What challenges did Swift face before AI implementation?

Rising volume and sophistication of financial transaction fraud Need to detect anomalies in real-time across billions of global transactions Strict privacy regulations restrict access to sensitive customer data Traditional fraud detection models require access to centralized data, increasing risk of data breaches

What was the impact of the AI solution?

Reduced false positives in fraud detection by 25% Enabled real-time fraud alerts for over 10,000 financial institutions Enhanced data privacy by leveraging federated learningâno sensitive data leaves institution premises Increased detection accuracy of anomalous transactions by 30%

Source

MicrosoftLive source

Fraud detection using Azure AI and Swift

Use case typeFraud detectionUpdated Jun 13, 2026

In a strategic collaboration, Swift developed an advanced AI-driven anomaly detection system to combat transactional fraud in financial services. Utilizing Microsoft's Azure Machine Learning and Azure Confidential Computing, the project enhances fraud prevention and secures financial transactions with federated learning capabilities, ensuring sensitive data remains secure.

Organization: Swift
Industry: Finance
Location: Belgium
Published: June 2023

Reported outcomes

+30%

accuracyQuality & accuracy

−25%quantified impact

Strategic outcomes

Risk & complianceEnabled secure fraud detection without exposing sensitive dataNew product / capabilityBuilt an AI-driven anomaly detection systemCustomer experience & trustEnabled real-time fraud alerts for institutions

Catalog median for quality & accuracy deployments: +90% across 282 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 5

1Real-time Fraudulent Transaction Detection
2Federated Learning for Collaborative Fraud Prevention
3Anomaly Detection in Cross-Border Payments

Rising volume and sophistication of financial transaction fraud
Need to detect anomalies in real-time across billions of global transactions
Strict privacy regulations restrict access to sensitive customer data
Traditional fraud detection models require access to centralized data, increasing risk of data breaches

Developed an advanced anomaly detection system using Azure ML
Implemented federated learning to allow model training without exposing sensitive data
Utilized Azure Confidential Computing to ensure secure processing of financial data
Integrated real-time monitoring and fraud alerting for financial transactions

Technologies

Azure Confidential Computing Azure ML

Reduced false positives in fraud detection by 25%
Enabled real-time fraud alerts for over 10,000 financial institutions
Enhanced data privacy by leveraging federated learningâno sensitive data leaves institution premises
Increased detection accuracy of anomalous transactions by 30%

Sources & evidence1

Live sourceStill referenced

The case's original source is still reachable.