What are Large Language Models (LLMs) in Finance?

Why LLMs in Finance Matter

Finance runs on documents. A single commercial credit can involve years of tax returns, interim financial statements, bank statements, rent rolls, appraisals, and a credit memo that pulls it all together. For decades, software could store these documents but not understand them, so the reading, comparing, and writing fell to analysts.

LLMs in finance change that. They can read a 150-page financial package, identify what matters, answer questions about it, and draft structured narrative in the institution’s own format. That makes them the engine behind most modern AI in lending and compliance, from document intelligence to automated credit memo drafting to policy question answering.

An LLM on its own, however, is a general-purpose language engine, not a finance system. It can misread a table, state an outdated fact with confidence, or produce an answer that cannot be traced. The institutions getting value from LLMs in finance wrap them in structure: grounding in the institution’s own data, deterministic calculations where numbers matter, guardrails, and human approval.

How LLMs Are Applied in Financial Workflows

1. Understanding documents: interpreting unstructured documents, classifying them, and locating the figures and clauses a workflow needs.
2. Extraction with structure: returning data in defined fields that downstream systems and calculations can use.
3. Grounded answering: answering questions using retrieved passages from the institution’s own files and policies, with citations (see RAG).
4. Drafting: producing first drafts of credit memo narratives, borrower correspondence, and summaries for a human to edit and approve.
5. Reasoning inside agents: acting as the planning component of AI agents that break a goal into steps and call tools to complete them.

General-Purpose LLMs vs Domain-Adapted LLM Systems

Common Use Cases for LLMs in Finance

• Commercial lending: reading financial packages, supporting spreading, and drafting credit memo narratives for underwriter review.
• Document intelligence: classifying and extracting data from tax returns, bank statements, and other borrower documents.
• Compliance: summarising regulatory changes, mapping them to internal policy, and answering procedure questions with citations.
• Servicing and support: drafting responses to borrower and member inquiries using approved content.
• Wealth management: summarising client documents and meeting notes and preparing materials for advisor review.

Risks, Regulation, and Controls

The main risks of LLMs in finance are inaccurate or fabricated output, inconsistent answers to the same question, exposure of sensitive data, bias, and outputs that cannot be explained. Existing expectations already apply: models that inform decisions fall under model risk management, vendor-provided models fall under third-party risk management, customer data remains subject to privacy and information security requirements, and any credit denial must be explainable with its specific principal reasons under the Equal Credit Opportunity Act and Regulation B. Practical controls include grounding answers in approved sources, calculating numbers outside the model, validating outputs against source documents, logging prompts and responses, and keeping a qualified human accountable for every consequential decision.

How Uptiq Uses LLMs in Lending

Uptiq’s Qore platform applies domain-trained AI agents to commercial lending: document AI, financial spreading, credit memo generation, and covenant monitoring. Outputs are grounded in the borrower’s documents, every figure traces to its source, agents run alongside the existing loan origination system, and an underwriter approves each result. Across more than 150 financial institutions, teams using Qore have seen 41% faster underwriting and 63% less credit memo prep time, with 95%+ document extraction accuracy.

Frequently Asked Questions