Explainable AI

The framework of policies, procedures, and organisational structures that guide the responsible development, deployment, and monitoring of artificial intelligence systems. AI governance encompasses risk management, ethical guidelines, regulatory compliance, model validation, and accountability mechanisms.

The intangible premium that a business commands above the fair value of its net tangible assets, reflecting factors such as brand strength, regulatory licences, customer loyalty, and market position. Franchise value is a critical concept in financial services and regulated industries where the right to operate carries significant economic worth.

A category of artificial intelligence systems capable of creating new content — including text, images, code, music, and video — based on patterns learned from training data. Generative AI is transforming content production, product design, and software development, raising novel questions about intellectual property ownership and the valuation of AI-generated outputs.

The computational expense of running a trained AI model to generate predictions or outputs in production. Inference costs directly impact the unit economics of AI-powered products and services, and are a key consideration in pricing, margin analysis, and the financial viability of AI deployments at scale.

The minimum amount of capital that financial institutions must hold as required by regulators, serving as a buffer against potential losses. Regulatory capital requirements influence how intangible assets — particularly goodwill — are treated on bank balance sheets and affect the valuation of financial services businesses.

The ability of different information technology systems, software applications, and data formats to communicate, exchange data, and use the information that has been exchanged effectively. Interoperability is a critical design requirement in open banking, healthcare IT, and enterprise software, and is increasingly mandated by regulation.

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.

The value derived from a company's capacity to develop new products, services, processes, and business models. Innovation capital encompasses R&D capabilities, creative talent, experimentation culture, and the pipeline of ideas at various stages of development.