Ten modules. One competency: judgment over machines.

A plain-language account of what a large language model actually is, written for professionals who work with numbers rather than with code. It explains tokens, training and prediction; why generating text is different from looking something up; why fabricated output follows from the way these models work rather than from a fault that can be repaired; and it sets out the four kinds of AI the rest of the course relies on: narrow, generative, multimodal and agentic. By the end you will be able to judge, from how a tool works rather than from how it is described in a sales presentation, what it can and cannot be trusted with when money and personal data are involved.

This module sets out the evidence. It describes how generative and agentic AI have changed the economics of financial crime: deepfake voice and video fraud, AI-synthesised identity documents, fraud-as-a-service marketplaces, and multi-step schemes run by AI systems that plan and act with limited human input. Each attack type is anchored in published figures and named cases, including the South African evidence. By the end you can describe the principal attack types, cite the leading sources when you make the internal case for AI-risk competency, and map where your own institution is exposed.

This is the first of the platform's hands-on modules. It explains how synthetic identities, forged documents and deepfake audio and video are produced, and then works through the detection of them: presentation and liveness attacks, the artefacts left in synthetic media, and the hardening of a know-your-customer and onboarding workflow against both attack classes. By the end you will have a repeatable detection routine that can be applied to any verification decision.

This module explains how machine learning performs transaction monitoring, where that monitoring fails, and how criminals now use automation against it. It covers supervised and unsupervised detection, graph and network analytics for structuring, the false-positive problem, and the emerging threat of agent-driven laundering, all set inside the South African FICA regime and the Risk Management and Compliance Programme obligation. By the end you will be able to read, challenge and supervise a model-driven alert, and to explain the human role that FATF expects institutions to retain.

This module explains how bias enters automated credit decisions through data, labels and proxy variables rather than through anyone's intention, and how to find it, measure it and explain an adverse decision lawfully. It covers the principal fairness criteria and why they cannot all be satisfied at once, the mechanics of proxy discrimination and disparate impact, a structured fairness-review routine, and the legal frame: the National Credit Act's affordability regime, POPIA section 71's automated-decision rights, and the EU AI Act's classification of credit scoring as high-risk. It is written for the South African lending environment, in which residential address still reflects the country's history of enforced segregation.

This module is about working responsibly with a model whose internal reasoning you cannot inspect directly. It introduces model-risk management as it developed from international supervisory guidance, the main explainability methods and their limits, and the difference between an explanation that satisfies a regulator and one that satisfies an engineer, all framed for a South African institution and the expectations of the Prudential Authority. It closes the Core certificate by setting out the governance framework the earlier modules assume. The verification routine, the monitoring stack and the fairness audit each depend on professionals who can read a model's documentation, test its claims and question its conclusions, and this module develops those skills.

This module covers how to use generative AI productively without disclosing the personal information that regulation protects. It sets out the four routes by which information escapes: prompts, outputs, memorisation and training reuse. It then covers the controls that address each route, namely the difference between consumer and enterprise tools, safe prompting and redaction discipline, and the POPIA mapping across security safeguards, the operator relationship, automated decisions and cross-border transfers. The practical result is a workflow that allows an adviser to use AI on a client report lawfully.

This module deals with security. Earlier modules dealt with models that are misused, or that fail on their own; the concern here is models that are deliberately attacked by someone who wants a particular result. It covers three attack classes: adversarial evasion, training-data poisoning, and prompt injection against AI-integrated applications. Each is connected to the financial threat of manipulated inputs submitted to fraud and underwriting models. The module returns to Module 5 to show how a manipulated application can defeat an automated credit decision, and it sets out a realistic defensive posture: what can be hardened, what cannot, and where a person must remain in the decision path.

This module covers what changes when an AI system moves from producing answers to carrying out tasks. It explains how agentic systems plan, use tools and act, and then applies that to advisory work: robo-advice, hyper-personalised recommendations, suitability and mis-selling risk, the competitive position of the human adviser, and the supervision duties that arise when work is handed to an agent. The module works throughout from one principle: a task may be delegated to an agent, but accountability for it stays with the institution and the professional. In South Africa the FAIS conduct regime provides the legal framework for that.

This module draws the course together and ends in an applied capstone. It maps the governance landscape (the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001 and 23894, and the OECD Recommendation) together with the financial-sector guidance of the FSB, BIS, IOSCO and the South African regulators. It then guides you through assembling an institutional AI-risk protocol covering scope, permitted and forbidden uses, model governance, data rules, incident response and the three lines of defence. The capstone deliverable is that protocol, built from the artefacts produced in every earlier module and assessed against the course as a whole. The work of this module is to combine the competencies of the preceding nine modules into a single governance position that an institution can defend.