New tools and technology are emerging that collect and analyze vast amounts of data from every corner of the shipping industry. Yet, despite these advancements, the marine cargo insurance industry may be missing out on a significant opportunity to leverage them in order to improve their pricing decisions and risk management.
Over the past few decades, marine cargo insurers have strived to make cargo insurance programs easier to administer and broader in coverage. To that end, marine cargo insurance policies have shifted from being largely declaration driven, to being more sales or revenues adjusted, where the details of the individual shipment transactions are not captured in the underwriting or policy adjustment process. The marine cargo insurance industry has an opportunity to embrace the availability of more granular shipment data and change how they incorporate data into their policy structures and risk assessment.
Here are three ways the marine cargo insurance industry can augment decision making and, in turn, improve underwriting, portfolio management, and event response by making better use of real-time and readily available supply chain data:
1. Ask for the Data
An issue throughout the industry is that many insurers and brokers are simply not asking their insureds to provide them with more granular data. Through transportation management systems (TMS) and electronic bills of lading (eBOLs) and customs documentation, the logistics industry is collecting and distributing a vast array of information about how goods are moving through the supply chain. This data is often already being enhanced through integrations with Internet of Things (IoT) devices within the cargo or the container that can include cargo values, routes, geopolitical risks, weather patterns, port conditions, among other useful information. However, if the marine cargo insurance industry is not aware that this data is available, or they don’t know how to ask for the data, then clients are not going to provide it.
By not asking for this data from clients, the industry is missing the opportunity to create tailored, dynamic policies that reflect the real-time risks as well as some of the specific needs of the businesses they insure. The traditional approach of setting static coverage based on broad categories of goods and routes can be replaced with detail that was never available in the past. With the right data, policies can be refined to better meet specific insurer appetites, as well as offer more precise pricing, more efficient limits, and broader coverage, better aligned with the actual risks shippers face.
For example, consider how cargo value accumulation and route optimization data could impact pricing decisions. Knowing the exact route goods are taking, particularly through high-risk areas, could allow insurers to provide pricing options that may promote the selection of lower risk routing, and/or allow the shipper options to work with alternative markets, or alternative coverage structures to better manage that specific risk. It also becomes feasible to look at creating new risk mitigation solutions to address traditional insurance limitations, such as coverage for trade disruptions or loss of market due to delay.
2. Standardize the Data
Collecting data is just the first step. To fully leverage the benefits, the marine cargo insurance industry must standardize data so that it can be reviewed consistently across a wide portfolio. In its desire to meet specific client needs, particularly in the way commodities are documented and categorized and coverage is constructed, the marine cargo insurance industry has created highly tailored wordings that address risk differently, but generally lead to a very similar coverage result. This practice also can make it difficult to analyze and compare risks across different shipments, routes, and insureds — even where the exposure and coverage is likely analogous.
By working to standardize the collected data, such as cargo descriptions and shipping routes, the industry can not only achieve better reporting, but can also start to unlock more sophisticated data analysis. Standardized data allows insurers to compare risks across clients, identify patterns, streamline pricing and even start to predict potential risk issues before they arise. It also provides a common language for insurers, shippers, and shipping companies, making it easier to communicate about risks and coverage needs.
For instance, standardizing how cargo is documented based on existing standardized documentation, such as eBOLs and customs documentation, can give insurers a more granular understanding of the goods they are covering. This deeper insight allows for more precise risk assessments and better alignment of coverage with actual needs. In turn, this specialized coverage alignment could lead to more competitive pricing, as insurers would have a clearer picture of where risks lie and could adjust premiums accordingly.
3. Use the Data to Create More Flexible Policies
With data properly collected and standardized, the structure of marine cargo insurance policies can keep pace with the evolution of global supply chains. Traditionally, policies have been designed to simplify coverage for insurers, often at the expense of fully reflecting the complexities of modern shipping. But today’s supply chains are highly integrated and data-rich, offering a wealth of information that could be used to create more dynamic, flexible policies that can be better integrated into the overall process of moving goods, such as freight costs, contracting, and financing.
To take full advantage of the data available, the marine cargo insurance industry needs to rethink both the structure and distribution of marine cargo policies. Rather than relying on rigid, one-size-fits-all coverage that are tailored to the cargo owner, insurers could develop policies that are flexible and responsive to real-time data. For example, policies could automatically adjust based on the entire route a shipment takes or the geopolitical risks present at the time of transit. This structure could not only help ensure that businesses are adequately covered but also allow insurers to price policies more accurately based on the actual risks involved.
Such innovations could also open the door to new types of coverage. For example, with better visibility into shipping routes and cargo values, insurers could offer parametric insurance that triggers payouts based on predefined conditions, such voyages being delayed beyond a specified number of days, or cargo sensors showing an impact or temperature variation beyond a certain threshold.
The Real-World Impact of Data-Driven Claims Handling
The benefits of collecting and standardizing data extend beyond pricing decisions. They also have a significant impact on how claims are handled.
In a traditional claim scenario, insurers often rely on the shipper’s account of what happened and lengthy investigations to determine the validity of the claim. With access to accurate, real-time data, insurers can validate claims expeditiously, based on records coming from IoT devices and telematics that are monitoring the condition and handling of the cargo, reducing the time and cost involved in processing claims.
Further, with access to real-time data, insurers can monitor shipments as they move through the supply chain and identify potential issues before they result in claims. For example, if a shipment deviates from its planned route or encounters severe weather, insurers could proactively engage with the shipper to assess the situation and manage the risk before it escalates.
Predictive data can also help reduce the frequency of claims by allowing insurers to identify and address risks early. If data shows that a particular route is prone to delays or losses, insurers can work with shippers to adjust their routes or take other precautions to mitigate the risk.
It’s Time to Modernize the Marine Cargo Insurance Industry
The marine cargo industry is sitting on a treasure trove of data, but we are not using it to its full potential. By actively collecting and standardizing this data, we can revolutionize the way marine cargo insurance is priced, structured, and managed.
Nearly two-thirds of Americans are concerned about severe weather events affecting their insurance coverage in the next 5 years, according to Insurity’s 2024 Severe Weather P&C Consumer Pulse Survey. With this concern, 52% of respondents said they were likely to buy a policy from an insurance provider that actively invests in new technology to improve the insurance claims process after severe weather events.
The increasing frequency and severity of extreme weather events pose significant challenges for the P&C insurance industry. Hurricanes, floods, tornadoes, and other natural disasters have become more intense, leading to substantial financial losses and operational disruptions. Insurers face unprecedented challenges in predicting, assessing, and mitigating risks associated with these events as traditional methods of risk assessment and catastrophe modeling are no longer sufficient to cope with the dynamic and non-linear nature of evolving threats.
However, the advent of artificial intelligence (AI) and advanced data analytics is transforming catastrophe modeling, offering P&C insurers a powerful tool to enhance risk management and improve resilience.
The Role of AI in Catastrophe Modeling
AI-powered catastrophe modeling leverages machine learning algorithms, neural networks, and big data analytics to provide more accurate and dynamic risk assessments. This ability to integrate and analyze diverse datasets allows AI models to identify patterns, correlations, and anomalies that may not be apparent through conventional methods.
1. Enhancing Predictive Accuracy
One of the key advantages of AI-powered catastrophe modeling is its ability to enhance predictive accuracy. Machine learning algorithms can continuously learn and adapt from new data, improving their predictive capabilities over time. For instance, AI models can analyze historical hurricane paths, wind speeds, and pressure patterns to forecast the trajectory and intensity of upcoming storms with higher precision. This improved accuracy enables insurers to better anticipate potential losses and allocate resources more effectively.
2. Real-Time Risk Assessment
In the age of extreme weather, timely and accurate risk assessment is crucial. AI-powered models can process real-time data from multiple sources to provide up-to-the-minute risk assessments. For example, during a hurricane, AI can analyze live weather data, track the storm’s movement, and predict its impact on specific regions. This real-time assessment allows insurers to issue timely warnings, advise policyholders on necessary precautions, and mobilize response teams to mitigate damages.
3. Dynamic Scenario Analysis
AI-powered catastrophe models excel in dynamic scenario analysis, which is essential for understanding the potential impacts of various extreme weather scenarios. Insurers can use AI to simulate different climate conditions, storm intensities, and geographic locations to evaluate the potential losses under each scenario. This capability helps insurers develop robust risk management strategies, optimize their portfolios, and set appropriate premiums based on a comprehensive understanding of potential risks.
4. Improving Claims Management
Efficient claims management is vital in the aftermath of extreme weather events. AI-powered catastrophe models can streamline the claims process by automating damage assessment, fraud detection, and resource allocation. For instance, AI algorithms can analyze post-disaster imagery to assess property damage and estimate repair costs. This automation reduces the time and effort required for manual inspections, allowing insurers to process claims faster and provide policyholders with timely settlements.
5. Enhancing Underwriting Practices
AI-powered catastrophe modeling also has a significant impact on underwriting practices. Insurers can leverage AI to assess the risk profiles of individual properties more accurately. By analyzing factors such as building materials, location, historical claims data, and exposure to extreme weather events, AI models can provide underwriters with detailed risk assessments. This granularity enables insurers to offer more tailored policies and pricing, ensuring that premiums accurately reflect the level of risk.
6. Building Resilience and Mitigating Losses
Beyond risk assessment and claims management, AI-powered catastrophe modeling plays a crucial role in building resilience and mitigating losses. Insurers can use AI to identify vulnerabilities in infrastructure, supply chains, and critical assets. By understanding these vulnerabilities, insurers can advise policyholders on proactive measures to strengthen their properties and reduce potential damages. Additionally, AI models can help insurers optimize their reinsurance strategies, ensuring adequate coverage for catastrophic events.
Will Your Org Bet on the Future of AI-Powered Catastrophe Modeling?
As the insurance industry continues to adapt to the challenges posed by severe weather, AI-powered catastrophe modeling will play an increasingly vital role. The ability to process real-time data, enhance predictive accuracy, and perform dynamic scenario analysis will empower insurers to make more informed decisions and better protect their policyholders. Moreover, ongoing advancements in AI and machine learning will further refine catastrophe models, enabling insurers to stay ahead of emerging risks.
Embracing these technologies is essential for insurers to navigate the evolving landscape of climate change and ensure long-term sustainability. By doing so, insurers can not only meet the rising expectations of their policyholders but also safeguard their own future in an increasingly unpredictable world.
Staying ahead of the curve is not just about offering better products or services—it’s about mastering the intricacies of regulatory compliance. Regulatory bureau maintenance, often seen as a necessary burden, can actually be transformed into a strategic asset that sets your organization apart. By approaching regulatory maintenance not just as a compliance requirement but as a core component of your business strategy, P&C insurers can leverage their compliance expertise to build trust, enhance operational efficiency, and ultimately, gain a competitive edge.
The Compliance Landscape: A Complex Terrain
The P&C insurance industry operates under a complex web of regulations that vary by state and region, with constant updates and changes that can impact everything from pricing to claims handling. Keeping up with these changes requires a significant investment of time, resources, and expertise.
For many insurers, regulatory bureau maintenance is viewed as a cost center—an unavoidable aspect of doing business that does little to contribute to the bottom line. However, this perspective overlooks the strategic opportunities that effective regulatory management can provide. By rethinking regulatory maintenance as a proactive, value-driven function, insurers can turn this perceived burden into a powerful competitive advantage.
Building Consumer Trust Through Compliance
At its core, insurance is a business of trust. Policyholders must trust that their insurer will be there when they need them most, and regulators must trust that insurers are adhering to the rules designed to protect consumers. By excelling in regulatory compliance, insurers can strengthen their relationships with both customers and regulators.
For example, insurers that consistently demonstrate a commitment to compliance are more likely to earn the trust of regulators, which can result in more favorable treatment during audits and examinations. This can lead to fewer penalties, lower regulatory costs, and a smoother approval process for new products and services. Moreover, a strong compliance record can be a selling point to consumers who are increasingly aware of the importance of regulatory oversight in protecting their interests.
Insurers that leverage their compliance expertise as a marketing tool can differentiate themselves from competitors by highlighting their commitment to ethical practices and consumer protection. This not only builds trust but also enhances the insurer’s brand reputation, attracting customers who prioritize reliability and integrity.
Enhancing Operational Efficiency
Beyond trust, effective regulatory bureau maintenance can drive significant operational efficiencies. By staying on top of regulatory changes and integrating them seamlessly into business processes, insurers can avoid the costly disruptions that often accompany compliance failures. This proactive approach minimizes the risk of fines, legal battles, and the need for costly corrective actions.
Moreover, regulatory maintenance provides an opportunity to streamline internal processes. Insurers that invest in technology to automate regulatory updates can reduce manual workloads, allowing their teams to focus on more strategic activities. Automation tools can quickly identify and implement necessary changes, ensuring that policies, forms, and rate filings are always up to date without requiring extensive manual intervention.
This level of efficiency not only reduces operational costs but also accelerates time to market for new products. In an industry where speed and agility are critical to maintaining a competitive edge, the ability to quickly adapt to regulatory changes can be a game-changer.
Leveraging Data for Strategic Insights
Regulatory bureau maintenance also presents a unique opportunity to gather valuable data that can inform broader business strategies. The data collected during the compliance process can provide insights into market trends, customer behavior, and emerging risks. For instance, analyzing the frequency and nature of regulatory changes can help insurers identify shifts in the market or anticipate new areas of regulatory focus.
By leveraging this data, insurers can make more informed decisions about product development, pricing strategies, and risk management. For example, if regulatory updates indicate an increased focus on certain types of claims or geographic regions, insurers can adjust their underwriting criteria or develop new products tailored to these emerging risks.
In this way, regulatory maintenance becomes more than just a compliance function—it becomes a source of competitive intelligence that can guide strategic decision-making and help insurers stay ahead of the competition.
Are You Ready to Transform Compliance into a Strategic Advantage
To turn regulatory bureau maintenance into a competitive advantage, insurers must adopt a mindset that views compliance as an integral part of their business strategy rather than a separate, burdensome task. This requires investment in technology, processes, and people to ensure that regulatory updates are managed efficiently and effectively.
It also requires a commitment to continuous improvement. Insurers should regularly assess their regulatory processes to identify areas for enhancement, whether through automation, better data integration, or improved communication between compliance teams and other business units.
Finally, insurers must recognize the value of compliance in building trust and enhancing their brand reputation. By promoting their commitment to regulatory excellence, insurers can differentiate themselves in a crowded marketplace and attract customers who value reliability and ethical business practices.
Is your organization equipped with the right foundation for a successful cloud project? Without it, your cloud initiatives could fail before they even start. Discover these essential strategies and set your cloud projects up for success. The Insurance Board expert Jonathan Victor shares his top 3 tips for CIOs on how to ensure they achieve a successful cloud transformation.
AI-powered chatbots have surged in popularity recently, yet many P&C insurers remain skeptical about their true value. The Insurance Board expert, Frank Heaps, dives into why a chatbot could be the game-changer your business needs, revolutionizing a critical aspect of your operations.
At AWS Re-Invent 2023, Dr. Swami Sivasubramanian, Vice President of Data and AI at AWS, likened AI’s relationship with human-supplied data to a mutually beneficial partnership, akin to the symbiotic relationships found in nature. While this metaphor captivates our imagination and underscores the potential synergy between humans and AI, it can also be misleading. It is important to remember that AI is not a living organism acting in its own interest for the benefit of itself, or for humans for that matter.
Symbiotic relationship of a whale shark and feeder fish
From your customer’s perspective, it is an extension of your company; the experience and relationship your customer has with the AI directly impacts how your customer thinks of you. In the insurance industry, where trust is paramount but often lacking – due to the complexity of policies, lack of transparency, and press coverage among other things – the adoption of AI presents both an opportunity and a challenge. Without thoughtful implementation, AI has the potential to exacerbate existing trust issues.
Here are 4 key factors that can erode trust in AI:
1. False identity
Failure to clearly distinguish between AI and human interactions can lead to confusion, frustration, and mistrust. Imagine a claims situation where a customer pours their heart out to a voice AI, only to find out it isn’t human after the fact. Whether through chat, voice, or video, users should always be aware when they’re engaging with AI versus a human representative. Transparency fosters trust and ensures that users know what to expect from each interaction.
2. Overpromising AI Capabilities
Misrepresentation of AI’s capabilities can set unrealistic expectations and ultimately disappoint users. If your AI is trained on a few key workflows, don’t allow for open-ended situations it can’t assist with. In the best case, your user will get frustrated by not getting the information or action needed from it. In the worst case, your AI might make up something wildly inaccurate (hallucinate) that could harm your business or reputation. It’s essential that your AI communicates clearly about how it can assist users and when human intervention may be necessary.
3. Forcing AI
It is important to recognize that customers may occupy different stages of the AI adoption curve. As highlighted in Insurity’s recent survey (link here), consumers are mixed on their perception of AI use in the Insurance industry. Acknowledging that some users may prefer human interaction over AI-driven interfaces, it’s essential to offer flexible engagement channels that accommodate diverse preferences. Prompt the user to start with AI to help onboard users to a new AI experience but offer an easily accessible option to speak to a human representative. This shows you understand the variability in users’ comfort levels and ensures that all customers feel supported and valued.
4. Lack of transparency
Users deserve insight into the data-driven decisions made by AI. Providing explanations for decisions and avenues for reporting errors both empowers users to understand AIs decisions, and helps you correct potential biases or inaccuracies in your AI. Building AI systems that are capable of evaluating and communicating their own decision-making process is just good practice in a highly regulated industry like Insurance. Admittedly, this isn’t easy: it means you must train the AI to be introspective and transparent to be able to tell you why it made the decision it made. But doing so reinforces accountability and builds both internal and external confidence in your AI.
AI Employee Persona
Human-centered AI best practices can help steer clear of these risks and foster trust. One beneficial human-centered AI methodology is to conceptualize AI as a virtual employee, complete with a defined role, goals, need, capabilities, and personality traits. Creating an AI persona that articulates these attributes helps your business ensure that your AI has the right data, directives, and personality to engage with your customer and reduce risks when faced with situations it isn’t suited for. An AI persona can also help place your AI in the ecosystem of your users’ workflows and experiences, ensuring a positive interaction and experience by your users.
As you consider your AI roadmap, it is important to recognize the importance of cultivating trust, especially in an industry such as insurance, which is both highly regulated and which struggles with customer trust as it is. By thinking of AI as a representative of your company and creating a persona for it, you can be better prepared to ensure it communicates candidly, transparently, and within the constraints of its abilities. With care and human-centered AI design practices, insurance companies can leverage AI to enhance user experiences while mitigating trust issues.
