In this episode of Unstructured Unlocked, hosts Michelle Gouveia and Tom Wilde sat down with Louis DiModugno, Global Chief Data Officer at Verisk, to discuss key challenges and innovations in data management and governance for the insurance industry. The conversation covered how advancements in technology, such as AI and data aggregation, are transforming how insurance carriers capture, clean, and leverage data. In this article, we’ll cover some of the highlights and main points from the conversation.
Listen to the full podcast here: Unstructured Unlocked season 2 episode 14 with Louis DiModugno, Chief Data Officer at Verisk
The evolution of data capture in insurance
One of the key topics DiModugno touched on was the evolving role of data in the insurance value chain. He emphasized how Verisk, a data analytics company, aggregates and normalizes data from various insurance carriers. These companies can then integrate this information across multiple areas, such as underwriting claims pricing and extreme events analysis.
DiModugno highlighted that one of the core challenges for insurance data analytics is the accuracy of initial data capture—which often originates at the broker or agent level, and is even recorded manually sometimes. He pointed out, “The challenges of getting that data accurately from the source is still probably one of the bigger efforts that [is] going on in our industry.” Once data reaches Verisk, the focus turns to normalization and entity resolution, ensuring that policyholders can be tracked across different carriers and products.
Data quality and governance: a growing priority
A significant part of the conversation centered on data governance, a key area of focus at Verisk. DiModugno described how data governance is critical for ensuring regulatory compliance and improving data quality. He mentioned that Verisk has implemented advanced processes around data observability, stating, “I’ve taken it to the next level…not just data quality, but it’s also across ‘What do my data flows look like,’ ‘What data hardware am I using,’ and ‘How is the data being used, and [what’s] the governance around that usage?’”
For carriers, the emphasis on data governance is vital because the cleaner and more accurate their data, the better their AI models and other predictive tools will perform. This is an element of data quality management that Indico is deeply familiar with. If the data that insurance companies use isn’t under strong governance to begin with, it can result in skewed analytics, leading to poor decision-making in underwriting and claims processes, increased risk exposure, and inefficiency. Robust data governance isn’t just about compliance; it’s about improving the reliability and precision to drive better business outcomes.
DiModugno noted that this growing attention to governance is even more important in the age of AI and machine learning, where high-quality data significantly influences the outputs of predictive models. Verisk is making sure that the data they process meets a stringent quality threshold, which in turn builds trust in the tools and models they offer back to carriers.
Related content: Automated decisioning in insurance: Enhancing underwriting efficiency and accuracy
Managing data complexity
The conversation also touched on the complexity of working with data in the insurance sector. Historically, insurance carriers have faced difficulties due to data silos, where underwriting data and claims data are often kept separate. DiModugno explained the importance of entity resolution in solving this issue, stating, “If I’ve got a data set that I’ve got 90% confidence in and I’ve got another data set that I’ve got 90% confidence in… I don’t add them all up and have 90% confidence. I literally have a multiplicative issue… 0.9 x 0.9 [which is 0.81% confidence]. …Just recognize the fact that you’re probably degrading your data set each time you add something else to it.”
He emphasized that Verisk is working on ways to resolve data across different datasets, assigning a unique identifier to each policyholder or company. As information is integrated from different sources into a cohesive whole, there will be more accurate modeling and improved data quality.
The role of AI in insurance data management
DiModugno also discussed the role of AI (and generative AI) in improving efficiency and data processing within the insurance space. Although AI certainly offers incredible opportunities, DiModugno argued that the use of artificial intelligence in data processing makes data quality even more important. Indico helps insurance companies automate their data intake and analysis with AI to improve decision making. Our experience is in direct alignment with DiModugno’s observations—it doesn’t matter how well we can automate a client’s processes if the data itself isn’t trustworthy and accurate.
On the podcast, he stated, “…What we are seeing, especially where we are right now in the AI and the generative AI space, [is] that data being as clean and as high quality as possible… increases the confidence in the output of those models. …The better my data is, the more confidence I have in any model or product that I have as an output from it.”
As an example, Verisk has been able to streamline internal contract analysis using AI, saving significant time and resources. The key to their success in this application, however, is that they utilize a “white box” AI model, meaning that they can always see the data points that their models are referencing for each output and adjust accordingly. Indico’s Intelligent Intake solution is also a white box AI solution. “Black box AI,” on the other hand, can lead to issues with regulatory compliance, as users are unable to see the inner workings of how the model arrived at a conclusion or chose a certain output.
Related content: Improve claims accuracy and enhance risk management through AI
The future of data in insurance
The podcast wrapped up with a discussion of the future of data management in insurance, where DiModugno shared his vision of how advancements in data aggregation and AI will shape the industry. He envisions a future where consumers have more clarity on their coverage and product options, thanks to improved data accessibility and transparency. “…As we democratize all of this insurance information, I think there’s going to be a really good opportunity for folks that just don’t even have access to some of the products that we’ve got in the industry today,” he noted.
We believe that as insurance companies continue to adopt more sophisticated data management strategies, the role of data governance and AI in enhancing risk assessment and customer service will only grow. Companies like Verisk and Indico, equipped with cutting-edge technologies and comprehensive data governance practices, are leaders in the next wave of innovation for the insurance industry.
The importance of data quality and compliance for the insurance industry
From data capture challenges to the growing need for data quality and observability, it’s clear that insurers need reliable, high-quality data to improve underwriting, claims processes, and overall risk management. As AI continues to accelerate change, the need for stringent data governance and compliance will only become more critical. Verisk, Indico, and other industry leaders are setting a great standard for other aspiring insurance solutions companies to follow in this area.
We’re grateful for our time with Verisk’ Global Chief Data Officer, Louis DiModugno, on this episode of Unstructured Unlocked. We hope our conversation left you with some insights to incorporate into your own company!
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Frequently asked questions
- How can smaller insurance carriers improve their data governance and AI readiness if they lack in-house technical expertise? Even without deep technical resources, smaller carriers can seek out training, consultation, and support services from Verisk and Indico. These companies often offer guidance, step-by-step onboarding, and ongoing resources to help carriers strengthen their data management practices at a reasonable pace.
- What steps can carriers take to integrate these data solutions with their existing legacy systems without completely overhauling their infrastructure? Verisk and Indico can work with carriers to assess their current systems, pinpoint key integration points, and recommend scalable tools or APIs. By blending new technology with legacy frameworks, carriers can make gradual improvements without ripping out what’s already in place.
- Will investing in data governance and AI tools ultimately save carriers money, or does it simply add another layer of expense? While there may be upfront costs, the long-term result is more efficient underwriting, fewer claims disputes, and higher overall accuracy. As these efficiencies build up, carriers often recoup their investment many times over, benefiting from reduced risk and smoother processes.
