AI for Finance Glossary: Key Terms Every Finance Professional Should Know

Artificial intelligence is moving fast into finance and accounting, and with it comes a wave of new jargon. This glossary explains the AI terms finance professionals are most likely to encounter, in plain English, so you can follow the conversation and judge the tools with confidence.

Foundational concepts

Artificial intelligence (AI)

The broad field of building systems that perform tasks normally requiring human intelligence, such as recognising patterns, understanding language or making predictions. It is an umbrella term rather than a single technology.

Machine learning (ML)

A subset of AI in which systems learn patterns from data rather than being explicitly programmed with rules. Most practical AI in finance today is machine learning of some kind.

Deep learning

A branch of machine learning using multi-layered neural networks, well suited to complex data like images, speech and natural language. It underpins most modern generative AI.

Training data

The data a model learns from. Its quality, coverage and biases directly shape how the model behaves, which is why data governance matters so much.

Algorithm / model

The mathematical procedure that turns inputs into outputs. In practice, a trained model is the artefact that makes predictions or generates content.

Generative AI

Generative AI

AI that creates new content — text, images, code or data — rather than only classifying or predicting. It is the technology behind tools like chatbots and writing assistants.

Large language model (LLM)

A type of model trained on vast amounts of text to understand and generate human language. LLMs power assistants that can draft, summarise and answer questions.

Prompt

The instruction or question you give a generative AI system. Clear, well-structured prompts strongly influence the quality of the output.

Hallucination

When an AI system produces information that sounds plausible but is false or fabricated. This is a critical risk in finance, where accuracy is essential, and is why outputs must be verified.

Retrieval-augmented generation (RAG)

A technique that grounds an AI's answers in a specific, trusted set of documents rather than only its training data, reducing hallucination and improving relevance.

Fine-tuning

Further training a general model on domain-specific data so it performs better on specialised tasks, such as a finance-specific assistant.

Applied AI in finance

Anomaly detection

Using AI to flag data points that deviate from expected patterns — central to fraud detection and audit analytics. See our guide to AI for fraud and anomaly detection.

Natural language processing (NLP)

The field concerned with how computers understand and generate human language, used in tasks like extracting data from contracts or summarising reports.

Optical character recognition (OCR)

Technology that converts images of text, such as scanned invoices, into machine-readable data — often the first step in automating document processing.

AI agent

An AI system that can take a series of actions to accomplish a goal, rather than only responding to a single prompt. Agents are an emerging area with significant potential and risk.

Governance and risk

Explainability

The degree to which a model's decisions can be understood and justified. Low explainability is a serious problem when decisions must be defended to regulators or clients.

Bias

Systematic unfairness in a model's outputs, often inherited from skewed training data. Identifying and mitigating bias is a core part of responsible AI use.

Human in the loop

An approach where people review, approve or override AI outputs rather than letting the system act unchecked. It is widely regarded as essential in high-stakes finance settings.

Model drift

The gradual decline in a model's accuracy as real-world conditions change from those it was trained on, requiring monitoring and periodic retraining.

The terminology will keep evolving, but understanding these fundamentals lets finance professionals engage critically with AI rather than taking it on faith. To build practical, responsible AI capability, explore our finance and technology CPD courses, or see how the tools apply in our guides to AI tools for accountants and AI for auditors.