Fraud detection

Approach

Harvest information from transactional systems against data lake:

1. Extract data from transactional systems
2. Apply data anonymization and cleaning
3. Apply data transformations to perform aggregations, time series analysis and additional features
4. Developed custom AI/ML models to detect fraud.
5. Continuous evaluation of model performance to be retrained frequently to avoid decay

Value Delivered

• Enhanced Security: Provides robust protection against a wide range of impersonation scams, safeguarding users’ financial assets and personal information.
• Improved User Trust: Increases confidence in online banking applications, encouraging the use of digital banking services.
• Reduced Fraud Losses: Minimizes financial losses due to fraud, protecting both the users and the financial institutions.
• Adaptive Fraud Detection: Keeps pace with evolving scam tactics, ensuring long-term resilience of online banking platforms against impersonation scams.
• User-Centric Approach: Balances security measures with user convenience, ensuring that fraud prevention does not impede the banking experience.