AI Glossary for Auditors: Essential Terms You Need to Know

Table of Contents

• Foundational AI Concepts for Auditors
  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Supervised Learning
  • Unsupervised Learning
  • Model Training
  • Training Data
• Advanced AI Techniques for Auditors
  • Large Language Model (LLM)
  • Generative AI (GenAI)
  • Fine-Tuning
  • Prompt Engineering
  • Tokenization
• Audit-Relevant AI Applications
  • Document Intelligence
  • Entity Recognition
  • Semantic Search
  • Anomaly Detection
• AI Governance and Quality Issues for Auditing
  • Explainability
  • Model Drift
  • Hallucination
  • Confidence Score
• Final Thoughts

Foundational AI Concepts for Auditors

Artificial Intelligence (AI)

AI refers to the simulation of human intelligence by machines. McKinsey defines AI as a set of technologies that enable machines to perform cognitive functions traditionally associated with humans.

Auditors can use AI tools to automate repetitive tasks, analyze large datasets, and surface risks that might otherwise go unnoticed. For example, AuditPal AI can scan hundreds of invoices to identify duplicate payments or unusual vendor activity.

Machine Learning (ML)

ML is a subset of AI that enables systems to learn from data and improve performance over time without being explicitly programmed. ML models identify patterns and make predictions based on historical data.

ML can support auditors by enhancing risk assessment and anomaly detection. For example, an ML model trained on prior audit findings can flag transactions that resemble past control failures.

Supervised Learning

Supervised learning is a type of ML where the model is trained on labeled data. That is, each input has a known output. The model learns to predict outcomes based on supervised learning patterns in the training data.

This approach is commonly used in fraud detection and control testing. For example, a supervised model trained on past audit exceptions can predict which transactions are likely to be problematic.

Unsupervised Learning

Unsupervised learning involves training ML models on data without labeled outcomes. The model identifies hidden patterns, clusters, or anomalies without prior guidance.

Unsupervised learning is useful for exploratory risk assessment and journal entry testing, especially when auditors lack historical labels. For example, an unsupervised model can group vendors based on payment behavior, helping auditors spot outliers.

Model Training

Model training is the process of teaching an AI system to perform a task by exposing it to data and adjusting its internal parameters. Training determines how well the model can generalize to new inputs.

Audit firms often use proprietary training data to build models tailored to their methodology and client base. For example, a model trained on thousands of financial statements learns to identify common reporting errors.

Training Data

Training data is the dataset used to teach an AI model how to perform a task. The quality, diversity, and relevance of training data directly impact model accuracy.

For example, an AI model trained on financial statements from manufacturing firms may not perform well when auditing technology companies. AuditPal AI uses generalized training data but allows auditors to fine-tune outputs using domain-specific prompts and documents.

Advanced AI Techniques for Auditors

Large Language Model (LLM)

An LLM is a type of ML model trained on vast amounts of text data to understand and generate human-like language. LLMs are central to generative AI platforms used in audit automation, as noted by PwC.

LLMs power tools like AuditPal AI by enabling conversational interactions and contextual document analysis. For example, you can ask an LLM, “What are the termination clauses in this contract?” and receive a precise answer.

Generative AI (GenAI)

GenAI refers to models that can create new content based on input prompts. Deloitte describes GenAI as a key enabler of agentic audit workflows.

Auditors can use GenAI tools to draft narratives, summarize findings, and format workpapers. Popular GenAI tools include ChatGPT, Microsoft Copilot, Claude, and Gemini.

Fine-Tuning

Fine-tuning is the process of adapting a pre-trained AI model to a specific domain or task by training it further on specialized data. For auditors, fine-tuning can improve the relevance and accuracy of AI outputs for industry-specific procedures.

Fine-tuning is a powerful way to customize AI tools for unique audit environments. For example, a firm might fine-tune an LLM using internal audit reports to improve how the AI drafts findings and recommendations.

Prompt Engineering

Prompt engineering is the practice of crafting effective inputs to guide AI models toward producing accurate and relevant outputs. It’s essential when using tools like AuditPal AI, which rely on precise prompts to deliver audit-relevant insights.

For example, a well-structured prompt like “Summarize the key risks in this IT policy” yields a more useful response than “What does this say?”

Tokenization

Tokenization is the process of breaking text into smaller units (tokens) that an AI model can process. Tokens can be words, phrases, or characters depending on the model.

For example, when you upload a contract, AuditPal AI breaks it into tokens to analyze clauses and context. This all happens behind the scenes.

Audit-Relevant AI Applications

Document Intelligence

Document intelligence refers to the use of AI to extract, classify, and analyze information from documents. It combines NLP, computer vision, and ML to understand the structure and content of files like contracts, invoices, and policies.

Document intelligence helps auditors reduce manual review time and improve accuracy in evidence gathering. For example, it can identify all lease agreements that contain variable rent clauses and extract the relevant terms.

Entity Recognition

Entity recognition is an NLP technique that identifies and categorizes key elements in text, such as names, dates, amounts, and locations. It’s central to AI-powered document review and is widely used in tools that support audit automation.

Entity recognition can help auditors extract structured data from unstructured documents. For example, AI tools can scan a contract and tag entities like “Effective Date,” “Client Name,” and “Payment Amount.”

Semantic Search

Semantic search allows an AI model to understand the meaning behind a query rather than relying on exact keyword matches. It uses context and relationships between words to deliver more relevant results.

For example, if you ask, “What are the early termination conditions?,” semantic search can find clauses even if they don’t use those exact words. This capability is especially useful in audit tools like AuditPal AI, where auditors need to locate information across multiple documents.

Anomaly Detection

Anomaly detection is the process of identifying data points that deviate significantly from the norm. It’s often a primary application of the unsupervised learning techniques discussed earlier.

According to IBM, anomaly detection can help auditors uncover unusual transactions, control failures, or potential fraud. For example, AuditPal AI can flag a vendor payment that’s 10 times higher than the average for a given category.

AI Governance and Quality Issues for Auditing

Explainability

Explainability refers to the ability to understand and interpret how an AI model arrives at its decisions. For example, if an AI model flags a transaction as high-risk, explainability tools can show which features (e.g., amount, vendor, timing) influenced the decision.

According to the National Institute of Standards and Technology, explainability is critical for validating AI outputs and ensuring compliance with professional standards.

Model Drift

Model drift occurs when an AI model’s performance degrades over time due to changes in the underlying data. This can lead to inaccurate predictions or missed risks.

For example, a model trained on 2022 expense data may misclassify 2025 transactions if spending patterns have changed significantly. Auditors should monitor model drift and retrain models regularly to maintain reliability.

Hallucination

Hallucination refers to an AI model generating outputs that are factually incorrect or unsupported by the input data. In auditing, hallucinations can lead to misleading conclusions if not properly reviewed.

Validating AI-generated content is important to ensure accuracy and avoid reliance on hallucinated outputs. For example, an AI model might summarize a policy document and invent a clause that doesn’t exist.

Confidence Score

A confidence score indicates how certain an AI model is about its prediction or output. Higher scores suggest greater reliability, while lower scores may require human review.

Confidence scores help auditors prioritize which AI outputs to trust and which to scrutinize more closely. For example, when an AI model flags a transaction as suspicious, a confidence score of 95% suggests strong evidence, while 60% may warrant further investigation.

Final Thoughts

Understanding these AI terms empowers auditors to evaluate technology confidently and apply AI tools effectively. From document intelligence to anomaly detection, each concept contributes to audit quality and efficiency.

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