Course Outline

Introduction to AI in Financial Services

  • Overview of AI applications in banking and finance
  • Use cases in fraud detection, risk management, and financial automation
  • Ethical and regulatory considerations

Machine Learning for Fraud Detection

  • Common fraud patterns and anomalies
  • Supervised vs. unsupervised learning for fraud detection
  • Building classification models for fraud identification

Real-Time Risk Assessment with AI

  • Leveraging AI for credit risk evaluation
  • Predictive modeling for financial forecasting
  • AI-driven decision-making in risk management

Building AI-Powered Financial Monitoring Systems

  • Automating transaction monitoring and alerts
  • Using NLP for financial document analysis
  • Integrating AI agents into existing financial systems

Deploying AI Models in Financial Institutions

  • Cloud-based vs. on-premises deployment
  • Ensuring security and compliance in AI-driven finance
  • Scaling AI models for high-volume transactions

Optimizing AI Models for Accuracy and Efficiency

  • Improving model precision and recall in fraud detection
  • Handling imbalanced datasets and false positives
  • Continuous learning and model retraining

Future Trends in AI for Financial Services

  • AI-powered personalized banking experiences
  • Blockchain and AI integration for fraud prevention
  • Advancements in explainable AI for financial decision-making

Summary and Next Steps

Requirements

  • Experience with financial data analysis
  • Basic understanding of machine learning concepts
  • Familiarity with risk management and fraud detection techniques

Audience

  • Financial analysts
  • Risk management teams
  • Fraud prevention specialists
  • AI engineers
 14 Hours

Number of participants


Price per participant (excl. VAT)

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