Success Story: Making the Invisible Visible – How Did FRAUDFLOW Disrupt a 1.5 Million TL Fraud Network with an Ordinary Claim File?

In the insurance industry, sometimes the biggest threats hide behind the most ordinary-looking files. At this point, having the right technology turns a reactive payment process into a proactive financial protection shield.

On January 5, 2026, a standard traffic accident report hit the system of one of our customers. At first glance everything seemed routine; A standard accident, multiple participants and a classic claim payment risk of approximately 290,000 TL.

Had it gone through traditional, superficial analysis processes, this file would likely have been overlooked and closed as a routine payment transaction. However, FRAUDFLOW stepped in and revealed that this single claim file was actually just the "visible face" of a huge organized abuse network.

Operational reality: Why could it escape human eyes?

The burden on claims departments is increasing day by day. While an average-sized operations center has approximately 4,000 open files at a time, teams' daily manual review capacity remains at only around 150 files (3.75%).

With such a workload, it is almost impossible to manually detect hidden links between files. FRAUDFLOW was designed to achieve exactly this impossible.

How did FRAUDFLOW work? go below the surface

Instead of just evaluating the file based on the conditions of that day, FRAUDFLOW started an in-depth analysis with its powerful analysis engine.

timeline analysis

The system scanned the crash participants' history within seconds and detected a surprising pattern. The same participants were also involved in different accidents on June 26, 2025 and December 10, 2025.

Network (network) analysis

Our AI-powered graph-based network analysis proved that the events were not random. The accidents were chained together; The participant in one accident became a different actor in the next accident. This was a complete "Chain-Linked Accident Network" (chain accident network) model, as it is known in the literature.

Red flags detected

FRAUDFLOW's artificial intelligence algorithms instantly reported these critical indicators:

• The same people assume different roles such as victim, criminal or witness in different accidents
• Suspicious participant intersections between files
• Clustering of events in a specific location within the same city
• Systematic and planned time intervals between accidents

When these dots were combined, the risk score rose to the top and the case was automatically placed on the review teams' "highest urgency" list.

Result and impact: From 290 thousand TL to 1.5 million TL saved

This case, which initially seemed to be a risk of only 290,000 TL for the institution, revealed a huge leak when FRAUDFLOW detected the linked files. The potential damage that the organized network operating in the background would cause to the company was at least 870,000 TL, and on average it reached 1,450,000 TL.

Thanks to FRAUDFLOW our customer:

• Decrypted invisible connection networks in seconds
• Directed limited human resources and investigation capacity to exactly the right target (the truly risky files)
• Saved millions of liras by preventing chain-linked financial losses at an early stage