AI-Based Fraud Detection in Logistics
A global logistics enterprise used AI-based fraud detection to identify suspicious orders, shipment patterns, and delivery activity while reducing fraud risk.
Business Process Challenges
A global logistics and supply chain company operating across multiple regions manages a large volume of orders and shipments every day. With increasing digital transactions, the organization needed a more intelligent way to identify fraudulent orders, suspicious shipment behavior, and fake delivery confirmations.
The existing fraud-detection process relied heavily on predefined rules and manual investigation. This made it difficult to identify new fraud patterns while creating a high volume of manual investigations.
Key challenges included:
- Fake delivery confirmations
- Duplicate or suspicious orders
- Unusual shipment routes
- High-value fraudulent orders
- False positives affecting legitimate customers
- Fraud patterns spread across multiple transactions
- Difficulty detecting previously unseen fraud patterns
Our Strategic Approach
Softree implemented an AI/ML-based fraud detection engine capable of analyzing both historical and real-time transaction and shipment behavior.
The system evaluates multiple signals, including order value, customer behavior, delivery address, shipment history, delivery confirmation, tracking information, shipment routes, order frequency, and transaction timing.
The AI generates a fraud-risk score and classifies transactions into:
Low Risk → Medium Risk → High Risk
The solution uses the following processing flow:
Transaction Data → Data Validation → AI Analysis → Risk Score → Classification → Business Action
The QA strategy went beyond traditional functional testing. It included functional testing, AI model validation, positive and negative testing, false-positive and false-negative testing, boundary testing, data validation, API testing, regression testing, model-version testing, explainability testing, performance testing, and end-to-end testing.
How we delivered it.
Explore the Solution Through visuals
What changed for the client.
The AI-based solution helped move the organization from rule-based fraud detection toward intelligent behavioral analysis.
Expected business benefits included:
- Improved fraud detection
- Reduced fraudulent shipments
- Reduced financial losses
- Lower manual investigation effort
- Better detection of unknown fraud patterns
- Reduced false positives
- Faster fraud investigation
- Improved customer trust
A key focus was ensuring that the AI could detect genuine fraud without unnecessarily disrupting legitimate logistics transactions.
The numbers behind the rollout.
The full integration layer.
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