Why Manual Claims Workflows Are Costing Insurers

In retail, finance, and healthcare, data drives decisions. Predictive analytics guide inventory management, real-time recommendations shape customer experience, and automated workflows reduce friction at every touchpoint. Insurance claims processing, on the other hand, still relies heavily on manual processes layered with human judgment and traditional workflows. And this legacy approach often comes with a hefty price tag.

What Does Legacy Processing Actually Look Like?

For most carriers, claims processing hasn’t evolved much in the last decade. “Standard” claims processing still means manual intake, paperwork routed through multiple reviewers, and investigations triggered by rule-based systems that generate high volumes of false positives. Decisions hinge on experience and gut instinct rather than data, and what appears routine actually drags down profitability and operational agility.

Three pain points consistently appear across carriers: false positives and wasted manual reviews, high-stakes decisions made without data, and operational leakage that compounds at scale.

The Real Costs of Legacy Workflows

Wasted Hours on Low-Risk Claims

Manual workflows don’t scale. Skilled investigators spend hours reviewing low-risk cases only to find no issue. Even worse, many senior investigators report feeling underutilized, reviewing files that should never have landed on their desks in the first place. This mismatch doesn’t just waste time. It wears down morale and drives turnover among your most experienced talent.

Consider a Workers’ Comp claim flagged for a routine back injury. A senior investigator spends four hours reviewing medical records, conducting database searches, and drafting a report, only to conclude the claim is legitimate and straightforward. Meanwhile, three higher-risk claims with actual red flags sit in the queue. Multiply this across hundreds of claims per month, and the cost becomes staggering.

When resources are constantly misdirected to legitimate claims flagged incorrectly, the real risk gets obscured. Delays amplify what’s often called “operational leakage,” where inefficiencies increase settlement pressure and erode margins.

The False Positive Problem

Fraudulent claims cost insurers billions annually. According to industry estimates, fraud accounts for 5-10% of claims costs across all lines. But the challenge is more complex than it appears: traditional rule-based systems don’t just miss fraud—they flag hundreds of legitimate claims that never warranted investigation.

This creates a dual problem: Investigators spend valuable time on false leads while actual fraud slips through the cracks. Your fraud detection tools might be causing more inefficiency than they’re preventing.

Bottlenecks and Decision Paralysis

Without intelligence-driven prioritization, teams often apply resources too late in the claim lifecycle. High-stakes decisions rely on subjective judgment rather than clear risk assessment. This leads to longer settlement durations and missed opportunities for early intervention. Meanwhile, AI and analytics can reduce settlement times from weeks to days, or even just a few hours, for straightforward claims.

So what does faster, smarter claims handling actually look like in practice?

Modern Claims Processing

The alternative is already here. Data analytics and AI detect risk and fraud more accurately than rule-based methods. Predictive models enable intelligent prioritization, flagging high-risk claims for expert review and automating low-risk decisions. This shift produces substantial cost savings and allows insurers to accelerate claims handling by up to 70% compared with traditional processes.

The business impact is tangible. Leading carriers such as Nationwide Mutual Insurance Company and Tokio Marine have already shown that strategic AI adoption improves customer satisfaction while lowering operational costs by up to 15%. Carriers that stick with manual workflows risk falling further behind every quarter.

From Detection to Direction

Most modern fraud detection tools stop at identification. They’ll tell you a claim looks suspicious. They’ll surface red flags and anomalies. But then what? Identifying a problem is only half the equation.

Adjusters and investigators need actionable intelligence that bridges the gap between spotting risk and knowing what to do about it. That’s where tools like Pathfinder come in.

Pathfinder provides three capabilities that actually move claims forward:

• Automated validation through case summaries and fraud insights, so investigators aren’t starting from scratch.
• Strategic direction with tailored action plans beyond surface-level red flags.
• Quantified impact to support better resource allocation, so you can prioritize the claims that actually matter.

This approach delivers fewer false positives, better prioritization, faster turnaround, and investigators who spend their time where it counts.

Traditional claims processing carries a real cost. The future looks different: smarter, more accurate, and strategic.

Want to see how intelligent decision-making can transform your claims operation? Talk to our team today.

Check out our sources:

Navigating the Impact of AI in Insurance: Opportunities and Challenges. Databricks. (2025, November 28). https://www.databricks.com/blog/navigating-impact-ai-insurance-opportunities-and-challenges

Optimizing AI in Claims Management: How Automation is Transforming Insurance Providers. iLink Digital. (2024, December 4). https://www.ilink-digital.com/insights/blog/optimizing-ai-in-claims-management-how-automation-is-transforming-insurance-providers

Wir-Konas, M. (2025, March 12). Streamlining Insurance Claims Processes with AI and Machine Learning. Decerto. https://www.decerto.com/post/streamlining-insurance-claims-processes-with-ai-and-machine-learning