The exam validates a candidate’s ability to apply foundational concepts, practical skills, and best practices in AI in Financial Services to real-world scenarios.
Learn with labs & projects at your own pace.
Timed questions with instant feedback & review.
Financial services teams are expected to deliver measurable AI outcomes—while maintaining auditability, fairness, and model risk controls.
Banks, insurers, and asset managers are scaling AI for decisioning, operations, and client experience—requiring professionals who can translate business needs into deployable, monitored systems.
Model risk management, documentation, validation, and audit trails are non-negotiable. Demonstrating competence in these areas helps teams ship AI responsibly and sustainably.
Generative AI is expanding into research, operations, and compliance workflows. Teams need clear patterns—like access-controlled retrieval and human oversight—to avoid unintended exposure and errors.
Designed for professionals applying these capabilities in real delivery environments.
For professionals shipping models, pipelines and AI-enabled products.
For people responsible for development, evaluation, deployment and monitoring.
For teams translating requirements into reliable, responsible AI solutions.
For advisors guiding architecture, adoption, risk and implementation choices.
The assessment spans the domains below. Weightings indicate relative emphasis in the exam.
Financial services landscape: banking, markets, payments, insurance, wealth & asset management. AI vs ML vs statistical models in a financial context. Financial data types: transac...
Data sources & pipelines: core banking, trading, CRM, KYC/AML, market data vendors. Architectures: data lakes, warehouses, lakehouse patterns; batch vs streaming pipelines. Data qu...
Credit decisioning, segmentation, next-best-offer, churn prediction, and collections optimization. Trade surveillance, anomaly detection, and NLP for research and news summarizatio...
Credit risk concepts: PD, LGD, EAD; scorecards vs ML trade-offs. Fraud & AML: anomaly detection, graph methods, false positive reduction, alert triage. Stress testing & scenario an...
Wealth: robo-advisory concepts, risk profiling, personalization for advisors. Research: NLP over filings and disclosures; GenAI summarization with human oversight. Insurance: under...
Governance lifecycle: inventory → validation → approval → deployment → review → retirement. Documentation standards: model dev docs, validation reports, change logs. Model risk cat...
Fairness & non-discrimination in lending, pricing, and customer treatment. Explainability: local vs global explanations; practical requirements for decisions. Human oversight: esca...
GenAI in finance: co-pilots, document automation, conversational enterprise interfaces. AI + digital assets/DeFi/CBDCs (high level): analytics, surveillance, and risk signals. Real...
A 90-minute assessment with 250 questions, a 70% passing threshold, delivered through the GIofAI Exam Portal.
The value of the certification is the combination of controlled assessment, identity verification, domain-level performance evidence and a verifiable professional credential.
A professional GIofAI designation supported by a digital certificate and official transcript. The transcript records section-level performance, giving the credential more context than a simple completion badge.
Identity-verified assessment under controlled examination conditions.
Section-wise performance information for a clearer view of your assessed capability.
A professional credential designed to be verifiable and shareable with employers and professional networks.
Use CAIFS as a core credential before moving into deeper specialisations.
Get the certification overview, domain weightings, exam format and preparation guidance in one concise guide.
 Yes. The assessment is designed to be closed book with no external assistance, supporting a consistent baseline of verified capability.
 All questions are multiple choice (A–D) with a single correct answer. Some items are scenario-based, but they are still answered via standard MCQ selection.
 Each question carries equal weight. There is no negative marking. The passing threshold is 70%.
 Candidates may retake up to two times within six months of the first attempt. A separate purchase is required for each retake, and a 7-day wait is recommended between attempts.
 Reasonable accommodations may be supported (e.g., extended time or additional breaks). Requests typically require documentation and should be submitted in advance of the scheduled assessment.
The assessment is intended for professionals applying AI/ML in financial services or learners with equivalent experience in AI fundamentals, data work, and governance concepts.
CAIFS is built to assess applied decision-making across use cases, risk, governance, and ethics—so your credential signals competence that matters in regulated environments.