Learning Objectives:

  • Explain the applications of AI in treasury and ALM.

  • Understand the governance and control requirements for AI in treasury.

  • Critically review the types of treasury technology available.

8.1 The Role of AI in Treasury

The BTRM “AI in Bank Treasury” masterclass explores how AI can transform treasury and ALM functions . The course covers “Artificial Intelligence as a modelling paradigm” and its application to :

  • Cash Flow Forecasting: Using ML models to improve forecasting accuracy .

  • FX and Interest-Rate Risk Management: AI-assisted hedging strategies.

  • Liquidity and Funding Management: AI-driven liquidity optimisation.

  • ALCO Dashboard Production: Automating the generation of ALCO MI packs .

8.2 Types of AI

The BTRM course distinguishes between :

  • Machine Learning (ML): Algorithms that learn from data to make predictions.

  • Generative AI: AI that generates new content (e.g., text, reports).

  • Large Language Models (LLMs): AI models that process and generate text.

  • Agentic AI: AI systems capable of autonomously initiating actions .

8.3 AI Governance and Implementation

The BTRM course emphasises the importance of governance and control for AI in treasury :

  • Hallucination Mitigation: Techniques such as Retrieval-Augmented Generation (RAG) to reduce AI errors.

  • Explainability: Ensuring AI decisions can be explained and understood.

  • Model Oversight: Establishing governance frameworks for AI models.

  • Data Infrastructure: Ensuring data quality and access.

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