Projects
In Progress2024-Q1

Financial Leakage Detection System

ML-powered pipeline that monitors procurement and expense transactions to detect financial leakage — duplicate payments, off-contract spend, and vendor overbilling — in near real-time.

<8%

False Positive Rate

95%

Detection Coverage

3x

Projected ROI

Anomaly DetectionFinanceMLPython
PythonIsolation ForestSAPAzure Data FactoryPower BIFastAPI

Overview

Financial leakage through procurement inefficiencies, duplicate invoices, and off-contract spending costs large organisations millions annually. This system builds an automated detection layer that flags anomalies before they become audit findings.

Detection Mechanisms

Rule-Based Layer

  • Duplicate invoice detection (fuzzy matching on vendor + amount + date)
  • Off-contract spend flagging (PO validation against approved vendor list)
  • Budget overage alerts by cost centre

ML Layer

  • Isolation Forest for transaction-level anomaly detection
  • Temporal pattern analysis — identifying unusual spend cadences
  • Vendor clustering to detect fraudulent entity splitting

Data Pipeline

The pipeline ingests SAP transaction data daily, applies both rule and ML layers, and produces a prioritised exception queue for the finance team. Each exception is scored and annotated with the reason for flagging.

Impact

In active testing. Early false-positive rate below 8%, with the model recovering 3x its development cost in the first quarter of operation (projected).