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.

Practical First Steps for Integrating AI Without Disrupting Operations

“AI is definitely frontier-level stuff that’s changing rapidly, so it’s natural to be cautious about its adoption,” Villa explained. A practical first step is to focus on data hygiene, ensuring that AI has access to accurate and structured data. He recommends insurers take key steps, including:

• Establishing clear data standards for formatting, naming conventions, and storage.
• Enhancing data security and privacy by anonymizing or adapting personal data for AI model training.
• Extracting, integrating, and consolidating data from various systems.

“Having limited data sets that are thoroughly cleaned and vetted will create a strong data foundation and position AI initiatives for success,” Villa added.

Using AI to Enhance Teams, Improve Decision-Making, and Drive Efficiency

Most insurers start their AI journey with chatbots or automated document processing to improve data access and decision-making. AI streamlines manual tasks, such as summarizing home inspection reports, which typically take underwriters about 30 minutes to review. AI can ingest data, redact personally identifiable information (PII), and generate a summary in seconds, making it immediately actionable. “This level of AI precision reduces cycle time, but does not replace the underwriter,” said Villa. “Instead, it’s a collaborative process that combines human expertise with machine learning a ‘human-in-the-loop’ approach.”

Overcoming Key Challenges in the AI Journey to Deliver Measurable Value

“I think the biggest challenge we face is thinking differently about the work we’ve been doing for the past 20 years,” said Villa. “Using ChatGPT to help enhance the quality of and reduce the time it takes to put things in writing or researching topics feels natural. Who wouldn’t want to spend less time writing emails or researching topics?” However, the challenge arises when AI begins to automate tasks professionals feel they have mastered. “What if I’m a senior underwriter and I am an expert at inspection report reviews?” Villa posed. “I may not be as open to using AI, even if it gives me back time in my day. Helping our team understand that AI isn’t going to replace the work we do, but rather augment it, will be key to successful adoption.”

How OneShield Helps AAIS Members Navigate the Evolving AI Landscape

OneShield is at the forefront of AI-driven transformation, offering two modern core systems: OMS and Enterprise. “Both solutions can be delivered as enterprise suites to automate policy, billing, claims, workflow, and reporting, or they can be implemented as a modular solution to address any one particular aspect of the insurance value chain,” Villa explained. For AAIS Members, OneShield’s expertise in AAIS lines of business makes adoption even smoother. “Across our two products, we can support all AAIS lines. We have particularly strong experience with unique AAIS lines such as COP and Artisans,” Villa noted. “Leveraging this experience to deliver pre-built AAIS content within our core solutions can accelerate core transformation projects at a significantly reduced cost to the insurer.”

Learn more at OneShield.com.

To view the full interview with Tony Villa, click on the video above.

The post AI in Insurance: Streamlining Underwriting Without Disruption first appeared on AAIS.

How Insurers Can Leverage New, Unconventional Technologies and Insights to Improve Loss Ratio

Ham believes that new behavioral insights offer insurers a critical tool to enhance risk assessment and loss ratios. “Insurers can leverage new and unconventional behavioral insights by performing better, more thorough risk assessment,” he explained. These insights provide an additional layer of understanding that complements traditional factors used in risk evaluation. With the inclusion of behavioral insights, carriers get “a factor that to date they haven’t had access to,” offering a fuller picture of the risk profile. This allows them to make more informed and profitable decisions.

How Carriers Can Integrate Behavioral Intelligence Into Existing Risk Mitigation Strategy

Carriers can integrate behavioral intelligence into their risk mitigation strategies at multiple points in the insurance value chain, according to Ham. By using behavioral insights, they can target individuals whose profiles align with their risk appetite, tailoring risk selection from the outset. “It goes down the insurance value chain, where they can dictate the buying journeys based on these new insights,” Ham explained. Additionally, carriers can use behavioral intelligence to develop renewal strategies, allowing for a more dynamic approach to risk management.

Using Behavioral Insights to Help Carriers Achieve Goals of Equity and Fairness

Behavioral insights can play a pivotal role in helping carriers meet goals of equity and fairness. “By getting that complete picture, we have to recognize the uniqueness of individuals, and behavioral insights are exactly that they’re telling the carrier unique insights about that individual, so they don’t have to bucket them or ban them, ” said Ham. He explained that by understanding individuals on a more personal level, carriers can avoid the unintended exclusion of certain groups, ensuring a more inclusive approach to underwriting and risk management. “It’s not based on banking, so you’re getting insights that are more inclusive of individuals, as opposed to those that may unintentionally exclude some.”

About Pinpoint Predictive, Inc.

Ham described Pinpoint Predictive as “the ultimate risk selection platform.” He elaborated, “Pinpoint Predictive leverages machine learning and these new unconventional behavioral insights to deliver unparalleled precision in underwriting and actuarial decisions. The company’s platform addresses not only profitability for insurers but also improves the overall customer experience and provides agents with better tools to represent carriers effectively. “For me, Pinpoint is that ultimate risk selection platform that allows everybody to win.”

Learn more at pinpoint.ai.

To view the full interview with Scott Ham, click on the video above.

The post Enhancing Risk Mitigation with Behavioral Intelligence 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.

LISLE, IL, July 29, 2024 — The American Association of Insurance Services (AAIS) is excited to welcome Pinpoint Predictive, Inc. to the AAIS Partner Program.

“We are proud to join the AAIS Partner Program and are thrilled to align with such a reputable organization that is providing the value and support it does for the insurance sector,” said Scott Ham, Chief Executive Officer of Pinpoint Predictive. “Working with AAIS to support U.S. insurance carriers offers Pinpoint a great opportunity to further our mission of promoting sustainable, profitable growth and fairness for insurers.”

Pinpoint Predictive’s innovative risk selection platform utilizes deep learning and individual-level behavioral intelligence to provide P&C insurers the ability to augment how they can assess and predict risk. Pinpoint’s predictions help insurers make informed risk decisions, improving loss ratios and accelerating profitability.

“We are excited to welcome Pinpoint Predictive to our AAIS Partner Program, providing our Members the opportunity to leverage their platform to improve loss ratios and accelerate their business,” said Werner Kruck, President and Chief Executive Officer of AAIS. “Pinpoint offers innovative technologies, like individual behavioral predictors and advanced AI, which is becoming crucial now more than ever in this ever-changing insurance marketplace.”

The AAIS Partner Program provides AAIS Members with unique access to quality products and services that help them streamline processes, achieve operational efficiency, and grow their business. To find out how AAIS Partners like Pinpoint Predictive can help create better customer experiences and increase speed to market, please visit AAISonline.com or contact the AAIS Member Engagement Team at membership@aaisonline.com.

About Pinpoint Predictive, Inc.

Pinpoint Predictive provides property & casualty insurers with the earliest and most accurate loss predictions and risk scores to fast-track profitable growth and improve loss ratios. Unlike traditional methods, Pinpoint’s platform leverages deep learning, proprietary behavioral economics data, and trillions of individual behavioral predictors to help insurers identify the risk costs associated with customers and prospects. Pinpoint Predictive has been recognized by several notable organizations including the 2022 Insurtech 100 Award, Insurtech Vanguard, AI Breakthrough Awards 2023, Global Tech Awards 2023 (Category winner for AI), AnalyticsTech and Insurtech, and the Insurance Awards 2023 (Category winner for Insurtech in World Finance Magazine). Find out more at pinpoint.ai.

About AAIS

Established in 1936, AAIS serves the property & casualty insurance industry as the only national not-for-profit advisory organization governed by its Member insurance carriers. AAIS delivers high-quality advisory solutions, including best-in-class policy forms, rating guidelines, and data management capabilities for commercial lines, inland marine, farm & agriculture, auto, and personal lines insurers. Their consultative and unbiased approach, unrivaled customer service, and innovative technical capabilities underscore a focused commitment to the success of their Members. Together with Member carriers, insurance regulators, Partners, MGAs, and other key stakeholders, the AAIS Community is working to build a more cost-effective and sustainable industry. For more information about joining the AAIS Community, visit AAISonline.com.

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Media Contacts

Pinpoint Predictive, Inc.
Shannon Minger
Product Marketing Manager/Media Relations
shannon.minger@pinpoint.ai

AAIS
Wen Tilghman
Director, Marketing & Communications
went@aaisonline.com

Source: Globe Newswire

The post Pinpoint Predictive, Inc. Joins AAIS Partner Program, Empowering AAIS Members with Strategic Risk Selection for Enhanced Profitability 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.

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