The Flaws in Traditional Rating Methods

Lynch explained that traditional flood insurance rating has historically relied on FEMA (Federal Emergency Management Agency) flood zones, which categorize areas into low and high-risk zones. “In the market today, it’s largely been zone-based. FEMA flood zones represent about three-quarters of the market,” he noted. However, this approach has significant limitations. “About 25% to 30% of FEMA claims come from areas outside these high-risk zones,” he pointed out. This indicates that many properties at risk of flooding may be overlooked due to their classification. “The Congressional Budget Office estimated that in 2020, 40% to 50% of flood damage occurred outside special flood hazard areas,” Lynch further elaborated. He explained that while NFIP (National Flood Insurance Program) created flood zones, they have moved away from using them in their rating methodology. “It seems reasonable that probably everyone else should consider not using them as well,” he stated, urging the industry to rethink traditional methods.

The Role of Catastrophe Models

In a market saturated with outdated methods, Lynch emphasized the importance of catastrophe (cat) models for predicting flood risks. “Cat models build out simulated possible futures,” he said, explaining how they analyze various scenarios to assess potential damage. They consider factors like storm strength, geographical location, and even tide levels to generate a more comprehensive view of risk.

However, he cautioned that no single model is sufficient to capture the complexities of flood risk. “Every model has its strengths and weaknesses,” Lynch remarked. The need for multiple viewpoints is crucial for a well-rounded assessment. “Those that take it further, often professional consultants and large reinsurers, will use multiple models. They may average the results, which is potentially dangerous,” he noted.

Understanding and Distinguishing Flood Damage

A significant challenge in flood insurance is the difficulty in separating flood damage from other storm-related damages, such as wind or tornado damage. “The cat models do the heavy lifting on that,” Lynch explained, noting that simulations help clarify what happened during an event. Yet, in the real world, distinguishing these damages can be problematic because of the ambiguity. “If the building’s not there anymore, did it get blown away, or did it get washed away?” he questioned. Despite these challenges, Lynch acknowledged that rainfall-driven floods are generally easier to assess. “Rainfall is typically a lot easier to separate out,” he stated. Understanding these distinctions is vital for accurate claims projections.

Leveraging AI for Improved Predictions

To address the limitations of traditional models, the webinar introduced an innovative AI technology developed by reThought Flood. “We used our AI technology to focus on this problem, not to replace catastrophe models, but to make them better,” Isaacson explained. The reThought team applied a bottom-up approach, analyzing detailed information on over 100 million buildings in the U.S. This method offers a more granular view of risk compared to traditional top-down models that assess geographical footprints. Lynch elaborated, stating that their approach helps identify strengths and weaknesses of various models. “We use our technology to develop our own specific view of each building and then we can roll that up to a policy, a portfolio, a state, or a country view,” he said. This innovation enhances the accuracy of damage predictions and ensures that rates are sustainable and profitable.

Closing the Coverage Gap

Despite advancements in technology and modeling, Lynch warned that having the best technology is not enough; product coverage is really important. He described the challenges faced by policyholders, particularly with federal programs like NFIP, which often lack adequate coverage for secondary dwellings, basement contents, and additional living expenses. “If someone buys a flood insurance policy, has a loss, and finds that much of it is not covered, that would be horrible,” Isaacson noted. He and Lynch underscored the critical role of education in closing the flood protection gap, particularly the need for property owners to understand the risks and potential for loss. “Unfortunately, flood is one of the weakest parts of education for insurance agents. We really need to provide them not only with education but also with the tools for them to help educate others,” Lynch stated.

Community Engagement and Awareness

Isaacson and Lynch highlighted that community involvement is crucial for increasing flood insurance uptake. “Communities need to educate their populations about the risks they face,” Isaacson stressed. “Two-thirds of flood losses are currently uninsured, and around 90% of the country lacks flood coverage.” They encouraged collaboration with communities to disseminate information and promote attainable mitigation measures. “Something like backflow preventers can help mitigate risks without requiring significant structural changes,” Lynch suggested, advocating for educational initiatives that inform property owners about effective, low-cost mitigation strategies.

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.

John Kadous

Vice President of Products, AAIS

johnk@aaisonline.com

Cory Isaacson

Chief Executive Officer, reThought Flood

cory.isaacson@rethoughtinsurance.com

Derek Lynch

Chief Underwriting Officer, reThought Flood

derek.lynch@rethoughtinsurance.com

The post Webinar: Making Flood Insurance Insurable and Profitable first appeared on AAIS.

This is where the AAIS S2M Toolkit comes into play. Designed to streamline the implementation process on a policy administrative system (PAS), the S2M Toolkit accelerates the success of new and updated insurance products. For existing and future AAIS Member companies, the S2M Toolkit is an essential resource during the AAIS onboarding process. Here’s a detailed look at how the S2M Tools work in conjunction with AAIS onboarding to expedite your product launch.

Understanding the Onboarding Challenge

Onboarding a new insurance product can be complex, involving a multitude of steps: determining which forms and endorsements to adopt, potentially developing a custom manual, converting loss costs into rates, and submitting the new or updated product to the respective insurance department. The final and often most critical phase is integrating the new or updated product into your PAS, which can become a bottleneck if not managed efficiently.

The Role of S2M Tools in Onboarding

The S2M Toolkit, which is delivered during onboarding, supports each stage of the process with a set of tools designed to help streamline and expedite. These tools provide clarity, accuracy, and speed in integrating new insurance products into your PAS. By integrating these tools into your onboarding strategy, you can overcome common challenges and achieve a faster, more efficient product rollout. Here’s how each component contributes to a more efficient onboarding experience:

Product Models: The Foundation of Efficient Development

The first component of the S2M Toolkit is the Product Models. These models offer a comprehensive representation of AAIS insurance products in both human-readable (Mind Maps) and machine-readable (JSON) formats. They facilitate the onboarding process through:

• Accelerated Product Development: Product Models include visualization tools and detailed product content that simplify the development process. This clarity helps your team quickly understand and integrate product data into your PAS, significantly reducing development time.
• Customization and Adaptation: Product Models enable easy tailoring of insurance products to meet specific market needs and regulatory requirements. Customization ensures that your products are not only relevant but also compliant with industry standards. 
• Centralized Metadata Management: Product Models provide a single source of product metadata, reducing inconsistencies and errors while facilitating smoother integration into your system.

By utilizing Product Models, you can streamline the development process and set the stage for a more efficient onboarding experience.

S2M Human-Readable Content: Visualizing Product Structures

S2M Human-Readable Content (Mind Maps) are a crucial component of the S2M Toolkit. These visual aids offer a graphical representation of AAIS insurance products, making it easier for your team to understand complex product details. Mind Maps contribute to a more effective onboarding process by providing:

• Enhanced Visual Clarity: Mind Maps provide a clear, visual overview of the product structure and its components, helping your team grasp product intricacies and integrate it into your PAS more easily.
• Facilitation of Future Expansion: By presenting a visual roadmap, Mind Maps prepare your team for future expansions, such as adding new states or product lines. This forward-thinking approach ensures that your system can accommodate future growth and changes.

Using Mind Maps during the onboarding process enhances your team’s understanding of the product and prepares you for seamless expansion.

S2M Machine-Readable Content: Ensuring System Compatibility

S2M Machine-Readable Content delivers an electronic representation of AAIS insurance products in a JSON format that can be consumable by your system. This component of the toolkit is critical for:

• Seamless System Integration: Technical Specifications provide detailed, standardized data that supports integrating new products into your PAS. This ensures that your system can accurately process and manage the product from day one.
• Reduction in Implementation Time: The standardized format of Technical Specifications helps reduce the time required for system configuration and testing. This efficiency is crucial for meeting your company’s goal of generating premium revenue on an expedited basis.

By providing a clear and standardized format for technical data, S2M Technical Specifications streamline the integration process and accelerate your time to market.

S2M Rating & Data Tables: Accurate Rating Information

S2M Rating & Data Tables include essential AAIS factors, loss costs, and territorial definitions from multistate and state manual pages. These tables are vital for:

• Providing Product Reference Data: Rating & Data Tables offer product reference data in a digital format that is easy to integrate into your system. This eliminates the need for manual data entry and reduces the risk of errors.
• Saving Time and Money: By using preformatted Rating & Data Tables, you can significantly speed up the implementation process. This efficiency not only saves time but also reduces the overall cost of bringing a new product to market.

Rating & Data Tables ensure that your premium calculations are accurate and that your system is efficiently configured to handle new or updated products.

S2M Rating Examples: Understanding Premium Calculations

S2M Rating Examples provide detailed insights into premium calculations using sample rating data. These examples are essential for:

• Ensuring Accuracy with Up-to-Date Data: Rating Examples offer current, state-specific rating information that reflects the latest data and regulatory requirements. This ensures that your premium calculations are accurate and compliant with industry standards.
• On-Demand Guidance: Step-by-step explanations of premium calculations help your team understand and implement complex rating methodologies, ensuring that your system processes premiums correctly from the outset.
• By providing clear and detailed examples, S2M Rating Examples support accurate premium calculations and help your team navigate the complexities of rating methodologies.

The Benefits of Using S2M Tools

Integrating S2M Tools into your onboarding process offers several significant benefits:

• Accelerated Time to Market: The primary goal of the S2M Toolkit is to expedite the implementation of new or updated insurance products. With the support of S2M Tools, you can achieve a faster time to market and start generating premium revenue by utilizing the AAIS onboarding process.
• Increased Accuracy: The detailed, standardized data provided by S2M Tools reduces errors during integration, ensuring correct PAS configuration and accurate premium calculations.
• Enhanced Efficiency: S2M Tools streamline the onboarding process, allowing your team to focus on strategic tasks rather than data entry and formatting issues. This helps you bring products to market more quickly and effectively.
• Flexibility and Adaptability: The S2M Toolkit offers a flexible framework that can adapt to various product types and regulatory environments. This adaptability is essential for managing a diverse portfolio of insurance products and expanding into new markets.
• Comprehensive Support: The S2M Toolkit provides robust support throughout the onboarding process. From visual aids like Mind Maps to detailed Technical Specifications, each component offers valuable insights and resources for a smoother and more effective integration.

Conclusion

The AAIS S2M Toolkit represents a significant advancement in the onboarding and implementation of insurance products. By providing essential tools and resources, the S2M Toolkit helps organizations accelerate product launches, improve accuracy, and enhance efficiency. As the insurance market continues to evolve, having a streamlined and effective onboarding process is more important than ever.

The S2M Toolkit’s comprehensive suite of tools, Product Models (including Mind Maps and JSON Technical Specifications), Rating & Data Tables, and Rating Examples, works together to address the complexities of integrating new products into your PAS. By leveraging these tools, you can overcome common challenges, reduce implementation time, and achieve your goal of generating premium revenue as soon as possible to recoup the costs of adopting a new or updated product.

To explore how S2M Tools can transform your onboarding process, watch the AAIS Webinar replay, Streamlining Product Adoption with the AAIS Speed to Market (S2M) Toolkit, for an in-depth demonstration of how these tools can accelerate your path to market success.

If you are ready to leverage the S2M Toolkit, please reach out to Sheila Morris or Jeff Holaway to kick off the onboarding process and support:

Sheila Morris, CPCU, CPM

Director of Member Success

sheilam@aaisonline.com

(630) 457-3286

Jeff Holaway, CPCU, CPM

Product Technical Lead

jeffh@aaisonline.com

(630) 457-3231

The post Transform Insurance Product Launches with S2M Tools first appeared on AAIS.

In 2024, a P&C insurer study conducted by Capgemini suggested that 41% of the underwriters’ time is spent on “administrative and operational activities, constraining their capacity and triggering value chain challenges in pricing and broker/customer experience.”

Improving underwriter efficiency has always been a key driving force behind selecting an insurance platform. However, merely adopting a modern insurance platform is insufficient to address some of the current inefficiencies. To truly enhance underwriting efficiency, insurers now have the opportunity to select an insurance platform with built-in advanced AI-based automation and process optimization solutions. Furthermore, selecting a platform equipped with AI capabilities and partnering with a solution provider that is actively investing in AI technologies will not only meet current underwriting demands but also drive future productivity and innovation requirements.

With built-in AI-driven solutions, insurers can streamline operations, reduce manual workloads, and enhance overall efficiency, positioning themselves for long-term success in a rapidly evolving market. A modern platform working in cohesion with its own AI solution is the only way to achieve maximum efficiency and success.

Key areas where AI can bring substantial benefits include:

• Improving User Experience and Automation: AI-powered platforms can streamline and automate repetitive tasks, such as data entry and analysis, thereby reducing the administrative burden on underwriters. For example, Cogitate’s DigitalEdge platform has UW Assist, powered by CogitateLabs, which includes data ingestion capabilities. The AI reviews the input submission forms, automatically identifies the inputs for the submission through its powerful LLM models, and inserts data automatically into the application. This drastically reduces the manual administrative work for the underwriters and improves their user experience.
• Reducing Premium Leakage and Adverse Selection: AI can play a crucial role in minimizing premium leakage by identifying discrepancies and ensuring that premiums accurately reflect the risk associated with each policy. Moreover, AI-driven analytics can detect patterns indicative of adverse selection, where higher-risk individuals might be disproportionately represented. By addressing these issues, insurers can better align premiums with actual risk profiles and improve the financial stability of their portfolios.
• Prioritizing Work: AI can assist underwriters in prioritizing their workload by analyzing and sorting tasks based on urgency and impact. Intelligent systems can assess cases that require immediate attention and those that can be deferred, optimizing the allocation of resources and improving workflow efficiency. This prioritization ensures that critical tasks are addressed promptly, leading to better risk management and decision-making.
• Identifying Demands: Attorney demand letters are one of the biggest challenges facing the insurance industry. Any bad faith claim can have a significant impact, with settlements running into millions of dollars. Insurance companies have to deploy the manual process of reviewing thousands of documents daily to identify demand letters from attorneys. This is a time-consuming and error-prone process. With solutions like Cogitate’s DemandAssist, an AI/ML-based product, significant operational efficiency with an error-free process can be achieved.

Conclusion

AI-based tools are becoming a part of the work process, and proper adoption is key for an organization’s success and sustainability. To effectively leverage innovative tools, particularly AI-assisted solutions, it is essential to first build trust within your team. This involves gradually introducing these tools to address specific issues, consistently using and testing them, and measuring the improvements they bring. By demonstrating tangible benefits and success, you foster confidence in the technology and cultivate advocates for innovation within your organization.

Equally important is finding the right partner, like Cogitate, to support your transformation journey. Selecting the right solution provider who not only offers advanced tools as part of their insurance platform but also invests in ongoing innovation and support ensures that you have a reliable ally in navigating the complexities of modern insurance technology needs. This partnership is crucial for maximizing the potential of AI solutions and achieving long-term success in underwriting and beyond. Learn more about Cogitate at cogitate.com.

The post AI-Powered Insurance Platforms Boost Underwriting Efficiency 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.

Introduction to Actionable Insights

Mayfield defined actionable insights as “curated data that allows the end user to make informed decisions and ask thoughtful questions.” He identified three key components of actionable insights:

• Business Focused: A collaboration between business units and IT is required to transform data from two-dimensional to three-dimensional.
• Clear Requirement Gathering Framework: A standardized approach helps streamline the process from request to report generation.
• Data Storage and Visualization Solution: Effective reporting solutions need strategies for easy data retrieval, transformation, and visualization.

Examples of Actionable Insights

Mayfield shared practical examples of actionable insights, illustrating how data can be used to make informed decisions. For instance, a claims team leader might ask, “How does average days to close impact my cost per claim?” This question aims to identify factors that can be controlled to improve cost efficiency. Another example involves examining the performance of underwriters by asking, “What’s the closure ratio of my top-performing underwriters, and which brokers are they working best with?” To pull out actionable insights, Mayfield explained, “You want to really look at submission data and get an idea of which underwriters are performing amazingly with certain agents, which may need some help, which agencies are providing equality results…things like that.”

Helping Carriers Make Informed Decisions with Data

To help carriers generate actionable insights, Mayfield emphasized the need to focus on the business problem and not the current limitations. “It’s about what the business wants,” he stated. Mayfield explained that actionable insights are rooted in addressing specific business questions and challenges. For instance, an underwriter might be concerned with increasing policy premiums, while a claims manager might focus on forecasting staffing needs based on task completion times.

Mayfield also highlighted the importance of frameworks and methodologies in generating actionable insights. One such framework that OneShield utilizes is the “BADIR” framework from the book Behind Every Good Decision. This framework helps bridge the gap between business questions and meaningful reports. It emphasizes the importance of understanding the goals of analysis and considering regional differences when generating reports.

Solutions to Evolve Reporting

Mayfield addressed the limitations of traditional reporting tools like Excel, highlighting the need for more advanced solutions. He recommended tools like Power BI and Azure Data Factory, which enable dynamic, interactive reporting and allow users to generate actionable insights efficiently.

Utilizing Core Systems for Actionable Insights

According to Mayfield, actionable insights require a core system capable of providing two-dimensional data that meets initial business needs from day one. Rather than focusing on regulatory requirements, he believes the emphasis should be on essential data needed to manage a business effectively. This includes policy details, earned premium, written premium, renewal rates, accounts receivable, and claims information such as average days to close and average cost to close. Mayfield advised this data to be available in various formats—by region, in real-time, monthly, or quarterly. “There’s a lot of different ways to view this data, and once you get there, you might want to mix and match the different elements to manage your business,” he said. But before advancing to three-dimensional reporting that provides tailored actionable insights, companies need a reliable core system for detailed data.

Leveraging Actionable Insights for Dynamic Analysis and Proof of Concept

When developing actionable insights, Mayfield believes it is crucial to understand that end users may not always know exactly what they need but will recognize valuable information when presented with it. For example, a claims manager might be uncertain about how to improve claim processing efficiency but will benefit from a flexible report that allows them to explore various data perspectives. In a given scenario, the manager might want to investigate how closing claims more quickly affects the overall cost of those claims. Mayfield explained that the report should provide detailed insights, such as identifying high-performing individuals like an adjuster who significantly reduces the average cost to close claims. Additionally, the manager might hypothesize that closing more claims within 90 days will reduce average costs. Mayfield added that the purpose of such reports is to allow users to verify their hypotheses through concrete data. “We’re no longer speculating about what the results are,” he confirmed. “We can now make informed decisions and go forward knowing that we’re building something that’s valuable, making decisions that we can back by data, and see results in areas that matter the most.”

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.

John Kadous

Vice President of Products – AAIS

johnk@aaisonline.com

John Dunn

Vice President of Sales, Americas – OneShield

jdunn@oneshield.com

Travis Mayfield

Reporting Product Manager – OneShield

tmayfield@oneshield.com

The post Webinar: Leveraging Core Systems for Actionable Insights in Policy Claims first appeared on AAIS.

AI Game Changers for Underwriters

With extensive industry experience and a keen eye on the evolution of technology platforms, particularly in AI and underwriting, Schaendorf identified numerous use cases for carriers, especially in underwriting and claims. She also sees significant potential in data access and utilization. Currently, data is often presented in static reports or dynamic formats, but AI has the power to transform how data is interpreted and interacted with.

Advancing Predictive Modeling in Underwriting and Claims Processes

In addressing the future of predictive modeling, Schaendorf explained how Cogitate focuses on its application in underwriting. “We’re looking at the profile of our risks, specifically the risk attributes that come in with each different type of client,” she said. While there are similarities in some variables, many are distinct, and Cogitate’s goal is to use these data attributes to build models that predict outcomes. For instance, Cogitate aims to determine the propensity for a loss ratio based on specific risk attributes.

On the claims side, the objective is to value claims accurately, especially complex ones. “It’s easy to handle the least complex claims, such as glass and towing in auto, but when it [comes] to a case that’s going to go to litigation, how do you set the reserves on that and what does that look like over the life of that claim?” Schaendorf proposed. “I see the ability to use that data and predictive modeling to get a little more accurate and a little better at it.”

Biggest Data Challenges Carriers Face and How to Overcome Them

According to Schaendorf, the biggest challenge carriers face when it comes to data is its structure and accessibility. While some data, like written premiums, policy numbers, and loss ratios, are well-structured, other types are not. This lack of structure poses significant difficulties in obtaining better, more accurate, and more useful data. To address this, Schaendorf believes it is crucial to foster a strong partnership between data professionals and top leadership within a company. “One group has one set of skills, and the other group has the business knowledge,” she explained. Although establishing these standards is time-consuming and may not seem immediately rewarding, they ultimately facilitate faster and more accurate data access.

About Cogitate

Cogitate was formed in 2012 by Schaendorf and her co-founder, Arvind Kaushal, as a digital platform operating on a software-as-a-service model. It is designed to streamline rating, quoting, binding, and issuance across all lines of business, including both commercial and personal lines. This modern-facing platform boasts numerous third-party data integrations, enhancing its functionality. “The proudest thing I can say about Cogitate is it’s an enabler,” Schaendorf shared. Cogitate has seen rapid growth and implementation success with their clients, processing a significant volume of quotes, binders, and premiums through the platform across various lines, such as commercial transportation, personal property, flood, commercial property, and professional liability. “It’s really facilitating growth for our partners, and that’s what we’re most proud of with the company.”

Learn more at Cogitate.com.

To view the full interview with Jacqueline Schaendorf, CPCU, click on the video above.

The post AI, Predictive Modeling, and Data Trends in Underwriting 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.

Sales and marketing have made arrangements with your agents, product development has established the forms and endorsement plus manual rules and rating information, underwriters have created underwriting guidelines for new business, and the filings have been submitted and approved.

However, everything is on hold while there are delays in implementing and launching this new product on your company’s policy administration system (PAS). But it’s not the fault of the developers and programmers; this is a new product that requires defined forms and endorsements attributes, final rating information, premium calculation steps, precise rating variables, and more.

The AAIS Speed to Market (S2M) Toolkit was created to streamline implementation and accelerate success in insurance rating. S2M Tools provide companies’ policy administration providers (whether it’s an internal home-grown system or an external provider) with a head start in defining the technical specifications.

The AAIS S2M Toolkit provides the following IT advantages:

Product Models

Product Models allow Member companies to consume the content of AAIS insurance products in both human and machine-readable formats.

■ Accelerate product development using visualization tools, product content, and code.

■ Personalize insurance products to meet demands.

■ Create one source of product metadata.

S2M Mind Maps

Mind Maps provide a visual representation of an AAIS insurance product.

 S2M Technical Specifications

Technical Specifications offer an electronic representation of an AAIS insurance product in a system consumable format.

S2M Rating & Data Tables

Rating & Data Tables are AAIS factors and relativities, loss costs, and territorial definitions from multistate and state manual pages.

■ Provide product reference data in a digital format that is easily consumable.

■ Saves time and money to implement a new or updated product on a speed to market basis.

S2M Rating Examples

Rating Examples provide an overview of premium calculations using sample rating information.

■ Current and accurate countrywide, state-specific rating examples are updated when needed.

■ Access to on-demand step-by-step explanations of basic and optional premium calculations.

Many AAIS Members have already benefited from using S2M Tools. Loudoun Mutual Insurance utilized Rating Tables for a new product launch and experienced an accelerated speed to market. “Loudoun Mutual sought speed and efficiency in launching a new BOP line of business and knew policy management system (PMS) configuration would be a significant challenge,” said Tim Koppenhaver, Executive Vice President at Loudoun Mutual. “We reached out to AAIS regarding their Speed to Market feature, and they quickly responded with rating tables in a clear and understandable format, which we shared with CoPilot, our PMS contractor, who found the tables to be essential in getting the line set up.”

“The biggest thing for me is that the tables have made formatting the data much quicker and easier than trying to copy and paste from the manual,” said Lindsay Brooks, Owner of CoPilot. “I would recommend that these always be provided for a smoother, more accurate implementation.”

S2M Tools have helped streamline new product adoption for CFM Insurance and Cornerstone National Insurance as well. “At CFM Insurance and Cornerstone National Insurance, we have introduced a full suite of AAIS products,” explained Joey Kenney from Innovated Holdings. “After implementing the Homeowners By-Peril program, using the AAIS Mind Map was a great way to help our development team understand what needed to be done. It also helped prepare us to introduce additional states in the future.”

Interested in learning more? Watch the replay of the AAIS Webinar, “Streamlining Product Adoption with the AAIS Speed to Market (S2M) Toolkit,” to see a live demo of the S2M Toolkit deliverables.

Reach out to any of the featured webinar speakers through the contact information below to find out how the AAIS S2M Toolkit can accelerate your speed to market!

Sheila Morris, CPCU, AMIM, CPM

Manager of Product Operations, AAIS

sheilam@aaisonline.com

Jeff Holaway, CPCU, CPM

Product Technical Lead, AAIS

jeffh@aaisonline.com

Jason Buchek, CPCU, ARM

Technical Business Analyst, AAIS

jasonb@aaisonline.com

The post Webinar: Accelerate Speed to Market with the AAIS S2M Toolkit first appeared on AAIS.