Internal Fraud Detection

Most people think of fraud cases as coming from outside the company, like requests from certain countries or sometimes geographies. But fraud can come from inside too. Sometimes from a bad actor who got past your defenses, but even from your own internal employees who might have easier access to certain sensitive information and systems. Internal fraud is even harder to find than external fraud and can be more reputation-damaging as well.

Company: Hi-tech real-estate & finance

Sector: Hi-tech real-estate & finance

Role: CTO

Customer challenge

the company was suffering fraud abuse that is common in the real estate and insurance industry where if data about an active deal is leaked (externally or internally), then the attacker can appear like a company employee and intercept communications with the victim, confirming to them the details about the deal that is happening, and then sending the victim false wire transfer instructions which route funds to the attacker.

This fraud is especially hard to find when it comes from an internal employee who already has access to different internal systems.


Traceable AI is able to help the company catch and stop these fraud attempts due to a few characteristics of Traceable AI’s Analytics capabilities

  1. User behavior anomaly detection – Traceable learns normal user behavior and API call patterns and flags anomalies. This can highlight illicit activity such as an employee accessing deal data that they shouldn’t be accessing.

  2. User attribution – Traceable uses intelligent user attribution to track user activity across sessions, IPs, resets, and no matter how deeply their user identity is buried. It uses this user attribution to provide an aggregated user storyline across all app activity.

  3. Trace data lake – Traceable can capture and store full request/response details of every transaction in the application landscape, with transaction relationships, enabling teams to efficiently investigate and extract critical security information about incidents or potential incidents.

How Traceable AI helped

  1. Find errant user behavior that indicates fraud –Traceable’s user behavior anomaly detection identified and flagged the unusual activity of a company employee who was accessing PII and deal data that they don’t normally use. Using Traceable’s trace data lake to investigate led them to find the wire transfer fraud taking place.

  2. Track user activity over multiple sessions over time – Traceable aggregated user activity over multiple sessions and over time using user attribution so changing IP addresses and resetting sessions didn’t impact the company security team getting a clear view of the fraud being done by the user.

Customer value in technical, business, and ROI/financial terms

  1. Avoid reputation-damaging fraud –As an institution that handles large sums of customers’ money, the company is not only a big target for fraud, but customers trusting transactions through the company is critical for success. It was important to catch and stop these frauds before their reputation was ruined.

  2. More secure systems and processes – Because Traceable was able to identify the wire transfer fraud and, using the trace data, show exactly how the data was leaked, the company is able to better and more precisely secure their systems and processes around this data to prevent this fraud from happening again.

Start tracing.
Start securing.