nor clarity, and it could be argued that any MGA in 2025 that isn't tech-enabled is an MGA ready to go out of business. Such phrasing isn't a differentiator; it's just filler.

Market intelligence can help carriers and MGAs identify the aforementioned whitespace where competitors are absent. This same data can help insurers get real about pain points for agents, like slow quoting or lack of access to niche programs. Considering this type of data helps insurers sound more human. Agents will respond accordingly because their clients are also seeking clarity.

This humanistic approach to messaging needs to carry over to the branding of carriers and MGAs as well. On the AI front, far too many insurers present themselves as providing:

• AI-powered solutions
• Advanced analytics and machine learning
• Tech-driven decision making
• Smart automation at scale
• Next-generation risk assessment tools

Agents, whose reputations are on the line and who are increasingly working to build relationships with clients as trusted advisors, want assurances there are still humans behind the machines, providing underwriting insights and checking the output of any AI tools.

Lastly, it's becoming increasingly clear that specialization matters in insurance. Those carriers and MGAs—and insurtechs, for that matter—that present themselves as one-stop shops for every agent's needs are looked at with skepticism. When you attempt to present yourself as a Jack of all trades, today's savvy and trust-focused agents take that as a sign your shop is a master of none.

Are You Using Data Well?

Insurers winning new business today aren't guessing. They are using data to move faster, speak more clearly and reach the right agencies at the right time. Working with a data analytics or marketing specialist can help refine the process of applying data to your business development strategy. Whether you are a growth-stage insurtech, a specialty MGA or a P&C insurance carrier looking to expand reach, the right tools and experts are available, accessible and necessary. You simply need to determine how best to grow where the data tells you.

The insurance industry is undergoing a profound transformation in 2025, powered by cutting-edge technologies designed to streamline operations, improve customer experiences, and mitigate risks more accurately. From artificial intelligence to blockchain, the future of insurance lies in embracing innovation. In this article, we explore the top 10 emerging insurance technologies in 2025 that are redefining the landscape for insurers and policyholders alike.

1. Artificial intelligence (AI) and machine learning

AI and ML are at the forefront of insurance innovation in 2025. They're being used to automate claims processing, detect fraud, and enhance customer service via chatbots and virtual assistants. Insurers are now using predictive analytics to assess risks more accurately and tailor policies to individual needs.

Key Benefits:

• Reduced operational costs
• Improved underwriting accuracy
• Personalized customer interactions

2. Blockchain technology

Blockchain is improving transparency and security in the insurance industry. Smart contracts allow for automated policy execution and claims processing, reducing disputes and human error. This is especially beneficial in life insurance and reinsurance where record accuracy is crucial.

Use Cases:

• Decentralized claims verification
• Real-time auditing
• Preventing double-dipping fraud

3. Telematics and usage-based insurance (UBI)

Telematics devices installed in vehicles provide real-time data on driving behavior. This tech is revolutionizing auto insurance by enabling usage-based insurance, where premiums are based on how safely you drive rather than static factors like age or location.

Advantages:

• Fairer premium pricing
• Enhanced driver safety
• Real-time accident response

4. Internet of things (IoT)

IoT-powered devices—like smart home sensors and wearable fitness trackers—are giving insurers access to real-time data that helps in proactive risk management. For example, a smart water leak sensor can notify both the homeowner and the insurer before major damage occurs.

Benefits:

• Fewer claims through early warnings
• More precise risk assessments
• Improved customer satisfaction

5. Robotic process automation (RPA)

RPA is automating repetitive back-office tasks such as policy issuance, data entry, and compliance reporting. In 2025, RPA tools are helping insurers save time and money while increasing accuracy and efficiency across workflows.

RPA Use Cases:

• Automated claims handling
• Real-time document verification
• Policy renewals and updates

6. Predictive analytics

Using big data and machine learning, predictive analytics can forecast future claims, identify high-risk customers, and refine underwriting processes. In 2025, it's also helping detect fraudulent behavior before it happens, which saves billions in potential losses.

Why It Matters:

• Better customer segmentation
• Fraud prediction and prevention
• Risk scoring and policy optimization

7. Chatbots and virtual assistants

AI-powered chatbots are more sophisticated in 2025, handling everything from policy inquiries to claims submissions. These tools offer 24/7 customer support and reduce the need for human agents, while also ensuring consistency in communication.

Main Features:

• Instant response time
• Multilingual support
• Integration with CRM systems

8. Augmented reality (AR) and virtual reality (VR)

While still emerging, AR and VR are being explored for claims processing and training. For instance, adjusters can use AR tools to assess damage remotely, and companies are using VR for employee training simulations in hazardous scenarios.

Innovative Uses:

• Virtual home inspections
• Risk scenario training
• Immersive customer engagement

9. Embedded insurance platforms

Embedded insurance allows coverage to be offered seamlessly at the point of sale—like travel insurance during flight booking or gadget insurance during an electronics purchase. In 2025, this model is streamlining policy purchases and expanding reach.

Notable Impacts:

• Frictionless customer experience
• Increased policy sales
• Better market penetration

10. Digital identity and biometric verification

With digital fraud on the rise, insurers are adopting biometric verification methods like facial recognition and fingerprint scanning to confirm user identities. This tech not only ensures security but also simplifies customer onboarding.

Security Enhancements:

• Faster KYC processes
• Reduced identity theft
• Seamless login and access

Final Thoughts

As we move through 2025, it's clear that insurance technology is no longer just about efficiency—it's about redefining how insurance is created, sold, and experienced. Companies that embrace these innovations will not only gain a competitive edge but also foster trust and loyalty among modern policyholders.

To stay relevant in this fast-evolving ecosystem, insurers must invest in digital transformation, cultivate tech partnerships, and prioritize customer-first innovation. The top 10 insurance technologies in 2025 aren't just trends—they're strategic necessities.

Even as so much has changed so quickly, including the entire insurance, automotive and mobility ecosystem, market leaders and their long-time trusted partners are better positioned than ever to weather the storm, adapt and succeed.

The ecosystem includes auto insurers, agents, brokers, car manufacturers, dealers and the automotive aftermarket. The extended auto physical damage supply chain with which they all interact includes roadside, emergency response, towing and temporary rental car service providers. Related and interdependent segments include connected services, telematics-based and other IoT and sensor-based programs.

But driven by sudden and dramatic changes in socioeconomics, politics, technology, and consumer expectations, almost all of the historical financial models that applied for so long among the participants are suddenly unrealistic and unworkable. The relationships and partnerships, however, are more relevant than ever.

What all of this means is that an entirely new set of business models, relationships, products, risk management and support services need to be developed, negotiated, and implemented. No small feat! What appears to be friction emerging between the various ecosystem participants is actually evidence of this transformation evolving. Furthermore, consumers are experiencing a new normal when it comes to insurance – high premiums, less protection and caution about making claims for fear of surcharges or worse. Collaboration between claim ecosystem players, in particular, is more important than ever and is being put to the test.

Auto Insurance Economy

One of the bigger industry segments that best illustrates these challenges and also presents many important new opportunities is the $390 billion U.S. auto insurance segment. This "insurance economy" is composed of thousands of supply chain participants and industry trading partners serving a common customer base of about 215 million insured motorists who are involved in ~22 million auto accidents annually.

Since 2022, as inflation drove up costs for all participants, auto insurers were among the first to recognize the need for aggressive rate increases. Early warning signs emerged with steep increases in auto body repair labor rates due to widespread repair technician shortages similar to numerous other service industries in the post-COVID era. Auto insurance premiums have increased 49% since 2019, resulting in 57% of auto insurance customers shopping for new policies in 2024. These increases drove auto insurance policy shopping to unprecedented heights and increased the already stiff competition for market share. In fact, more than half (57%) of auto insurance customers have shopped for a new policy in the past year, the highest rate ever recorded by J.D. Power. Although rate increases are slowing, in 2025 they are still developing and being digested by consumers.

Inevitably, ecosystem participants also began to feel the pinch of increased costs of just about everything and started passing them up the supply chain, putting further pressure on all participants and ultimately reaching consumers.

Auto Physical Damage (APD) Ecosystem/Rental Car Coverage

The $250 billion auto physical damage ecosystem is a prime example of how symbiotic these segments have become. And one critical subset of this ecosystem - rental reimbursement coverage – is one that displays high relevancy and interdependency. It provides auto insurers, collision repairers, and policyholders with temporary transportation while accident and theft claims are being processed. Surprisingly, according to recently published 2025 U.S. Auto Insurance Trends Report by LexisNexis Risk Solutions, only ~ 40% of eligible auto policies carry this relatively inexpensive and high-value coverage, which becomes obvious whenever a driver must pay out-of-pocket for a rental vehicle. The average duration of these temporary rentals is currently ~16 days, and the average daily retail rate ~$61.50, totaling almost $1,000 out-of-pocket. Rental car coverage typically costs ~$30/year.

Total loss claims, which generate significant opportunities for lengthier replacement rentals have soared to 29% of claims in 2024 from only 17% in 2018, according to LexisNexis Risk Solutions. It says: 

"Now with almost 30% of collision claims ending in a total loss, carriers need to place an even greater focus on speed and customer satisfaction in this process, especially because our research shows that approximately 40% of vehicles with full coverage (liability and physical damage) opted to purchase rental reimbursement coverage."

This rental car protection gap represents a particularly high-value opportunity for auto insurers, agents and brokers to educate policyholders on the value of this protection and differentiate their customer care and service levels from competitors. Rising rental costs are also a call to action for insurers to adjust daily policy limits to match new market norms.

Despite alternative modes of mobility, renting a car is essential during the repair process and a huge source of dissatisfaction expressed by policyholders when they learn so-called "full coverage" may not include this valuable protection. This scenario leads to poor claim experiences and often has downstream consequences in terms of customer retention rates.

Partnerships Matter More Than Ever

Recently, many of the same economic factors that caused auto insurers to raise premiums have begun to affect supply chain partners such as rental car companies. These include an aging car parc, tariffs and higher vehicle acquisition costs, OEM production constraints, advancing vehicle technologies, higher repair costs, and evolving global economic conditions.

Adding to rental companies' operating challenges is the marked reduction in claims filings, which depresses rental car transactions and revenue. In addition to raising their deductibles, many consumers have opted to remove rental reimbursement coverage to lower costs, further contributing to a decline in rental transactions.

BUSINESS MODEL OPPORTUNITIES

The changing marketplace also presents new opportunities in distribution. New channels such as direct-to-consumer, point of sale and embedded insurance are rapidly emerging with support from retailers seeking incremental revenues and customer engagement and digital-forward consumers.

Hyper-personalized, parametric and episodic insurance products are also meeting consumer appetites and demand and delivering a more dynamic and flexible customer experience.

PRODUCTS & SERVICES OPPORTUNITIES

Protection gaps have become much more visible as extreme weather events created unprecedented property damage, which exposed extensive lack of coverage. Insurance-to-value calculation based on historical loss data is no longer relevant. Carriers that can address and cure these gaps will be tomorrow's market leaders

The auto insurance market is out of sync. New products are needed now. Telematics, usage-based insurance and shared value programs are one good answe,r but the industry needs to address several related hurdles, including data ownership and control. Claim process designs must move from historical to real time to predictive in order to maximize potential.

In general, we need to encourage the industry to shift from a repair-and-replace to a predict-and-prevent mindset.

CLAIMS & TECHNOLOGY OPPORTUNITIES

A large number of obvious opportunities between ecosystem partners exist but have not been aggressively explored or adopted for a variety of reasons.

• Data privacy concerns need to be eliminated, and obvious opt in/out choice need to be addressed, paving the way to unleash the transformative power of telematics
• Misaligned business models, while sharing the very same customer base, have been a constant, and the problem is best assuaged with negotiated pricing agreements and balance of containing total cost of claims, shared
• Real-time accident management, emergency response, crash detection and e-FNOL could transform the auto insurance market and unleash compelling value and customer service but are minimally deployed
• Straight-through processing of claims remains a challenging but enticing design model, and platform providers integrated with cloud-based claims management systems may be getting closer to enabling it
• AI needs and deserves more careful, thoughtful exploration to unlock its seemingly unlimited potential

PARTNERSHIPS, COLLABORATION AND TRUST MATTER MORE THAN EVER

Recreating a relevant insurance economy will require all the trust and goodwill fostered by relationships over the years. Even in the midst of such extensive disruption, some values remain constant.