eyko Ideas

What does the fraud signal actually look like?

Fraud loss typically surfaces after the fact in reconciliation. A Fraud Detection Playbook reads transaction-level, behavioral, and process signals to flag likely fraud patterns in real time and route high-confidence flags for investigation.

Explore Ideas

The Challenge

Fraud surfaces in reconciliation

  • Reactive detection runs after loss occurs

    Fraud often surfaces during period-end reconciliation when transactions fail to align. By then the loss has occurred. Real-time detection would have flagged the pattern at the transaction level before the loss completed.

  • Rule-based controls catch the obvious and miss the subtle

    Existing controls catch obvious fraud patterns: out-of-range amounts, blacklisted vendors, segregation-of-duties violations. They miss subtle multi-signal patterns: collusion across employees, vendor-employee relationships, transaction-pattern drift over time.

  • False positives consume investigation capacity

    When rule-based controls generate high false-positive rates, investigation teams chase noise. The real signals get diluted by the volume of false flags. Investigation capacity is the constraint, and capacity goes to the wrong cases.

How eyko Solves It

Detect the pattern, route the investigation

A Fraud Detection Playbook reads transaction-level data, behavioral signals (timing, approval-pattern, vendor-employee relationship), and process signals (workflow drift, segregation-of-duties patterns) to detect likely fraud patterns in real time. It surfaces high-confidence flags, classifies the likely fraud type (vendor fraud, expense fraud, AP collusion, payroll fraud), and routes investigation with contextualizing data attached.

Fraud Detection Watch | What
Executive Summary

The Playbook scored 240,000 financial transactions over the past quarter. 84 high-confidence fraud-pattern flags surfaced. Classification: 24 vendor-related fraud signals, 18 expense fraud signals, 22 AP collusion patterns, 12 payroll fraud signals, 8 other. Investigation routing projects 5x the value identified per investigation hour against the prior rule-based detection baseline.

Fraud Pattern Drivers
Vendor-employee relationship patterns
0.72
Transaction-pattern drift over time
0.62
Timing-and-approval-pattern anomalies
0.48
Segregation-of-duties drift
0.34
Pure amount thresholds
0.22
MetricCurrentBenchmarkStatus
Primary indicatorFlaggedTargetAction needed
Secondary indicatorMonitoringWithin rangeOn track
Trend directionDecliningStableReview required
Recommendations
1The Playbook scored 240,000 financial transactions over the past quarter.
2Full analysis available across all connected data sources.

Fraud Detection reads transaction-level data, behavioral signals (timing, approval-pattern, vendor-employee relationship), and process signals (workflow drift, segregation-of-duties patterns) to detect likely fraud patterns in real time. The Playbook surfaces high-confidence flags, classifies the likely fraud type, and routes investigation with contextualizing data attached.

FAQ

Frequently asked questions

Everything you need to know about Fraud Detection Watch.

Fraud Detection is an AI-driven detection of likely fraud patterns in real time using transaction-level data, behavioral signals (timing, approval-pattern, vendor-employee relationship), and process signals (workflow drift, segregation-of-duties patterns). The Playbook surfaces high-confidence flags, classifies the likely fraud type (vendor fraud, expense fraud, AP collusion, payroll fraud), and routes investigation with contextualizing data attached.

The Playbook reads from your ERP and GL (transaction records, approval workflow data), procurement system (vendor master, PO data), HR system (employee data, role and team context), and expense management system. At least 12 months of paired transaction-and-finding data anchors the detection.

Rule-based fraud controls rely on hard rules (amount thresholds, blacklisted vendors, segregation-of-duties checks). They catch obvious patterns and miss subtle multi-signal patterns. Fraud Detection learns the patterns of normal financial behavior and flags statistical anomalies that combine multiple signals. The two are complementary, but pattern-based detection catches the patterns that rule-based controls miss.

Yes. The Playbook produces a confidence score per flag combining multiple signal patterns. Low-confidence flags get dismissed or routed for light-touch review; high-confidence flags route to investigation. The multi-signal scoring filters out noise that single-signal detection would surface as false positives, so investigation capacity goes to the highest-value cases.

Ready to build your first Playbook?

Join the enterprises replacing weeks of manual analysis with a single prompt. See what eyko Playbooks can do with your data.

Explore eyko Beats