Real-Time Fraud Detection in Banking

🔹 Use Case: Real-Time Fraud Detection in Banking

Detect and respond to fraudulent transactions (e.g., card swipe, UPI transfer, online payment) in real time.

🧩 High-Level Architecture Components:

1. Channels / Ingestion Layer

• Mobile App / Net Banking / ATM

• API Gateway (🔗 Azure API Management)

• Azure Event Hubs / Azure Service Bus (for transaction events)

2. Microservices Layer (deployed in AKS with Istio)

• Transaction Service – Receives transaction requests

• Fraud Detection Service – Analyzes patterns in real-time using rules + ML

• Rules Engine Service – Dynamic rule evaluation (Drools / custom logic)

• Notification Service – Sends alerts/SMS/email

• Audit & Logging Service – Stores all events for traceability

• User Profile & Risk Scoring Service – Maintains user risk profiles

• ML Model Service – Serves real-time fraud prediction (via Azure ML / ONNX)

3. Streaming & Processing Layer

• Azure Event Hubs / Kafka on AKS – Event stream pipeline

• Azure Stream Analytics / Apache Flink – Real-time fraud signal processing

• Azure Functions / Durable Functions – Serverless pattern for async logic

4. Data Layer

• Azure SQL / Cosmos DB – Transaction, user, rule data

• Azure Data Lake / Blob Storage – Historical data for ML training

• Azure Synapse / Databricks – Model training and analytics

5. Security & Compliance

• Azure AD + RBAC – Authentication/authorization

• Azure Key Vault – Secrets and key management

• Azure Policy / Defender – Compliance and security posture

6. Monitoring & DevOps

• Azure Monitor, Log Analytics, App Insights

• CI/CD – Azure DevOps Pipelines with gated deployments

• Istio – Traffic control, A/B testing, circuit breaking

7. Notification & Actions

• Real-time alerting to fraud analysts

• Automated actions (block card, reverse transaction, escalate)

🛡️ Key Features

• Real-time event processing

• Scalable ML-based prediction

• Dynamic rule evaluation

• Secure, resilient, and compliant

• Auditable and explainable decisions

Great — let’s walk through one end-to-end architecture flow for Real-Time Fraud Detection on Azure Cloud, using a specific scenario:

🔍 Scenario:

A customer attempts a credit card transaction on an e-commerce website. The system must validate the transaction in real-time, detect if it is fraudulent, and take immediate action.

✅ End-to-End Architecture Flow:

Step 1: Channel Event Ingestion

• The transaction is initiated via the mobile/web app or POS device.

• It hits the Azure API Management Gateway, which routes it to the Transaction Service running on Azure Kubernetes Service (AKS).

Step 2: Event Publishing

• The Transaction Service publishes a transaction event to Azure Event Hubs.

• The event contains metadata (amount, location, device, IP, card number hash, etc.).

Step 3: Real-Time Processing

• Azure Stream Analytics or Apache Flink (on AKS) processes the stream.

• It invokes:

  • Rules Engine Microservice – checks against configurable fraud rules (e.g., velocity rules, geo-blocks, blacklists).

  • Fraud ML Model Microservice – calls a model hosted on Azure ML or ONNX Runtime for scoring.

Step 4: Fraud Evaluation

• The Fraud Detection Microservice aggregates results from both:

  • Rules Engine (boolean result)

  • ML Score (e.g., 0.95 risk probability)

• If fraud is suspected (threshold breach or rule match), it triggers a flag.

Step 5: Action Trigger

• If flagged:

  • Notification Microservice sends an alert to the user and fraud operations team (SMS/email/in-app).

  • Transaction Orchestration Service (Azure Durable Functions) invokes:

    • Block Card Microservice

    • Reverse Transaction Service

    • Audit Logging (via Audit Microservice to Azure Cosmos DB / Azure SQL)

Step 6: Storage & Analytics

• All events are logged to Azure Data Lake Gen2.

• Data is processed later via Azure Synapse / Databricks for:

  • Retraining models

  • Generating fraud heatmaps

  • Compliance reporting

Step 7: Governance & Monitoring

• All services secured via Azure AD, Key Vault, App Gateway, and WAF.

• Monitoring through Azure Monitor, App Insights, and Log Analytics.

• CI/CD via Azure DevOps Pipelines with blue/green deployment and Istio traffic shifting.

🧠 Example Flow:

Transaction → API Gateway → Transaction Service → Event Hub → Stream Analytics → Rules + ML → Fraud Detection Service → Notification + Blocking Actions