How Predictive Analytics is Transforming Insurance Claims Management

Predictive analytics refers to statistical techniques and technologies that can be used to identify the likelihood of future outcomes. Huge volumes of data can now be processed by machine learning tools, analysed accurately, and provided to professionals in claims – who can then use them to save you and your company money. 

For insurance industry professionals, predictive analytics in insurance claims sounds like a dream come true. Predictive analytics in insurance claims has been a hot topic in recent years, rising in popularity alongside artificial intelligence tools and machine learning. 

With “ChatGPT” now a household name and AI tools sitting in the hands of virtually every consumer, claims professionals are becoming more curious about how they can get predictive analytics working for their teams. For example, predictive analytics could capture patterns of fraud before a human claims team even gets involved. Predictive analytics could flag area codes for home insurance claim risk, or even risky claims providers (like a contractor with 9 hours of billing in one 8 hour day). Deloitte’s 2023 Insurance Outlook suggested that as digitization becomes more of a priority, insurance providers were likely to partner with tech companies to extend these analytics capabilities. So have they?

Using Predictive Analytics in Insurance Claims: the Industry in 2025

Insurance providers have been working with AI solutions for many years. The release of publicly available (and often free) generative AI tools has driven experimentation and greater adoption of AI. Despite this, a report from Deloitte suggests that many insurers remain in the proof of concept stage. Hallucinations, bias, and privacy concerns have remained stubborn obstacles to widespread adoption. To mitigate these challenges, human oversight is placed at key points in the workflow, in a process known as ‘human-in-the-loop.’ 

Human oversight works naturally with predictive analytics tools, where an experienced adjuster or underwriter can work with a well calibrated tool. Predictive analytics in insurance claims use a combination of rules, modelling, text mining, database searches, and exception reporting. Using predictive analytics, an insurer could flag claims with higher litigation risk, for example, and assess the need for a more or less attention to detail (or room for more automated processing). 

Today, most health insurance providers in the United States have embraced AI, and 77% of the insurance industry is adopting AI at some stage. 

Use Cases for Predictive Analytics in Insurance Claims

When it comes down to it, predictive analytics makes it faster and less expensive to process a claim, at virtually every stage in the claims process. Using analytics tools to capture something like fraud, for example, allows the insurance company to find hidden patterns or irregularities in large datasets. In many cases, these datasets are too large for human claims adjusters to ever foreseeably analyse – just ask anyone who has ever had to turn on manual calculations for their Excel spreadsheet! 

Predictive analytics powered by AI or machine learning integrate data in real time. This data is evaluated all at once across thousands of variables, and can learn from and flag emerging patterns. For example, a predictive analytics model might flag small amounts of wind and hail damage (the most common reason for a home insurance claim) with timestamped photos, an estimate from an approved local roofer, and other claims in the area code as automatically approved. It might flag repeat thefts, complex claims like mold damage, or long term leaks as requiring more review. However even with rapid AI-driven analysis, human oversight is crucial to ensure accuracy, compliance, and sound decision-making in claim resolution, particularly as regulations continue to evolve.

The use of predictive analytics is continually being used to optimize workload, improve staffing, and reduce the complexity, and the cost, of claims. Public agencies, state funds, and government insurers are increasingly adopting these new technologies to identify gaps in care, increase visibility for service use, and better allocate resources used for claims. By leveraging real-time insights and identifying emerging patterns, these solutions not only reduce administrative burden but also improve outcomes for claimants, enabling agencies and insurers to allocate resources more strategically and proactively address risk.