There are many variables to be considered, and a lot more data sources to integrated with in order to:

  • Analyse User Behavior Analysis
  • Develop Natural Language Processing (NLP) models
  • Real-time Transaction Monitoring
  • Anomaly Detection
  • Machine Learning Model Adaptation/Evolution
  • User Education and Feedback Integration


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.

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