Large language models in finance
Large language models can search policies, summarize documents and draft answers. In a bank or asset manager, the useful test is whether a user can trace an answer back to approved information and whether confidential data stays inside the intended boundary.
Where text models fit
A language model works with text. It can help staff find a policy passage, classify a customer request or draft an internal summary. It does not calculate capital or approve a loan by itself. The European Banking Authority describes growing general-purpose AI use in EU banks and separates generative AI from other methods.
Ground the answer in bank documents
For a policy assistant, the first design question is which documents it may retrieve. A useful answer names the source passage and version. Teams should test outdated documents, conflicting instructions and questions that have no answer in the corpus. Generative AI in finance covers the wider category; AI agents addresses workflows that take actions.
Data access and review
Bank data can include customer records, trade information and internal controls. An LLM project needs access rules, logs, evaluation examples and a human review step when output could affect a customer or a regulated process. The EBA analysis flags data quality and explainability as issues for general-purpose AI. The EU AI Act page explains risk classifications for specific uses.
Is an LLM the same as a trading model?
No. An LLM generates or interprets language. A trading model may forecast prices or select orders using numerical market data. The systems can be combined, but controls must follow the decision they influence. AI in trading examines the order side.
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Large language models and Finance Loop
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