AI in Bank Treasury and Risk

Workshop overview:

The availability of Artificial Intelligence (AI) through open AI has made the technology readily accessible to banks, and the possibilities for the application of AI in banking operations are almost limitless.

This workshop considers those areas of AI adoption that Finance, Risk and Treasury departments can potentially benefit from, steps that banks can take to maximise efficiency and effectiveness in the use of AI, and areas where AI can enhance balance sheet risk management practice in banks.

Learning outcomes:

be familiar with the range of AI applications and uses in bank risk management and treasury processes
understand the parameters to set when developing the model and testing output
be aware of the full checklist to run when integrating AI model output into finance, risk, treasury and ALCO processes
have an appreciation of how Agentic AI can be adopted
understand what issues to consider when finalising policy on Agentic AI use for anti-money laundering and balance sheet risk management processes.

Area of expertise:

Unsecured Consumer Credit Commercial finance Economic crime Mortgages Payments, innovation & resilience Personal banking Prudential risk, reporting and tax

Intro to Artificial Intelligence as a modelling concept and application in banks and corporate treasury

origins of AI and its application to banking and finance
distinguishing between AI, Machine Learning, and Generative AI
AI and treasury.

Setting up your AI, data integration, and integrating AI initiatives into treasury and ALCO processes

Gen AI and Large Language Models: uses and applications

Examples:
Machine Learning (ML) and Large Language Models (LLMs) in cash management, (in/outflows, intraday, zero balance, payments cutoffs, risk management (exposure identification. Payment breach, cash ladder), treasury policy automation
Cash Flow forecasting: using predictive analytics to improve accuracy.

Automation: using LLMs to automate treasury policy and reporting

LLMs and ALCO dashboard production

Small Language Models:

cheaper to train, fine-tune, and run.
SLMs trained on your bank’s past ALCO decks
financial report drafting, scenario stress testing, and using Retrieval-Augmented Generation (RAG) to ensure accuracy.

Writing strong prompts: examples and refining issues

data availability, system integration, black box, explainable AI Agentic AI and AI in decision making
transition to Agentic AI
moving beyond passive AI to systems that can autonomously initiate actions
executing interest-rate risk exposure hedging.

AI governance

appropriate governance for AI in terms of model development oversight.

Application example: demonstration of relevant applications

generation of ALCO and CAMELS reports that present the bank’s strategies with clarity, and their strategic goals
collaboration between Buy and Sell agents, Risk Agents, and the CFO agent to generate NII before and after simulations.
“real-time” collaboration: Agentic AI enabling ALCO to evaluate ‘What-If’ simulations.
demonstration of product pricing and profitability while meeting long-term risk-based performance
selection of optimal risk-appetite adjusted strategies (with and/or without benchmarking against peers).

ALCO members
Board Non-executive directors
CFO
Finance function
Head of Treasury
Head of Balance Sheet Management
Head of Money Markets
Head of ALM
Head of Liquidity Risk
CRO and staff
CFO and staff
Head of Regulatory Reporting
Head of Internal Audit

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AI in Bank Treasury and Risk

Workshop overview:

Learning outcomes:

AI in Bank Treasury and Risk

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