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Artificial intelligence

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Glossary

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Plain‑language explanations of AI terms used in accounting, each with an example and why it matters.

Agentic AI: AI that acts autonomously to achieve goals without constant human input.
Example: Automatically chasing overdue invoices and negotiating payment terms.
Why it matters: Enables proactive decision-making and reduces manual work.
Algorithm: A set of rules or instructions that an AI system follows to process data and make decisions.
Example: Flagging unusual transaction patterns for fraud checks.
Why it matters: Helps accountants understand how automated checks work.
Anomaly detection: AI technique for identifying unusual patterns in data.
Example: Spotting duplicate invoices or suspicious expense claims.
Why it matters: Reduces risk of fraud and errors.
Artificial intelligence (AI): The simulation of human intelligence by machines.
Example: Automating reconciliations or generating financial insights.
Why it matters: Drives efficiency and accuracy in accounting processes.
Automation: Technology that performs tasks with minimal human intervention.
Example: Auto‑posting journal entries from bank feeds.
Why it matters: Saves time and reduces manual errors.

Big data: Extremely large datasets analysed by AI to uncover patterns.
Example: Analysing millions of transactions for audit sampling.
Why it matters: Enables deeper insights for decision‑making.

Chatbot: An AI tool that interacts via text or voice.
Example: Answering client FAQs about tax deadlines.
Why it matters: Improves client service and internal support.
Cognitive computing: AI systems that mimic human thought processes.
Example: Analysing complex contracts for compliance risks.
Why it matters: Supports judgement‑based tasks.
Computer vision: AI that interprets visual information from images or documents.
Example: Extracting data from scanned invoices.
Why it matters: Speeds up document processing.

Data mining: Discovering patterns in large datasets.
Example: Identifying seasonal trends in expenses.
Why it matters: Helps accountants spot opportunities and risks.
Deep learning: Advanced machine learning using layered neural networks.
Example: Detecting subtle fraud patterns across multiple ledgers.
Why it matters: Improves accuracy in complex scenarios.

Ethics in AI: Principles ensuring fairness, transparency, and compliance.
Example: Avoiding bias in credit scoring models.
Why it matters: Protects integrity and regulatory compliance.

Generative AI: AI that creates new content based on learned patterns.
Example: Drafting client emails or summarising audit findings.
Why it matters: Saves time on routine communication.

Machine learning (ML): AI systems that learn from data patterns to make predictions.
Example: Forecasting cash flow based on historical data.
Why it matters: Enhances planning and decision‑making.

Natural language processing (NLP): AI that understands and interprets human language.
Example: Extracting payment terms from contracts.
Why it matters: Automates text‑heavy tasks.
Neural network: A machine learning model inspired by the human brain.
Example: Powering fraud detection systems.
Why it matters: Enables advanced predictive tools.

Predictive analytics: Using historical data and AI models to forecast trends.
Example: Predicting tax liabilities for the next quarter.
Why it matters: Improves financial planning.

Supervised learning: Machine learning trained on labelled data.
Example: Categorising expenses based on historical coding.
Why it matters: Improves accuracy in classification tasks.

Unsupervised learning: Machine learning that finds patterns in unlabelled data.
Example: Grouping similar transactions for anomaly checks.
Why it matters: Reveals hidden insights without manual tagging.