Adoption Filings Present a Key Challenge for Insurance Companies

Adoption filings often involve changes to rates, coverages, or both—making accurate management critical. “They also touch many stakeholders at an insurance company,” Perr explained. “These include the regulatory team that needs to know what filings to prepare, the marketing group that needs to communicate with policyholders, the claims department that needs to track coverage changes, and the IT department that must program new rates.” Because each department depends on timely and accurate information, sequencing becomes essential. Missteps in coordination can lead to delayed implementations, potential fines, and reputational risk for insurers.

How Perr&Knight Supports AAIS Members in Managing Adoption Filings

Perr&Knight helps insurers manage adoption filings efficiently through a blend of consulting expertise and proprietary technology. “Our software provides customizable workflows tailored to each company’s business needs and bureau affiliations,” Perr said. Their platform manages the sequencing of regulatory actions, submissions, and notifications—ensuring all stakeholders receive the information they need, when they need it. All activities are documented and archived for easy retrieval and accountability. Built-in automation further enhances speed and precision, and Perr&Knight continues to invest in ongoing improvements to their software.

The Advantages of an Automated Approach

Many insurance companies rely on small compliance teams—sometimes just one person—to manage their bureau adoption process. Perr noted that this can create a single-point dependency risk: if a key team member leaves or is out for an extended period, their institutional knowledge can disappear with them. Perr&Knight’s automated solution eliminates that risk by driving workflows, tasks, and communications through a proven, structured framework. “Our software ensures the process doesn’t rely on one person,” Perr said. “It’s built on years of best practices to deliver consistency and reliability every time.”

About Perr&Knight

Perr&Knight’s consultants and proprietary software work hand in hand with clients and their internal teams. Solutions are tailored to each organization’s specific needs—whether they choose to license the software, engage consultants, or use a combination of both. “In every case, our focus is on delivering services with speed, precision, and value,” Perr shared.

Learn more at perrknight.com.

To view the full interview with Tim Perr, click on the video above.

The post Efficient Adoption Filings and Bureau Monitoring for Insurers first appeared on AAIS.

If you’ve been in the industry long enough, the current AI conversation may feel reminiscent of the Agile revolution that swept through IT departments in the early 2000s. Back then, Agile was the hot topic. Today, AI has taken its place. But the parallels are more than superficial. The missteps many insurers made in their Agile transformations offer cautionary lessons—and opportunities—for how we approach AI implementation now.

Agile: A Cautionary Tale of Incomplete Transformation

Agile methodology was born around the turn of the century out of a desire to overhaul the pre-existing Waterfall methodology known for rigid, documentation-heavy development processes. It promised more alignment with the customer, faster delivery, higher quality, continuous feedback, and more customer-centric outcomes. The Agile Manifesto, published in 2001, was a rallying cry for a better way to develop software—one rooted in iteration, collaboration, and adaptability.

Fast-forward to today: ask any insurer if they’ve adopted Agile, and most will say yes. But peel back the label, and the reality is more nuanced. What many organizations call Agile is, in practice, Hybrid Agile—an undefined blend of Agile and traditional Waterfall practices.

Why Did This Happen?

Because most companies didn’t fully commit. They adopted Agile ceremonies—standups, sprints, backlogs—without embracing the cultural and operational changes required to make Agile truly work. Agile wasn’t just a process shift; it demanded new ways of thinking about leadership, accountability, team structure, and value delivery. But many insurance organizations never invested in the change management necessary to drive that deeper transformation.

The result? Agile became a buzzword. A checkbox. And many of the benefits it promised—greater speed, higher quality, better alignment with customer needs—were never fully realized. Project failures, delayed implementations, and disappointing outcomes became common, despite “Agile” being the official methodology.

Human Resistance to Change

To understand why Agile adoption fell short, we need to acknowledge a simple truth: change is hard. Humans are wired for comfort and predictability. Even when change is necessary—or desirable—it can be deeply uncomfortable. As anyone who’s ever tried (and failed) to stick with a new diet or exercise routine knows, sustaining change is exponentially more difficult than starting it.

Now multiply that resistance across an enterprise. You’re not just asking individuals to change; you’re asking teams, departments, and entire business units to work differently. And if only some adopt the new approach while others resist, the result is friction and dysfunction.

This is why true transformation requires more than just new tools or processes—it requires a deliberate, strategic approach to people and culture. In other words: change management.

Change Management: The Missing Link

Change management isn’t about IT, or even methodology. It’s about people. It’s about psychology, motivation, leadership, and learning. It’s a discipline in its own right—just like finance, operations, or claims.

Agile transformations that succeeded had one thing in common: they were supported by formal change management strategies, often led by dedicated teams that reported directly to the C-suite. These teams didn’t just roll out new software practices; they enabled deep organizational change. They aligned HR, finance, operations, and leadership to a shared vision. They provided training, coaching, communication, and cultural reinforcement. In short, they treated transformation as a human endeavor.

AI: A New Era, Same Story?

Today, we’re on the brink of another transformation—this time driven by AI. And once again, the signs are familiar. AI is everywhere in the headlines. Executives are eager to say their company is “doing AI.” Vendors are marketing AI-powered solutions for every pain point. But in many cases, the implementation is superficial. AI, like Agile, is not a plug-and-play solution. It’s a capability that must be aligned with real business problems. It requires not just technical infrastructure, but organizational readiness. It requires oversight, governance, ethical considerations, and cultural adaptation. And, most critically, it requires humans—skilled, empowered, and change-ready humans. Without that, AI risks becoming yet another buzzword. Another checkbox. Another opportunity squandered.

Learning from the Past to Succeed in the Future

If the insurance industry is to harness the full power of AI, we must treat its implementation not as a technical rollout, but as a business transformation. We must invest in the people side of change—just as we should have with Agile.

That means:

• Appointing change leaders who report into the C-suite and have the authority to drive cross-functional alignment.
• Training managers and teams not just on AI tools, but on new ways of working, decision-making, and collaboration.
• Defining success criteria that go beyond technical deployment to include adoption, impact, and cultural fit.
• Creating space for learning and iteration, understanding that transformation is a journey, not a sprint.

At INFORCE, we’ve seen firsthand how critical the human element is to technology implementation success. Effective systems integration implementations require more than IT know-how; they need seasoned leadership, effective change management, and client-centric people. That’s why we created our INFORCE Academy: to train our team on systems integration and to develop the cultural, technical, and behavioral skills needed for transformation. We specialize in building high-performance teams that can lead and sustain change, from Agile to AI and beyond.

Final Thoughts

As we enter the AI era, the stakes are high. This technology has the potential to redefine how we serve customers, manage risk, and create value. But it won’t succeed on its own.

To unlock its promise, we must lead with intention. Let’s learn from our past. Let’s prioritize people, process, and purpose. Let’s approach AI not just as a tool, but as a transformation. That’s how we’ll turn AI from a buzzword into a breakthrough.

The post Digital Transformation in Insurance: From Agile to AI first appeared on AAIS.

Why Insurance Still Relies on Paper and the Challenges It Creates

“I think one of the primary reasons insurance carriers have been so reliant on paper for so long is due to the industry being so deeply rooted in everything from regulatory requirements and compliance to legacy systems,” Eubank explained. Processes like claims handling, underwriting, and policy issuance involve multiple parties, signatures, and compliance checks—many of which have historically required physical documentation for legal or audit purposes.

In addition, customer preferences play a role. “A significant portion of the customer base—especially in commercial and life insurance—is older and still prefers or expects paper-based communication,” Eubank said. But the cost of staying on paper is high. Eubank highlighted several major challenges for carriers:

• Operational inefficiency: Manual processes are slow and prone to error.
• High costs: Storing, mailing, and managing paper adds up quickly.
• Data fragmentation: Paper documents are unstructured and hard to integrate into digital systems.
• Poor customer experience: Clients expect digital-first interactions, and paper often falls short.

Practical Steps Toward Paper-Free Workflows for Insurers

While a full transformation may seem daunting, insurers don’t need to overhaul everything at once. “Start with high-impact areas where paper slows down operations—like claims intake or third-party notifications,” Eubank advised. From there, build momentum by introducing simple digital solutions including:

• Replace paper forms with online applications.
• Adopt secure electronic signature tools like DocuSign.
• Use OCR (optical character recognition) to digitize incoming documents.
• Train staff and customers to support the transition away from paper.

How Carriers Can View Automation as an Ecosystem for Future-Proof Operations

To truly future-proof operations, Eubank encourages insurers to think of automation as a long-term ecosystem—not a one-time project. “Start by promoting cross-functional collaboration—get operations and business leaders working together to prioritize automation that serves shared goals,” he said.

He also recommends a modular approach, where carriers implement flexible, plug-and-play components—like for claims triage or fraud detection—without disrupting their core systems. Artificial intelligence (AI) and machine learning can also help automate decision-making in areas like risk scoring, customer service, and document classification—particularly when integrated into modern systems. “Lastly, it’s about leveraging APIs and web services within a cloud environment,” Eubank said. “That kind of architecture ensures any future tools can integrate easily and support a land-and-expand strategy.”

Visit LenderDock.com to explore how insurers can streamline workflows and reduce paper dependency with digital verification and automation solutions.

The post Modernizing Automation in Insurance Toward a Paper-Free Future first appeared on AAIS.

The answer lies not in the existence of digital tools, but in how intelligently and efficiently they’re being used. Today, the conversation is no longer about simply digitizing manual processes—it’s about empowering insurers to innovate faster, smarter, and more affordably. This is where low-code/no-code (LCNC) platforms and artificial intelligence (AI) are becoming the real game changers.

Rethinking Digital Transformation: Beyond Just “Going Digital”

Digital transformation has often been loosely defined—ranging from replacing paper processes with software to overhauling core legacy systems. At its heart, it has always been about operational efficiency—streamlining workflows, cutting costs, and keeping pace with a rapidly evolving market.

However, many so-called “transformative” efforts have been largely incremental. Migrating from spreadsheets to web-based forms or deploying an off-the-shelf policy admin system isn’t transformation—it’s digitization.

What’s missing from many of these initiatives is agility—the ability to quickly roll out new products, adapt to regulatory changes, or respond to evolving customer behavior. That’s where LCNC platforms enter the picture.

Digital Evolution with Low-Code/No-Code

Low-code and no-code technologies mark the industry’s shift from digital transformation to digital evolution—where speed, flexibility, and configurability take center stage.

Unlike traditional software development, LCNC platforms enable business users, product teams, and IT to rapidly design, deploy, and modify applications with minimal coding effort. This evolution has redefined how insurers bring products to the market in many ways:

• New insurance products configured in weeks, not months
• Product launches driven by business teams instead of IT avoiding resource conflicts and backlogs
• Workflow automation and rule changes without full-scale deployments

In recent years, insurers have adopted LCNC platforms to build reinsurance admin systems, launch supplemental health plans, and even create agent compliance modules—all with a fraction of the time and cost of traditional development.

It’s no longer about replacing old systems; it’s about evolving them intelligently with scalable, modular components built for continuous innovation.

The Rise of AI: From Digital Evolution to Digital Intelligence

As insurers evolve their infrastructure with LCNC tools, the next logical leap is toward digital intelligence—powered by AI and real-time data.

AI is no longer just about chatbots or predictive models. It’s about creating systems that adapt, learn, and evolve on their own. In the insurance context, this means:

• Self-adjusting rating models based on real-time behavioral data
• Dynamic underwriting that adapts with every new data point
• Claims triage and fraud detection fueled by machine learning (ML)

While usage-based insurance (UBI) scratched the surface of this concept, it failed to make systems truly intelligent. The models were still static. True digital intelligence requires constant data loops, where AI doesn’t just observe behavior but learns from it and adjusts system logic in real time.

Imagine a platform that automatically recalibrates premiums, deductibles, or policy terms based on live behavioral insights—without human intervention. That’s the future AI enables.

The Fusion of LCNC and AI: A New Operating Model for Insurance

The real innovation happens when LCNC and AI converge. Together, they form a powerful combination:

• Low-code platforms offer speed, modularity, and democratization of development
• AI algorithms bring automation, personalization, and real-time decision-making

This fusion allows insurers to:

• Build AI-powered rating engines without writing complex code
• Train and deploy ML models inside workflow apps configured by business users
• Continuously optimize customer journeys, pricing, and claims processes—without costly development cycles

This isn’t theoretical. Leading insurers today are already deploying AI-infused, LCNC-driven systems to streamline product launches, manage agent compliance, and deliver personalized policyholder experiences on a scale.

Conclusion: Stop Talking About Transformation—Start Driving Intelligence

The term digital transformation has served its purpose. But it’s time to shift focus. Insurers must now embrace a new blueprint—one that starts with LCNC evolution and matures into digital intelligence through AI.

While the transformation journey may have begun years ago, its real momentum is just beginning. With LCNC and AI, the insurance industry is finally equipped to move beyond catch-up mode and into a future of continuous innovation, real-time agility, and intelligent automation.

The finish line isn’t just digitization. It’s self-optimizing, intelligent systems that learn, adapt, and grow with your business.

To learn more about InsurIQ’s transformative technology solutions specializing in policy administration, agent compliance management, and digital insurance sales and enrollment, visit insuriq.com.

The post How Low-Code/No-Code and AI Are Digitally Transforming Insurance first appeared on AAIS.

Today, change in insurance is not only necessary but inevitable. As customer expectations grow and technology advances, insurers must rethink how they handle policy verification, lienholder updates, and compliance notification processes. Automating policy information in real time can reduce administrative burdens and enhance efficiency across the industry.

The Industry Challenge — Inefficiencies in Lienholder Communications

The Burden on Insurers

For decades, insurance carriers have been overwhelmed with thousands of daily policy verification requests. Banks, credit unions, and mortgage lenders continuously seek confirmation of policy details, creating a relentless administrative burden in the form of unnecessary phone calls, emails, faxes, and postal mail. Without automation, insurers must rely on human capital to manually handle these inquiries, leading to wasted time, excessive costs, and the potential for compliance risks. Conservative estimates claim that the insurance industry handles over 500 million phone calls annually. The sheer volume of requests diverts attention from more critical business functions, making it difficult for insurers to focus on their primary objective—providing high-quality service to policyholders.

The Cost of Existing Solutions

Many traditional industry solutions, such as Interactive Voice Response (IVR) systems, outsourced call centers, and Electronic Data Interchange (EDI) data sharing, require significant resources to maintain. Despite their widespread use, these approaches still contribute to operational inefficiencies. One of the largest burdens that both the insurance and banking communities face is managing millions of daily tasks, processes, and deliverables via paper documents and spreadsheets. The costs associated with printing, postage, and processing physical paper disrupt the ability to adopt more cost-effective, agile, and scalable digital solutions. Insurers relying on legacy systems often find them inflexible, unable to support real-time updates, and ineffective at addressing the growing demand for seamless communication between financial institutions and insurance providers. Without an efficient digital solution, insurers remain stuck in a cycle of administrative burden, missed opportunities for growth, and declining customer satisfaction.

The Roadblocks to Digital Transformation

Breaking into a modernized approach to lienholder communications comes with challenges. The sector operates on deeply entrenched legacy systems, and insurers have historically been hesitant to deviate from familiar technologies. Concerns about workflow disruptions, outdated IT infrastructures, and the complexity of integrating new solutions have made digital transformation a slow and cautious process. Additionally, regulatory and compliance considerations add another layer of complexity, making insurers wary of adopting new technologies without proven security and efficiency measures. But, these challenges, while significant, should not deter insurers from embracing automation – especially when the right solutions can seamlessly integrate with existing workflows.

A Digital Solution for Lienholder Process Automation

As the demand for efficiency and automation increases, insurers must rethink how they handle policy verification, lienholder updates, and compliance notifications. Implementing a fully digital, real-time system can significantly reduce administrative burdens, improve response times, and enhance operational efficiency across the industry.

LenderDock addresses this issue by providing a SaaS platform that automates lienholder communication and the exchange of policy data. By eliminating manual processes, LenderDock streamlines policy verification and ensures real-time updates for financial institutions. Their key features include:

• VERiFi™ – Allows banks, lenders, and financial third parties to self-serve online policy verification requests in real-time, eliminating the need for phone calls, emails, and faxes.
• LIENSure™ – Provides digital updates and correction requests for mortgagee clauses and loss-payee information, ensuring process efficiency and data integrity.
• LENDERDocs™ – Enables mortgagees and lenders to access policy documentation instantly, including proof of insurance and paid receipts, fast-tracking information sharing.

The shift toward automated lienholder communications isn’t just an operational upgrade—it’s a strategic necessity. By reducing administrative overhead, insurers can lower costs, ensure compliance, and reallocate valuable resources to higher-value initiatives that drive growth and innovation.

As technology continues to advance, solutions like LenderDock will play a critical role in shaping the future of insurance operations. The industry is at a turning point—those who embrace digital transformation will lead the way, while those who resist may struggle to keep up.

Learn more at LenderDock.com.

The post Automating Lienholder Communications to Boost Efficiency first appeared on AAIS.

Why AI Is a Hot Topic Across the Insurance Industry
While predictive modeling has long been a part of insurance operations, the rise of advanced AI technologies, including generative models such as GPT, has created new possibilities. Habayeb shared, “Once we got into the world where GPT and generative happen, you now had non-technical people, non-actuaries, and business leaders, saying, ‘I want to build a model to do this thing.'” This democratization of AI technology has sparked growing interest in how AI can solve insurance’s unique challenges, especially in a low-margin, often manual environment. Aufenthie further emphasized AI’s mainstream rise, noting that as AI becomes more accessible, it signals a significant shift in its potential across the industry.

Regulatory Perspective of AI in the Insurance Industry
The growing role of AI in insurance has caught the attention of regulators. This can be proven by the NAIC Innovation Cybersecurity and Technology (H) Committee, which was created to address AI and its potential to transform the industry, Block explained. “Insurance is an industry that is sort of perfect for the use of technology,” she noted. Regulators are focused on ensuring AI is applied within existing frameworks, though the complexity of integrating emerging technologies with traditional regulations remains a key challenge.

Consumer-Facing Applications of AI in the Insurance Industry
Regulators are particularly concerned with AI’s consumer-facing applications, such as in underwriting, fraud detection, and claims processing. Block referenced surveys in the auto insurance sector, highlighting a significant gap between the intention to use AI and its actual implementation. “The numbers went from… 80-90% are thinking about using it… to 20% are actually using it,” she revealed. Despite this, areas like claims, fraud detection, and marketing are seeing more widespread AI adoption.

Biggest Hurdles That Insurers Face in Advancing Their AI Journeys
One of the largest obstacles for insurers, especially smaller carriers, is the reliance on third-party vendors for AI solutions. While large insurers may have the resources to build and tailor their own AI systems, smaller companies often lack the infrastructure to do so. Habayeb advised smaller carriers to manage relationships with external vendors to implement AI solutions effectively.

The AI Bulletin: Its Purpose and How It Hopes to Protect the Consumer
The AI Bulletin, passed about a year ago, serves as a crucial step toward regulating AI in the insurance industry. Block explained that the bulletin is not a statute but provides clarity on regulators’ expectations for AI use within the context of existing laws. “It aims to ensure that AI is used responsibly and transparently, focusing on protecting consumers,” she explained. The bulletin addresses concerns such as AI-driven pricing, ensuring fairness, and avoiding discriminatory practices in line with established regulations. However, Block noted that this is only the beginning. Future legislative efforts may further refine AI governance, particularly around third-party data and models.

Managing State-by-State Differences With AI Compliance
Habayeb explained that navigating AI regulations is complicated by the different approaches taken by individual states. “If a state establishes their AI risk management program in 2025, the degree to which you have robust testing, validation, and continuous monitoring of the high-risk things… might take a little bit more time,” he said. States like New York, Colorado, and California have already implemented their own policies that differ from the AI Bulletin’s guidelines, creating additional challenges for insurers trying to maintain compliance. Habayeb advised insurers to develop a consistent, repeatable structure for managing AI compliance, with a focus on the highest-risk use cases in 2025. “Have some consistent structure for knowing that it’s happening, proving that you evaluated its appropriateness, and that you’re keeping an eye on it,” he said.

NIST: Finding a Framework and Importance of Investing in AI Now for the Future
The NIST AI Risk Management Framework (AI RMF) offers a critical tool for companies seeking to ensure responsible AI use. While Habayeb noted that the framework is still evolving, he praised its structure as a valuable guide. He emphasized that insurance companies should start aligning AI governance with existing industry standards, such as actuarial practices.

Steps for Insurers to Align With the Bulletin’s Goals
To align with the AI Bulletin’s guidelines, Block emphasized that insurers must assemble the right team to build a practical governance structure. “You need all the people in the room to build a practical governance structure so that it makes sense,” she said. After the team is in place, insurers should focus on defining clear internal policies that outline the roles, responsibilities, and risk management strategies related to AI. Habayeb added that insurers should be ready to explain how their AI systems align with corporate policies and regulatory expectations. “You want to see those connected, right? How have you affected that policy?” he asked, reinforcing the need for transparency and clear communication with regulators.

To view the full webinar, click on the video above.

Questions? Please reach out to any of the featured speakers through the contact information below.

Chris Aufenthie
Director of Regulatory Filings, AAIS
chrisa@aaisonline.com

Anthony Habayeb
Co-Founder & CEO, Monitaur
anthony@monitaur.ai

Mary Block
Director of Insurance Regulation, Vermont Department of Financial Regulation
mary.block@vermont.gov

The post Webinar: Advancing AI in Insurance: Navigating Regulatory Trends first appeared on AAIS.

The Evolving Landscape of Homeowners Insurance

The homeowners insurance market is under intense pressure due to a combination of factors:

• Population Shifts and Real Estate Development: Growing populations and new developments in high-risk regions exacerbate underwriting difficulties.
• Unpredictable Weather: Increased frequency and severity of weather-related events make risk prediction more complex.
• Market Disruptions: Factors like social inflation, macroeconomic pressures, rapid innovation demands, and heightened competition are intensifying the strain on insurers. Litigation management costs surged 19% from 2018 to 2023 for the combined P&C sector, reflecting an approximate $24 billion loss adjustment expense (LAE).

These issues are compounded by a surge in consumer insurance shopping, a rise in higher-risk policies, and an increase in long-time policyholders switching carriers. Insurers are forced to make tough choices, such as raising premiums, exiting markets, or discontinuing certain coverage lines. These pressures are preventing the industry from achieving necessary positive outcomes critical for future profitability.

The Burden on Underwriters

Amid the ongoing market uncertainty, underwriters particularly are facing significant challenges:

• Overwhelming Submission Volume: An influx of submissions strains underwriting resources.
• Inaccurate Risk Prediction: Traditional methods based on demographic data and zip codes are proving to be inadequate, necessitating more precise risk assessment tools.
• Data Quality Issues: Poor-quality or unstructured data and manual processes further complicate risk assessment.

Underwriters are questioning why the process is so burdensome and how it can be alleviated. The crux of the issue is underwriting profitability, crucial for maintaining healthy bottom-line results. Fortunately, there are modern solutions to address these goals.

Modernize Risk Assessments with Behavioral Predictions

Traditional underwriting relies heavily on demographic data and location. Enhanced risk assessment goes beyond traditional methods such as ZIP Codes and credit scores by incorporating individual behavior and decision-making patterns.

Underwriters today can access a wealth of data outside traditional risk variables to deepen the understanding of their policyholder’s risk profile. By utilizing AI-powered behavioral predictions, which incorporate information about consumer activity, interests, buying choices, etc., underwriters can more accurately predict outcomes relevant to insurance underwriting performance.

To remain competitive, insurers must invest in this digital transformation and reduce reliance on complex, manual processes. In fact, a McKinsey analysis found that the most successful carriers are those leveraging the latest technologies to optimize underwriting capabilities. Enhanced approaches for success involve adding:

• Comprehensive Risk Profiles: A detailed view of prospective and existing policyholders to start transforming underwriting workflow.
• AI and Person-Level Insights: Using AI to access powerful person-level insights about customers and their individual risk propensities, which directly impact underwriting profitability.

Leveraging Behavioral Predictions Across the Insurance Value Chain

Using behavioral intelligence represents a transformative shift in insurance underwriting. By incorporating policyholder behaviors into risk evaluation, insurers gain a deeper understanding of risk profiles and individual customer nuances, leading to a more customer-centric approach to coverage. This intelligence offers specific predictions that enhance risk assessment:

• Identifying policyholders with a high propensity to seek an attorney at first notice of loss (FNOL) or a likelihood to litigate.

• Predicting claims frequency and severity for current policyholders and prospective customers.

• Assessing the likelihood of non-payment or early cancellation.
• Determining which prospects are most likely to convert to new customers and predicting their lifetime value.

When insurers identify policyholders with a higher propensity for risk, they can proactively manage these policies more precisely using unique identifiers. Meanwhile, they can handle other policies based on different qualifiers that indicate varying levels of risk. This becomes a game-changer for insurers’ ability to predict and review for underwriting.

Empowering Underwriters

Underwriters are tasked with building profitable books with targeted risk profiles. To be successful, they need to make quick, effective, and accurate assessments of the profitability of each policyholder. However, they are often working with limited or convoluted information and are under considerable time constraints.

Utilizing person-level intelligence in underwriting allows underwriters to focus their craft and expertise on the most complex risks. By integrating person-level intelligence, insurers can:

• Identify High-Risk Insureds Early: Early identification of high-risk individuals allows underwriters to focus on the most complex cases, improving resource allocation.
• Improve Efficiency: With simply using a name and address, significant risk insights can be obtained in seconds.
• Enhance Risk Handling: Focus on complex cases while automating the handling of low-risk applications, improving overall workflow efficiency.
• Provide Personalized Service: Offer coverage tailored to individual risk profiles, moving beyond traditional factors like location and credit scores.

Future-Proofing Insurance Underwriting

For P&C insurers, integrating person-level intelligence into underwriting processes offers a more precise and complete view of the policyholder risk profile. This approach helps insurers better prepare for uncertainty, respond to market volatility, avoid adverse selection, and achieve profitable, sustainable growth. These AI-powered behavioral predictions empower insurers to:

• Predict and Manage Risks More Accurately: Identify high-risk individuals earlier and adjust policies accordingly.
• Enhance Customer Understanding: Gain insights into customer behavior, such as propensity to litigate or likelihood of early cancellation, improving risk management strategies.
• Improve Underwriting Profitability: Achieve more accurate risk assessment and better manage underwriting resources, contributing to healthier bottom-line profitability and sustainable growth.

Using Advanced AI Risk Assessment with Pinpoint Predictive

A more comprehensive and inclusive risk assessment requires a deep understanding of the individual behind the policy, as well as incorporating insights into an insurer’s decision-making processes as part of risk analysis.

Pinpoint Predictive empowers underwriters by enabling them to make smarter, more equitable assessments of risk, accurately identifying high-risk and low-risk individuals. This enhanced accuracy in underwriting workflows helps insurers better serve their customers by identifying and quantifying individual risk earlier and more accurately.

By bridging the gap between the most powerful behavioral predictions made by the world’s leading tech companies and the specialized requirements of the insurance industry, Pinpoint is delivering unmatched risk-selection capabilities at various points along the insurance value chain.

Conclusion

The homeowners insurance sector faces unprecedented challenges. Behavioral intelligence, centered on individuals, represents the next generation of technology, transforming policyholder risk assessments and offering insights into future customer risks. As the industry adapts to these new technologies, the focus will shift toward more informed, efficient, and customer-centric underwriting practices, paving the way for a more resilient and profitable insurance market. Insurers that integrate these advanced risk assessment tools and insights will ultimately be the most successful in enhancing their underwriting processes, addressing the evolving risks associated with new developments and weather events, and ultimately improving financial outcomes.

Improve your Underwriting Outcomes with Pinpoint

With predictions available earlier and more accurately than any other risk solution on the market, Pinpoint is transforming the P&C insurance industry and helping underwriters drive better outcomes with an AI-powered, real-time solution for precise risk selection. For more information about how Pinpoint can help you, visit www.pinpoint.ai or contact info@pinpoint.ai.

The post Addressing Homeowners Underwriting with Behavioral Risk Predictions first appeared on AAIS.

The Relationship Between Reinsurance and Home Inspections

According to Kalisch, the relationship between reinsurance and home inspections boils down to the quality of the book of business that the insurance carrier maintains, as reinsurers always seek a healthy book. “If you have an inspection program within your underwriting guidelines, you are more apt to have a healthy book of business,” he explained. “This means that you have looked at these risks, assessed them, and made sure that they are acceptable for your own guidelines, which then helps reinsurance carriers feel more comfortable with writing your business.”

Biggest Oversight by Insurance Companies Seeking Reinsurance

Kalisch believes the biggest oversight for insurance carriers seeking reinsurance is not considering potential risks in their book. “They approach it with the mindset that the only concern is CAT (catastrophe) claims,” he said. “They think that’s the only thing reinsurance covers; however, they don’t realize that there is a myriad of other claims that can impact how reinsurance carriers look at them.” For example, if many roofs in an insurer’s book of business are beyond their age guidelines or in poor condition, Kalisch warns they will be the first affected by a hurricane, even a small one, as large hurricanes don’t impact new roofs the same way they do old ones. “If you are doing inspections [and] maintaining a healthy book of business, when reinsurance companies look at you, they will see your ability to maintain a small storm and maybe will have to pay for a larger one. This provides a level of comfort when they assess your book and the risk they are taking on.”

About Chrp Technologies

Chrp Technologies is an AI-based loss prevention program deeply rooted in construction and underwriting expertise. Developed using claims data, 20 years of underwriting data, and on-site experience observing the cause and effect of potential losses, it features proprietary AI designed to identify and mitigate risks for carriers. This scalable solution enhances the profitability, expertise, and efficiency of underwriting departments, according to Kalisch.

How AAIS Members Can Leverage Chrp Solutions

The easiest way to benefit from Chrp is to reach out and schedule a demo. “The way this product is set up is to help underwriters,” Kalisch stated. “It’s very easy to use and get into the process. Start with basic loss assessments; find things that you’re missing on a regular basis that traditional inspections are not doing for you. Give your homeowners and policyholders the option to be able to conduct their own [inspections] in a safe environment and use the analysis that comes back from years of industry experience to be able to assess the quality of the business.”

Learn more at chrptech.com.

To view the full interview with Micah Kalisch, click on the video above.

The post AI Home Inspections: Enhance Loss Prevention and Reinsurance first appeared on AAIS.

What is GenAI?

GenAI is a relatively new form of artificial intelligence and it represents the natural evolution in the path of AI, according to Pasricha. “What makes it special is its ability to actually understand language, be able to synthesize it, analyze it, compare it, and create sensible responses for you,” he said. “So, we see it as a big step forward, especially with the large language models because they now have the ability to ingest and analyze a lot of data in context.”

How GenAI Can Be Applied to the Insurance Industry

Pasricha feels that GenAI has a lot of applications across the whole insurance value chain. “We all know that the insurance industry still has a lot of manual processes,” he explained. “With GenAI, you can actually start to make sense of complex documents. You can read them like a human would, take meaning [and] values out, and start taking comparative steps.” For example, GenAI can assist with reading a claims note. It will look at what the claim is for and what the values are, then compare it to what’s covered in the policy. From policy checking and comparing different quotes from carriers in reinsurance, to the ingestion of large treaty and portal documents, Pasricha believes the applications of GenAI in the insurance industry are endless.

Biggest Roadblocks to Utilizing GenAI

Pasricha sees five main roadblocks in the enterprise and insurance adoption of GenAI.

1. Security and Customization: Security and customization are important in GenAI applications. “When you’re using [GenAI] for sensitive and complex insurance tasks and doing it in the enterprise, it requires some sort of a secure environment,” Pasricha shared. “This involves a lot of integration and building custom models that understand insurance.”
2. Precision: There is no room for error in the insurance industry. “In this industry, a single error can cost a lot,” Pasricha warned. “If you’re a broker and you didn’t get the right coverage, or you failed to warn [the insured], that can be a major exposure. So, the risk of experimentation is very high.” Therefore, having a very finely tuned GenAI model and a good validation and QA (quality assurance) process is imperative.
3. Data Privacy and Confidentiality: Pasricha emphasized the significance of data security when implementing GenAI. “You can’t just use a general open GenAI service because that will leak your confidential information, like your forms or your special underwriting rules, and become a part of the public office,” he stated.
4. Skill Set: According to Pasricha, expertise and modern approaches are essential for GenAI development. “You need to know what you’re doing,” he stressed. “You cannot apply old knowledge, old methods, or old software techniques to building the GenAI; you’ll just waste a lot of time and money.”
5. Time and Cost: Pasricha highlighted the financial pressures and urgency faced by organizations. “Nobody has endless budgets, yet executive teams and boards are asking for results today,” said Pasricha. “There is a way to get accelerated results from GenAI, but you need to be able to solve all these other problems to get it to work.”

How Carriers Can Best Navigate GenAI Roadblocks

Pasricha believes insurers need to have the right software foundation to be successful with GenAI. “You need to put in place a lot of building blocks so that you can build safely, securely, and with high accuracy and effectiveness,” he shared. With software like BluePond.AI, carriers have a partner that has already built some of those foundational building blocks. “Even if you want to build your own application or train your model with your own data, you still have a partner [in BluePond.AI] who knows what they’re doing, has the right skill set and expertise, and more importantly, has an enterprise-grade software platform where you can test out all of your data privacy, security, [and] quality concerns to make sure that they’re taken care of.”

About BluePond.AI

BluePond.AI is a relatively new firm with a team of more than 50 members, each bringing 15 to 20 years of experience in the insurance industry and a long track record of developing machine learning tools for the sector. “Our special sauce is that we have a platform that is very targeted and pre-trained for property and casualty (P&C) insurance,” Pasricha explained. “Think of it as a GenAI platform that, out of the box, understands complex P&C language, can identify P&C insurance documents, and can run very fine-grained operations and data extraction on those documents.” For example, for brokers, BluePond.AI can identify a policy, the line and class, and who the insured is. “In the context of the line of business, we can automatically do a policy check or a code comparison for underwriting at a carrier site,” said Pasricha. “We can look at compliance to underwriting rules for claims and match claims documents and policy documents for reinsurance, too.” BluePond.AI is a custom-built platform for the whole P&C value. Learn more at bluepond.ai.

To view the full interview with Pranav Pasricha, click on the video above.

The post Applications of GenAI in Insurance first appeared on AAIS.

One challenge to maintaining a healthy book of business for carriers is that their margins are too thin to inspect every property. This is why property insurers use a variety of data sources to understand the risks both inside and outside the home, such as:

• predictive analytics for non-catastrophe (non-CAT) water and fire risk;
• roof scores to assess roof condition;
• building characteristics and permit data; and
• traditional loss control inspections.

While these data sources are a great start, they fall short because they don’t identify specific failure points inside a home or determine a home’s overall condition.

Small Issues Lead to Big Claims

Chrp Technology’s analysis of claims data has shown that small issues can lead to big losses. For example, did you know one of the biggest drivers of non-CAT water losses in homes (both old and new) is related to a plastic coupler nut located under a toilet’s tank? Even though the nut says, “install by hand,” these nuts are often tightened using a wrench or pliers. This improper installation causes ripples or cracks in the nuts, accelerating their deterioration and leading to claims. Recently, a carrier we spoke with suffered a $1.5 million claim from a $2.50 nut. Advances in AI can now estimate the lifespan of components inside and outside homes which may lead to non-CAT water and fire claims.

As personal lines property insurers grapple with securing reinsurance capacity, some carriers are using AI-based home health assessments to flip the script on traditional underwriting methods by proactively fixing issues and writing more low-risk policies focused on a home’s condition, rather than an arbitrary threshold (e.g., age of home).

By taking a more modern approach to underwriting through the use of AI-based home health assessments, carriers have been able to secure excess reinsurance capacity by fixing issues inside and outside of homes, capturing vital data points about properties, and making better underwriting decisions based on the home’s condition.

In the sections below, we’d like to show you how the next generation of AI and digital technologies is helping carriers in three key areas to maintain a healthy book of business and reduce their loss ratios.  

Risk Selection: A Picture is Worth a Thousand Words

Adverse risk selection plays a vital role in minimizing losses. However, insurers cannot do much regarding catastrophic claims. All they can do is perform their due diligence and spread their risk to minimize losses.

Non-CAT water and fire claims are a different story. Now, more than ever, underwriting is under pressure to avoid as many of these claims as possible, and with good reason. Our analysis has shown that eight out of the top 15 issues leading to claims are related to basic plumbing issues.

AI and digital technologies make it possible to identify non-CAT water and fire issues from cell phone imagery. By simply texting a link to a homeowner, carriers can walk a consumer through a guided self-inspection of their home. After capturing the imagery, AI can analyze the photographs pixel by pixel to assess the condition of 250 subcomponents inside and outside of a home.

Every carrier has hundreds—or even thousands—of minor issues (which are easy to fix) in their book of business. AI can alert underwriters about these issues to help carriers:

• reduce losses by identifying symptoms that lead to claims; and
• increase policyholder retention by creating programs that help customers maintain healthy homes.

By proactively identifying and resolving maintenance issues early in the underwriting process, our carrier partners have reported a 5 to 10% decrease in annual loss ratios and up to a 12% increase in renewals.

Underwriting Efficiency: Finding Needles in a Haystack

Traditional loss control inspections can be very helpful in mitigating risk, but they are geared towards the home-buying experience rather than insurance underwriting. Reviewing an inspection to identify hazards that could lead to a claim is like finding a needle in a haystack. It can take an underwriter 20 to 30 minutes to review a single report, which is neither scalable nor cost-effective.

AI has evolved over the last few years from identifying objects (e.g., type of water heater or appliance) to assessing the condition of homes and identifying potential issues.

By highlighting the issues found and putting them on top of home health assessment reports, underwriters no longer need to dig through reports to take action. This can reduce the time it takes to review an individual report from 20 to 30 minutes to under 1 minute. In addition, carriers can create unique underwriting guidelines and workflows based on the health of a home (unhealthy vs. healthy).

For example, healthy homes with no issues can be automatically moved down the underwriting chain.

For new business submissions or renewals with minor issues, underwriters can let consumers know that they noticed a few small problems, but if the consumer can fix them, they’d be willing to work with them.

Finally, for policies with major hazards, carriers can automatically send reports to agents and homeowners to let them know they don’t provide coverage for renovations, or their roofs need to be fully replaced. 

Claims Prevention: Little Things Can Make a Big Difference

An ounce of prevention is worth a pound of cure. While some claims are inevitable, many others are preventable.

It’s been estimated that in 2024, one in 60 homes will file a water damage claim.¹ Our analysis of claims data has shown that little things can lead to big claims. For example, did you know the second leading cause of water claims is related to water supply lines?

Around 30 years ago, manufacturers of stainless-steel supply lines started using surgical rubber tubing wrapped in stainless steel braids to improve supply lines’ aesthetics and to reduce costs. Unfortunately, manufacturers didn’t consider the reaction between chromium in the steel braids and the chlorides found in most household cleaning products. These chlorides can strip away steel’s protective properties, leaving behind iron strands that are susceptible to rusting and fracturing over time. Once braids begin to weaken, it’s only a matter of time before the malleable rubber pushes through an opening in the braiding and bursts like a balloon, leading to a water claim.

Home electrical fires are another example of minor issues turning into big claims. Electrical fires cause an estimated $1.3 billion in property damage each year.²

One of the benefits of AI-based home health inspections is that they can identify issues related to water and fire claims, such as:

• rusting and pitting on supply lines;
• exposed wires under sinks; and
• recalled electrical panels and breakers.

If a traditional home inspection catches any of these issues, it can take 20 to 30 days for it to reach an underwriter. The turnaround time with AI-based home health assessments is much faster at five to seven days, allowing underwriters to work with insureds and agents to fix any issues, avoid unnecessary claims, and create healthier homes.

Gaining Reinsurance Confidence with AI
Loss control in commercial lines property is a mature capability. Some would even
argue that loss control engineers at companies like FM Global know more about the facilities they insure than the employees who work in them.

Unfortunately, due to razor-thin margins in personal lines property, carriers had to be selective about the properties they inspected and how they inspected them. AI and digital technologies can now help carriers gain vital information about the inside and outside of homes in a scalable and cost-effective way.

The advancement of AI and digital technologies couldn’t have happened at a better time as one of the first questions reinsurers and brokers ask carriers is, “What is your loss ratio and how healthy is your book of business?” By capturing vital data about policies and fixing potential issues, carriers can provide insights about the number of homes with roofs under ten years old and other valuable data points to gain reinsurer confidence.

Going forward, we believe carriers who leverage AI-powered home health assessments will have a much easier time securing reinsurance capacity and will gain a competitive edge by writing more profitable new business and renewals.

About Chrp Technologies

Chrp Technologies helps home insurance companies build great relationships with their insureds. Our proprietary AI products deliver loss prevention and enhanced customer experience. Learn more at chrptech.com.

Resources

¹Hattle-Cleminshaw, A. (n.d.). Smart home water monitoring devices prevent property damage. propertycasualty360.com. https://www.propertycasualty360.com/2024/03/26/smart-home-water-monitoring-devices-prevent-property-damage/

²NFPA report – home electrical fires. nfpa.org. (n.d.). https://www.nfpa.org/education-and-research/research/nfpa-research/fire-statistical-reports/electrical-fires

The post Can AI Home Health Assessments Boost Reinsurance Capacity? first appeared on AAIS.