Overview

Fraudulent transactions in banking can occur within seconds, leading to substantial financial losses. Traditional fraud detection systems often struggle to keep pace with emerging fraud tactics, requiring a shift toward real-time fraud detection powered by AI and cloud-native technologies.

This blog explores how machine learning models, MongoDB Atlas, and AWS services work together to identify and mitigate fraudulent transactions in real time, enhancing banking security.

Business Challenge: The Need for Real-Time Fraud Detection

Key Fraud Challenges in Banking:

• High-Speed Fraud: Fraudsters exploit digital banking infrastructure, executing unauthorized transactions within seconds.

• Sophisticated Attack Methods: Advanced fraud techniques, including synthetic identity fraud and account takeovers, require AI-driven detection.

• False Positives vs. False Negatives: A balance between detecting fraud and minimizing legitimate transaction blocks is crucial.

• Regulatory Compliance: Banks must adhere to evolving security standards and fraud prevention regulations.

Given these challenges, banks must deploy AI-powered fraud detection systems that analyze transactions in real time while ensuring high availability and scalability.

The Solution: AI-Powered Real-Time Fraud Detection Architecture

Architecture Diagram - Anomaly and Fraud Detection

1. Transaction Data Ingestion

When a user initiates a transaction via mobile banking, online banking, or ATMs, the data is captured via APIs and processed through Ditto Sync and Ditto Big Peer, ensuring secure, low-latency data transfer.

2. Data Storage and Processing with MongoDB Atlas

The transaction data is stored in MongoDB Atlas, which provides:

• High availability & scalability to handle massive transaction volumes.

• Automated data tiering for efficient storage and retrieval.

• Real-time analytics for fraud detection models.

• Full-text search capabilities to analyze historical transaction behavior.

3. Real-Time Fraud Detection with AI Models

Once a transaction is recorded, MongoDB Atlas Triggers invoke AWS EventBridge, which triggers an AWS Lambda function to analyze the transaction for potential fraud.

Two Amazon SageMaker models work in parallel to evaluate risk:

• Anomaly Detection Model (Random Cut Forest - RCF): Identifies unusual transaction patterns that deviate from normal customer behavior.

• Fraud Classification Model (XGBoost): A supervised machine learning model trained on historical fraud data to classify transactions as legitimate or fraudulent.

The models generate:

• Anomaly Score: Measures how different a transaction is from past behaviors.

• Fraud Probability Score: Assesses the likelihood of fraud based on historical trends.

4. Decision-Making & Risk Dashboard

Once the fraud analysis is complete, another AWS Lambda function processes the results and updates the Risk Dashboard, displaying:

• Real-time fraud risk scores for transactions.

• Automated alerts for potential fraud cases.

• Graphical insights into fraud trends (e.g., fraud heatmaps, anomaly detection visualization).

5. Automated Fraud Response & Mitigation

Based on fraud scores, the system executes the following actions:

• Legitimate transactions: Processed normally.

• Medium-risk transactions: Sent for manual review by fraud analysts.

• High-risk transactions: Blocked immediately, triggering an alert to security teams and notifying customers.

Business Impact: Benefits of AI-Driven Fraud Detection

• Faster Fraud Detection: AI models detect fraudulent transactions within milliseconds, preventing unauthorized fund transfers.

• Reduced False Positives: AI-powered risk scoring minimizes unnecessary transaction declines.

• Regulatory Compliance: The system ensures adherence to banking fraud regulations and reporting requirements.

• Scalability: MongoDB Atlas and AWS services provide seamless scalability for high transaction volumes.

Conclusion

By leveraging AI, cloud-based analytics, and real-time data streaming, banks can proactively detect and mitigate fraudulent transactions, ensuring secure and seamless banking experiences for customers.

For more information on AI-driven fraud detection in banking, contact us at partners@wekan.company.