This lesson explores how AI and ML are moving banking from being reactive to predictive and personalized.

6.1 The AI Revolution in Banking
Data-driven technologies such as Data Science, Machine Learning (ML), and Artificial Intelligence (AI) are reshaping financial services . AI is being used across the bank to automate complex processes, enhance decision-making, and improve the customer experience . Universities now offer specific courses on AI and ML in banking to equip executives with these critical skills .

6.2 Core Applications of AI/ML

  • Personalization: AI algorithms analyze customer behavior and transaction history to offer personalized product recommendations .

  • Customer Service: AI-powered chatbots and virtual assistants handle routine inquiries 24/7, reducing the workload on call centers .

  • Risk Management: ML models are used for advanced credit scoring, fraud detection (by identifying anomalous transaction patterns), and even predicting financial distress .

  • Operational Efficiency: AI can automate complex end-to-end processes that previously required human judgment .

6.3 AI Risks and Governance
The use of AI also introduces new risks, including:

  • Algorithmic Bias: AI models may perpetuate or amplify existing biases in data .

  • Explainability: “Black box” models can be difficult to interpret for regulatory compliance .

  • Cybersecurity: AI can be used to create more sophisticated attacks like deepfakes or adversarial AI .
    Managing these risks is a new area of focus for banks, requiring new frameworks and governance structures .

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