The insurance industry is evolving into what experts now call the “decision economy.” Every success hinges on the quality of decisions made—from underwriting risks to processing claims. During a recent episode of Insurance Covered, Tom Wilde, CEO of Indico Data, spoke about how better data is revolutionizing these decisions, helping insurers adapt and thrive.
Peter Mansfield, a partner at the law firm RPC, recently sat down with Tom Wilde, CEO of Indico Data, on the Insurance Covered podcast to explore the critical role of better data in this new economy. Here are the key insights from their conversation and how better data is shaping the future of insurance.
Listen to the full podcast episode here: AI in Revolutionizing Data Processing with Peter Mansfield, Partner at Reynolds Porter Chamberlain
Transforming Insurance in the Decision Economy
The decision economy highlights the critical role data and advanced technologies play in transforming decision-making processes. No industry feels this shift more acutely than insurance. Mansfield asks Wilde, “I think it might be sensible to remind ourselves that insurance is all about making decisions and I’ve heard you talk about something that you describe as the decision economy. So could you expand upon that please and explain why it’s particularly relevant to insurance?”
“Insurers are in the business of making decisions,” explains Wilde. “Their success depends on answering critical questions like, ‘Should we underwrite this risk?’ or ‘How should I price this risk?’ ‘Is this claim covered by our policy?’” He emphasizes that these decisions rely entirely on the quality and usability of available data. And that those decisions are only as good as the data they’re based on.
This emerging economy underscores that it isn’t just about having data—it’s about transforming it into actionable insights that enable better, faster, and more accurate decisions. This is where the concept of the “decision supply chain” comes in. Wilde describes it as a seamless process where each step—from data ingestion to insight generation—feeds into critical workflows that improve efficiency, profitability, and customer outcomes.
What sets the decision economy apart? It’s not just about accumulating vast quantities of data. It’s about transforming unstructured and incomplete inputs—like scanned PDFs, medical records, or accident reports—into structured, actionable data that flows seamlessly into decision-making processes.
Data as the Cornerstone of Insurance
The importance of data in insurance is nothing new. From early actuarial science to modern predictive models, data has long been the foundation of underwriting, risk management, and claims processing. But there’s a challenge. Insurers possess vast amounts of data, much of it unstructured—scanned PDFs, medical records, accident reports, and loss runs.
Wilde touches on how insurers excel at collecting data but often struggle to make it useful. He mentions, “The challenge they have is the people supplying that data to them, namely the brokers and the insureds, don’t always provide that data to them in a ready to use format, or a complete format.” Wilde notes. “I think the insurance industry, almost more than any other industry, is powered by documents, right? Documents capture all of the data, typically, that insurers need to understand risk and claims and so forth. It could be very unstructured, unpredictable inputs like medical records. Doctors notes, accident reports, police reports, loss runs, and these are truly heterogeneous inputs that are not ready to be used by an insurer to make these predictions. They need that data transformed in its raw state, it’s not ready for use.”
Scans of handwritten medical histories, police reports, or loss runs are common, but insurers can’t effectively analyze them without transforming them into usable formats. Historically this has been one of the big challenges insurers have almost more than any other industry.
To bridge the gap between data collection and actionable insights, insurers are turning to Intelligent Document Processing (IDP). Wilde reveals, “IDP allows insurers to lift data from a document, put it into a schema—a structured format—and make it actionable. For instance, from a scanned medical record, IDP can extract key details like diagnoses, medications, and patient demographics for underwriting decisions.” This technology converts raw, unstructured data into consistent, structured formats that insurers’ workflows can utilize.
For instance, scanning a medical record is just step one. With IDP, crucial details like medical history, diagnoses, and treatments can be extracted and validated for underwriting or claims decisions.
From Data to Better Decisions
Efficiently transforming raw data into actionable insights is fundamental to thriving in the decision economy. Insurers can leverage advanced tools like IDP to create better data, which in turn enables smarter decision-making.
Wilde explains, “Better data is going from raw data which might be in the form of a scanned PDF, you know, application or loss around or police report or whatever it might be, and turning that into schematic data, in a way that the downstream systems expect it.”
For insurers, better data means:
- Consistency across data inputs
- Validation of critical information
- Standardized formats that systems can interpret
These practices create a “decision supply chain,” as Wilde describes it. Every step—from data ingestion to generating insights—must feed into a seamless process that ultimately impacts profitability and efficiency.
Related Content: 5 Resolutions for insurers in 2025: Embrace the decision era
How Artificial Intelligence Powers Decisions
Artificial Intelligence (AI) is at the heart of insurance’s transformation. Unlike traditional methods that merely analyze historical data, AI enables predictive and generative insights.
“AI doesn’t just expedite decisions; it makes them smarter,” says Wilde. Here are key ways AI is reshaping insurance operations:
- Predictive Underwriting: AI-driven models accurately assess risk by analyzing historical and prospective data.
- Fraud Detection: AI swiftly identifies fraudulent claims by analyzing patterns.
- Proactive Loss Prevention: Real-time AI alerts help policyholders take preventive measures, such as moving cars before a flood or reinforcing homes ahead of severe storms.
Wilde mentions the power of technologies like geospatial mapping and Internet of Things (IoT) devices to assist AI in providing real-time actionable insights. For example, smart sensors can monitor building conditions, providing early warnings for potential risks such as structural stress or water leaks.
Wilde argues that AI is transformative, “You can’t understate the transformational effect of that, and the ability for machines to hold in their understanding a near complete contextual understanding of sound, image and text is really profound, right?” He also adds, “I mean, we’re at the point now where these very large language models like. GPT 4 are so large that, you know, they may contain all of the context required. We may not have to give them much more context.”
Proactive Insurance Models
One of the most significant shifts fueled by better data and AI is the move from reactive to proactive insurance models. Traditionally, insurers would only respond after a claim was filed. Now, advancements in technology allow insurers to intervene before a loss occurs. Wilde shares an example of flooding alerts sent to policyholders, prompting them to move vehicles or secure homes ahead of a storm.
“Proactive measures like these build trust and reduce losses,” he explains. “It’s a win-win for insurers and their customers.”
Another application revolves around real-time monitoring. Smart sensors and Internet of Things (IoT) devices track metrics like temperature, humidity, or structural stress on insured properties. The insights gathered help insurers and clients address potential risks before they escalate.
Competitive Advantages of Better Data
The decision economy is more than a buzzword—it’s a competitive edge for insurers that invest in better data. Wilde highlights several benefits for insurers who prioritize data transformation and AI-driven insights, including:
- Quicker Decisions: Faster, more informed actions reduce delays in underwriting and claims.
- More Accurate Risk Assessments: Precision-rich data improves pricing and loss reduction.
- Enhanced Fraud Detection: AI-enabled technologies spot anomalies that might go unnoticed by human analysts.
Wilde explains how AI can examine data even faster than people, “AI arrives and now one of the things AI is really, really good at is examining vast amounts of data much faster and more robustly than we can do as people, right? So, I can’t stare at a database and make connections and inferences from a database with a million rows of records, right? Not possible, but a machine can do that really, really well.”
Wilde shares a compelling statistic from the field: “An insurer leveraging AI-powered decision-making reduced their claims processing time by 45% and decreased fraud-related losses by 20%.”
Related Content: Leveraging AI to Streamline Workflows and Boost Efficiency
Building a Smarter Future with AI
The future of insurance is about leveraging agentic AI—systems that take on decision-making roles once reserved exclusively for humans. AI’s impact will only deepen as insurers adopt hyper-personalized policies and real-time underwriting adjusted for shifting risks. Wilde explains to Mansfield, “Agentic AI is the dawn of AI’s ability to take initiative and solve a task and sort of understand how to solve a task and define its own approach to a task, somewhat autonomously. We’re at the very dawn of that, but agentic AI is gonna use all of those tools at its disposal to accomplish a task that we give it.”
Wilde predicts that even foundational elements, such as policy durations, could shorten. “AI and real-time data,” he explains, “may allow insurers to offer policies tailored to specific, immediate risk factors—moving away from the standard one-year cycle.”
These possibilities mark a radical shift in how insurers think about risk, customer engagement, and operational efficiency.
The Future of Insurance Lies in Data-Driven Innovations
“Insurance allows us to do a tremendous amount of things that we couldn’t do if there wasn’t a safety net, right, or sort of risk protection. So insurance is absolutely a vital engine to the global economy that wouldn’t exist without it, right?” Wilde explains. “I think that my experience with insurers is that they are very eager to improve.”
Wilde explains the importance of insurance and how AI and better data are equipping insurers to make smarter, faster, and more proactive decisions. This approach not only helps businesses grow but also creates stronger partnerships with customers.
The decision economy represents the next frontier for insurance, emphasizing the value of better data and technological innovation. To stay ahead, insurers must tackle challenges like unstructured data and outdated workflows. By investing in intelligent technologies like AI and IDP, they can transform operations and sharpen their competitive edge.
Better data leads to better decisions—and the time to act is now.
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Frequently asked questions
- How can insurers ensure the ethical use of AI in decision-making? To ensure ethical AI use, insurers must prioritize transparency, accountability, and fairness in their models. This involves regularly auditing AI algorithms for biases, ensuring compliance with data privacy regulations, and maintaining human oversight in critical decision-making processes. Establishing clear guidelines for AI governance, providing explanations for automated decisions, and incorporating feedback mechanisms can help build trust with policyholders and regulators while minimizing the risk of unintended consequences.
- What challenges do insurers face when integrating AI with legacy systems? One of the biggest hurdles insurers encounter is the incompatibility between modern AI-driven solutions and outdated legacy systems. Many insurers still rely on decades-old infrastructure, making it difficult to seamlessly integrate new technologies. Data silos, inconsistent formats, and security concerns can slow down implementation. To address these challenges, insurers must adopt a phased approach, investing in middleware solutions, cloud-based storage, and data standardization practices to bridge the gap between legacy and modern systems.
- How does AI impact fraud detection in insurance, and what are its limitations? AI enhances fraud detection by identifying patterns and anomalies that might go unnoticed by human analysts. Machine learning models can analyze vast amounts of historical claims data, flagging suspicious activity in real-time. However, AI is not foolproof—fraudsters continuously evolve their tactics, and AI systems require constant updates and training to stay ahead. False positives can also be a challenge, leading to unnecessary claim delays. To maximize effectiveness, insurers should use AI in conjunction with experienced fraud investigators, ensuring a balanced approach that combines automation with human expertise.
