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A governed framework for AI in Asset-Liability Management

Integrating large language models (LLMs) as an analytical co-pilot for financial risk

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When the ALM engine finishes a stress run, the hard work is no longer calculation; it is interpretation, challenge, and ALCO-ready narrative. This technical Discussion Paper sets out a governed framework for using Artificial Intelligence (AI) and Large Language Models (LLMs) as analytical copilots in that layer, without replacing the deterministic contract engine.

 

Dr. Ioannis Akkizidis (Regnology) and Michail Michoulas (Agora Financial) present a practical approach to embedding AI into the Asset-Liability Management (ALM) workflow, from architecture and inputs through a full geopolitical stress-testing case study.  The framework assumes AI sits on top of consistent ALM analytics - results for income, value, liquidity, and credit derived from a single analytical cash-flow engine.

What you’ll learn: 

  • How to use LLMs to augment modelling, scenario generation, and reporting without replacing deterministic contract and valuation engines.
  • How to build a three-pillar architecture combining quantitative ALM data, a retrieval-augmented knowledge library (RAG), and human-led expert prompts for grounded insights.
  • How an LLM interprets multi-factor geopolitical stress tests across sensitivity gaps, liquidity profiles, and credit-risk metrics, and where its ‘judgment’ remains bounded by expert framing.
  • How to deploy AI as a second analytical layer to interpret results, surface blind spots, test scenario assumptions when prompted to challenge them, and support ALCO decision-making.
  • The governance controls required to manage prompt framing, output variability, and human-in-the-loop oversight.

Discussion Paper

A governed framework for AI in Asset-Liability Management

Download the Discussion Paper for the architecture, governance model, and complete geopolitical case study. ↓

Download the discussion paper

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