Pattern Recognition Through Practical Investigation
Financial fraud doesn't announce itself. It hides in transaction patterns, timing inconsistencies, and behavioral shifts that most systems miss entirely. We've spent years learning what actually works when detecting sophisticated fraud schemes across different business contexts in Germany and beyond.
Building Detection Systems That Actually Notice Things
Most fraud detection focuses on obvious red flags. But actual fraud? It's quieter than that. Someone changes their payment timing slightly. A vendor relationship shifts without clear reason. Small pattern breaks that individually mean nothing.
Our approach combines what we call "context layering" with behavioral baseline tracking. We don't just look at transactions—we map relationship networks, timing patterns, approval chains, and how these elements interact over time. When something breaks pattern, we can see why it matters.
Baseline Mapping
We establish normal patterns across six months of activity before flagging anomalies
Network Analysis
Track how entities relate and when those relationships change unexpectedly
Timing Recognition
Identify when transaction timing shifts suggest intentional pattern masking
Threshold Awareness
Notice activities that stay just under reporting limits consistently
Lennart Voss
Investigation LeadFifteen years tracking financial irregularities across manufacturing, retail, and service sectors
How Investigation Actually Happens
People think fraud detection is about sophisticated algorithms. Sometimes, sure. But often it's about knowing which questions to ask when data doesn't quite fit expected patterns.
When we investigate potential fraud, we start with what changed. Not what looks wrong—what shifted from established patterns. A vendor who always invoiced on the 15th suddenly invoices on the 28th for three months. Why? Maybe it's innocent. Maybe procurement deadlines changed. Or maybe someone figured out when oversight is lighter.
Pattern Deviation Identification
We flag activities that break from established behavioral baselines, then prioritize based on context and potential impact rather than chasing every anomaly
Relationship Mapping
Build networks showing how entities connect—vendors, employees, accounts. New connections matter less than relationship changes in existing networks
Evidence Documentation
We document findings in formats that actually work for legal review and regulatory reporting. Pattern evidence needs clear presentation to be useful
Three Phases of Verification Work
Fraud investigation isn't linear, but these phases represent how we typically move from initial detection through to actionable findings. Each case determines its own timeline based on complexity and evidence availability.
Initial Assessment
Review flagged patterns to determine whether investigation makes sense. Many anomalies have legitimate explanations—we filter for cases where pattern breaks suggest intentional activity worth examining further.
Deep Analysis
Map complete relationship networks and timing patterns around flagged activities. This phase involves detailed transaction tracking, approval chain review, and identifying who had access when irregularities occurred.
Documentation
Compile findings into formats suitable for internal review, legal action, or regulatory reporting. Evidence needs clear presentation—we focus on building documentation that actually supports decision-making.
