Payline

Multi-Tier Supply Chain Fraud Detection & Management Engine — A high-performance Node.js system for detecting, tracing, and mitigating fraud across complex supplier networks.

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Overview

Payline (Hackathon Edition) is a production-grade backend reimagined as a real-time fraud intelligence engine for multi-tier supply chains.

Modern supply chains involve multiple layers — manufacturers, distributors, logistics partners, retailers, and financial intermediaries. Fraud can occur at any layer in the form of:

• Duplicate invoicing
• Phantom shipments
• Inflated procurement costs
• Counterfeit goods
• Unauthorized vendor onboarding
• Payment diversion

This system acts as the central risk orchestration layer, integrating operational, financial, and logistics data to detect anomalies and enforce compliance across tiers.

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Problem Statement

Supply chain fraud is complex because:

• One shipment may involve multiple invoices, payments, and carriers.
• Tier-2 and Tier-3 vendors often lack transparency.
• Data sources are fragmented across ERPs, logistics APIs, banking feeds, and procurement systems.
• Fraud patterns evolve and bypass rule-based detection.

Payline addresses the N+1 problem of supply chains:

One legitimate procurement event may produce N transactional artifacts across multiple systems — and fraud can hide in the gaps between them.

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Core Features

Multi-Source Data Ingestion

Universal adapter architecture for ingesting:

• ERP exports (CSV / XML)
• Logistics manifests
• Vendor master records
• Bank statements
• Procurement APIs
• Webhooks from partner systems

Cross-Tier Entity Linking

• Vendor → Sub-vendor → Shipment → Invoice → Payment graph mapping
• Builds a unified risk profile across tiers

Heuristic & Rule-Based Fraud Detection

Strong Match Detection

• Exact invoice ID duplication
• Duplicate payment reference
• Reused shipment tracking numbers
• Repeated vendor bank accounts

Fuzzy Anomaly Detection

• Amount mismatch across invoice vs payment
• Suspicious date-window clustering
• Vendor behavior deviation
• Abnormal pricing variance
• Tier-level risk propagation

Risk Scoring Engine

Each entity (vendor, invoice, shipment, payment) receives:

• Dynamic fraud score
• Tier-level risk aggregation
• Escalation flag

Exception Management Queue

• Flagged transactions moved to review queue
• Manual override support
• Audit trail maintained

Idempotent Ledger & Audit Trail

• Immutable transaction logs
• Double-entry validation for financial flows
• Full traceability across supply tiers

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Architecture

Designed as a monolithic modular backend optimized for high-throughput fraud analytics.

• Runtime: Node.js (TypeScript)
• Framework: Fastify
• Database: PostgreSQL
• ORM/Query Builder: Knex.js
• Validation: Zod

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System Modules

1. Ingest Service
   Normalizes heterogeneous supply chain data into a unified schema.

2. Graph Builder
   Constructs cross-tier relationships (vendor ↔ invoice ↔ shipment ↔ payment).

3. Fraud Detection Engine

   • Rule engine
   • Heuristic matcher
   • Risk scoring model

4. Exception & Review Service
   Manages flagged anomalies and review lifecycle.

5. Audit & Reporting Layer
   Generates risk dashboards and compliance reports.

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Data Flow

graph LR
    A[ERP System] -->|Invoices| B(Ingest Service)
    C[Logistics Partner] -->|Shipment Data| B
    D[Bank / Payment Gateway] -->|Payment Records| B
    E[Vendor Registry] -->|Vendor Data| B
    
    B --> F{Fraud Detection Engine}
    F -->|Low Risk| G[Verified Transactions]
    F -->|High Risk| H[Exception Queue]
    
    G --> I[Audit Ledger]
    H --> J[Manual Review Dashboard]

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API Quick Reference

1. Register Vendor

POST /vendors

{
  "vendor_id": "V-1023",
  "tier": 2,
  "bank_account": "XXXX1234",
  "country": "IN"
}

2. Record Invoice

POST /invoices

{
  "invoice_id": "INV-9001",
  "vendor_id": "V-1023",
  "amount": 120000,
  "currency": "INR",
  "shipment_id": "SHP-5501"
}

3. Import Shipment Data

POST /shipments/import

Upload logistics manifest files or partner exports.

4. Run Fraud Scan

POST /fraud/run

Returns:

{
  "total_entities_scanned": 5400,
  "high_risk": 43,
  "medium_risk": 112,
  "low_risk": 5245
}

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Hackathon Value Proposition

• Detects fraud across multiple supply chain tiers
• Graph-based linking for anomaly detection
• High performance and scalable backend
• Production-ready architecture
• Built for real-world datasets

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Getting Started

# 1. Install dependencies
npm install

# 2. Run database migrations
npm run migrate:latest

# 3. Start the engine
npm run dev

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Future Scope

• Machine learning anomaly detection
• Real-time streaming pipeline (Kafka integration)
• Blockchain-based supplier verification
• AI-assisted investigation summaries
• Risk heatmap visualization

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Payline — Bringing transparency and intelligence to complex supply chains.