In an increasingly complex claims environment — marked by higher customer expectations, growing fraud risks, and rising litigation costs — insurers need tools that not only streamline operations, but also improve decision-making. Thanks to advanced analytics, claims management is turning into a strategic advantage.​ In fact, insurers identified as “top performers” by McKinsey reported a 10 to 25% increase in operating profits due to their use of these tools.   

Despite having access to vast amounts of data, many claims organizations struggle with siloed data systems and unstructured data; manual, inconsistent processes that drive up costs and increase the risk of errors; and limited or inaccurate fraud detection and claim severity capabilities.  

These challenges hinder an insurer’s ability to operate proactively, scale efficiently, and provide seamless experiences across the claims life cycle. To remain competitive and meet evolving expectations, insurers must adopt advanced analytics solutions that deliver actionable insights and unlock new value from their data assets. 

Applying Advanced Analytics to Claims 

What do we mean by advanced analytics? The term encompasses a range of artificial intelligence (AI) capabilities and other techniques, including predictive modeling, machine learning, and data visualization, which collectively enhance the efficiency and effectiveness of claims processes. By leveraging these advanced analytics capabilities in claims management, insurers can accelerate claims resolution, reduce loss costs and fraud exposure, enhance customer experience, and improve adjuster productivity and accuracy.  

From first notice of loss (FNOL) to final settlement, data-driven insights empower claims teams to act faster, smarter, and more strategically. Here are five powerful use cases for insurers to enhance their claims management processes with advanced analytics:

1. Estimating Litigation Propensity

According to the Swiss Re Institute, litigation-related costs have significantly impacted U.S. liability claims. Commercial casualty insurance losses grew at an average annual rate of 11% over the past five years — reaching $143 billion in 2023. That’s more than the total insured losses from global natural catastrophes in the same year. Swiss Re attributes much of this increase to aggressive litigation practices, particularly in lines exposed to bodily injury claims. 

AI-driven litigation propensity models provide actionable insights at the FNOL stage, enabling insurers to proactively identify and mitigate potential litigation risks. By analyzing historical trends, claimant behavior, and legal involvement, these models assess the likelihood of a claim leading to litigation. This proactive approach allows adjusters to manage complex claims more effectively, potentially reducing legal costs and improving settlement times.

2. Identifying Claims Fraud

Fraudulent activities, such as intentional misrepresentation or exaggeration of claims, result in unwarranted payouts and increased premiums. According to Forbes, an estimated 20% of insurance claims are fraudulent. Advanced analytics can detect potential fraud early by analyzing patterns and anomalies in claims data, reducing manual investigation time and minimizing risk exposure.​  

A workers’ comp insurer ValueMomentum worked with saw an 11% improvement in detection of claims fraud and a 5% reduction in fraudulent claims payouts by integrating AI-powered advanced analytics capabilities with its Guidewire core systems. Real-time claim scoring assigns each new claim a fraud probability score, and behavioral analytics monitor claimants’ digital footprints for any suspicious or contradictory information. High-risk claims get flagged for manual review while suspected fraudulent claims undergo deeper investigation. The AI models are continuously updated to improve accuracy. 

3. Improving Loss Estimation

Predictive analytics can provide real-time loss estimates based on historical data and similar claims, helping adjusters make faster, more accurate decisions. These models draw from thousands of data points — such as claim type, policyholder history, location, and even weather conditions — to generate more precise loss projections than traditional methods. 

Using these analytics not only speeds up the claims resolution process, but it also supports better reserve management and reduces the risk of over- or underpayment. Real-time loss estimation can also go a long way toward improving customer satisfaction; it can help carriers provide policyholders with faster and more accurate updates on potential payouts, reducing uncertainty and increasing transparency.

4. Accelerating Segmentation, Assignment, and Knowledge Management

AI-powered claims knowledge management and Q&A solutions streamline the process by providing instant access to accurate information and automating responses to common queries. Analytics can also help segment and prioritize claims based on complexity or urgency, ensuring they’re routed to the right adjusters. 

Improving these areas is critical to reducing cycle time and rework. Smart segmentation ensures high-risk claims are escalated early, while straightforward ones are fast-tracked. Intelligent assignment boosts productivity by matching claims to adjusters based on expertise and availability. And strong knowledge management empowers teams, especially newer adjusters, to make faster, more consistent decisions backed by historical data and best practices.

5. Proactively Strengthening Customer Retention

Claims experiences are a key driver of customer loyalty — or dissatisfaction. Advanced analytics can be used to model policyholder behavior and identify customers at risk of churn. Insurers can then use these insights to personalize communications, offer targeted retention incentives, or streamline the claims journey for high-value segments. By addressing dissatisfaction early, insurers can improve retention as well as strengthen long-term brand trust. 

ValueMomentum recently worked with a Midwestern carrier to improve the efficacy of its phone-based customer retention efforts. After building a model that determined which customers were likely to churn, in addition to a list of factors that may lead the customer to renew, personalized insights helped call center agents tailor their conversations and improve their pitches. This strengthened customer relationships and led to higher retention rates.  

By integrating advanced analytics into their operations, insurers can transform their insurance claims management processes, leading to increased efficiency, reduced costs, and improved customer experience.  

Enhancing Claims Management 

Advanced analytics offers insurers a path to reimagine claims management — from speeding up decisions and reducing litigation risk to improving accuracy and customer satisfaction. The use cases outlined above show how analytics can deliver measurable value across the entire claims life cycle. Whether it’s detecting fraud earlier, segmenting claims more effectively, or refining loss estimates, the benefits of data-driven decision-making are clear. 

More importantly, integrating analytics into claims workflows enables carriers to shift from reactive claims handling to proactive claims strategy. With litigation rates and customer expectations both on the rise, now is the time to invest in advanced analytics as a core part of a claims management approach. 

Interested in learning more about advanced analytics in insurance? Read our whitepaper, “Driving Business Value with Insurance Data Analytics” to gain insights into how these tools are being employed across the insurance value chain.