Artificial Intelligence in Banking

Introduction to Artificial Intelligence

• Understand AI principles and types across industries.
• Identify opportunities and challenges in AI adoption.
• Evaluate emerging AI technologies and their impact.
• Discuss the ethical, legal, and social implications of AI.

AI in Decision-Making

• Apply AI insights for operational and strategic decisions.
• Integrate predictive analytics to improve decision outcomes.
• Use AI for scenario modelling and data interpretation.
• Manage biases and transparency issues in algorithmic decisions.

Ethical AI and Governance

• Implement frameworks for responsible and transparent AI use.
• Identify ethical risks associated with automated decision systems.
• Design governance models ensuring fairness and accountability.
• Promote stakeholder confidence in ethical AI implementation.

AI-Powered Credit Risk & Underwriting

• Build feature pipelines from bureau, transactional, and alternative data sources.
• Train and compare PD, LGD, and EAD models; set cut-offs for approval tiers.
• Apply explainability (e.g., SHAP, counterfactuals) to justify decisions and reduce bias.
• Monitor model drift with challenger–champion testing and rigorous back-testing KPIs.

AI for AML, Fraud, and Transaction Monitoring

• Use anomaly detection, graph analytics, and sequence models on payments data.
• Cut false positives via risk-based segmentation and active learning feedback loops.
• Integrate alerts into case-management workflows with full auditability.
• Align with regulatory expectations (model risk management and EU AI Act) through robust controls.