Natural Language Processing

A type of neural network trained on vast corpora of text data, capable of generating human-like text, answering questions, summarising documents, and performing reasoning tasks. Large language models such as GPT and Claude represent significant R&D investment and are reshaping knowledge work, customer service, and content production across industries.

The end-to-end handling of electronic payment transactions from initiation through authorisation, clearing, and settlement. Payment processing involves multiple parties — merchants, payment gateways, acquiring banks, card networks, issuing banks, and payment processors — coordinating in real time to validate, authorise, and settle funds.

A field of artificial intelligence that enables machines to interpret and extract information from visual inputs such as images, video, and documents. Computer vision is applied in quality inspection, medical imaging, autonomous vehicles, and document processing.

An output generated by an artificial intelligence system — particularly large language models — that is factually incorrect, fabricated, or nonsensical, yet presented with apparent confidence. AI hallucinations pose significant risks in applications such as legal research, medical advice, and financial analysis, and their mitigation through grounding, retrieval-augmented generation, and human oversight is a key challenge in enterprise AI deployment.

The branch of applied ethics concerned with the moral implications of designing, deploying, and using artificial intelligence systems. AI ethics addresses issues including fairness, transparency, privacy, accountability, and the societal impact of automation.

The International Accounting Standard governing the recognition, measurement, and disclosure of intangible assets. IAS 38 requires that an intangible asset be identifiable, controlled by the entity, and expected to generate future economic benefits.

A framework for developing and deploying artificial intelligence systems that are fair, transparent, accountable, and aligned with human values and societal well-being. Responsible AI encompasses technical practices such as bias testing and model interpretability, alongside governance processes including ethical review boards, impact assessments, and stakeholder engagement.

A valuation methodology that estimates the value of an asset based on the cost to reproduce or replace it, adjusted for obsolescence. The cost approach is frequently used to value internally developed intangible assets such as proprietary software and databases where market comparables are unavailable.