Fraud detection
Detect irregularities in payment and booking data.
Fraud detection uses pattern recognition to identify suspicious postings, duplicate invoices and unusual payment routes, giving auditors prioritised leads.
- Lower risk
- Cost reduction
- Easier compliance
fraud cases
audit effort
payback
hit rate
Calculated from avoided losses and reduced audit staffing.
- 01
Data integration
Consolidate data from ERP, MES and further sources for Fraud detection.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the Fraud detection model on historical data.
- 04
Pilot
Pilot Fraud detection in one area and collect feedback.
- 05
Rollout
Scale the solution and integrate it into operational processes.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Financial data
Cost centres, budgets, cash flows and invoices.
ERP data
Master and transaction data from ERP.
Documents
Specifications, quotes, contracts and reports.
Customer data
Contracts, cases and communication.
Supplier data
Lead times, certificates, prices and ratings.
HR data
Shift plans, qualifications and absences.
CFO
Receives reliable financial and risk metrics.
Controlling
Quantifies effects and supports budgeting.
IT
Builds on a scalable and secure data infrastructure.
Management
Receives reliable metrics for strategic decisions.
Lower risk
Cost reduction
Easier compliance
More transparency
Less manual work
With a solid data foundation this use case gets faster, cheaper and far more stable.
Central data platform
Real-time data integration
Scalable analytics pipelines
Reusable data products
Automated reporting
Metrics are provided without manual effort.
Compliance reporting
Audit-ready data supports evidence.
BI reporting
Metrics are delivered in dashboards.