RAI – Risk and AI Certificate

Build Expertise in Artificial Intelligence, Machine Learning, AI Risk Management and Responsible AI Governance

RAI – Risk and AI Certificate is a professional certificate programme conducted by the Global Association of Risk Professionals (GARP), USA.

The programme focuses on the fundamental concepts, tools, techniques and risk management frameworks associated with artificial intelligence, machine learning and generative AI.

RAI combines AI concepts, machine learning techniques, model risk assessment, responsible AI principles and governance frameworks, developing essential analytical and professional skills for careers in financial services, insurance, technology, consulting and enterprise risk management.

Actuators Educational Institute (AEI) offers structured preparation aligned with the GARP RAI curriculum, helping students develop an understanding of AI technologies and the risks associated with their practical applications.

Course Structure & Syllabus

The RAI curriculum comprises five major subject areas covering artificial intelligence concepts, AI applications, risk management and governance principles.

Key Topics Covered:

1. History and Overview of AI Concepts:
   Evolution of artificial intelligence, machine learning fundamentals, supervised and unsupervised learning, deep learning, generative AI, large language models and applications of AI in business.
2. AI Tools and Techniques:
   Machine learning algorithms, regression, classification, neural networks, Natural Language Processing (NLP), predictive modelling, model training, testing, validation and AI-based decision-making.
3. Risks and Risk Factors:
   AI model risk, data quality, algorithmic bias, model uncertainty, cybersecurity, privacy risks, operational vulnerabilities, lack of transparency, model explainability and third-party AI risks.
4. Responsible and Ethical AI:
   Ethical AI principles, fairness, accountability, transparency, responsible data usage, human oversight, discrimination prevention, explainable AI and emerging regulatory considerations.
5. Data and AI Model Governance:
   Data governance frameworks, AI model lifecycle management, model documentation, validation, monitoring, internal controls, organisational responsibilities, regulatory compliance and governance practices.
6. AI Applications and Business Risk Assessment:
   Applications of artificial intelligence in banking, insurance, financial services, fraud detection, credit assessment, risk modelling and organisational decision-making.
7. AI Risk Mitigation and Governance Applications:
   AI risk identification, model oversight, governance implementation, ongoing performance monitoring, accountability frameworks and practical approaches to managing AI-related risks.

The RAI syllabus focuses on artificial intelligence fundamentals, machine learning, AI risk identification, ethical considerations and AI model governance, developing the knowledge required to evaluate and manage AI-related risks across organisations.

Syllabus Topics & Weightages

The RAI curriculum comprises five principal examination areas:

The curriculum is reviewed annually by GARP. Candidates should refer to the official RAI Study Guide and Learning Objectives for the number of examination questions allocated to each subject area and the curriculum applicable to their examination year.

Note: GARP also provides supplementary case studies and practitioner perspectives. These are optional learning resources and are not directly examined.

Examination Structure

RAI is a computer-based multiple-choice examination assessing AI concepts, machine learning techniques, associated risks and responsible AI governance.

Important Examination Guidelines

• Multiple-Choice Examination: Candidates must attempt 80 equally weighted questions within four hours.
• Conceptual & Practical Applications: Questions evaluate artificial intelligence concepts, machine learning techniques, AI risks, ethical considerations and governance frameworks.
• Scenario-Based Questions: Some questions may involve practical business scenarios requiring candidates to identify risks and evaluate appropriate governance approaches.
• Programming Requirements: Candidates are not required to perform coding or programming during the examination.
• Examination Mode: Candidates may appear at authorised Pearson VUE test centres or through eligible online proctoring arrangements.
• Examination Booking: Candidates must register through the GARP portal and schedule their examination within the applicable testing window.
• Examination Conduct: Candidates must comply with GARP’s identification, examination security, technical and testing requirements.

Eligibility Criteria

The RAI Certificate is suitable for students, graduates and working professionals interested in artificial intelligence, risk management and responsible AI governance.

• No minimum educational qualification or professional experience is required to register for the RAI examination.
• Students from Commerce, Finance, Economics, Mathematics, Statistics, Actuarial Science, Engineering and Computer Science backgrounds may pursue the programme.
• Working professionals in banking, insurance, investments, technology, auditing, consulting and financial services may also enrol.
• Prior knowledge of artificial intelligence, machine learning or programming is not mandatory.
• A basic understanding of business, analytical reasoning and risk management concepts is beneficial.
• Candidates must satisfy GARP’s applicable registration, identification and examination requirements.

Important: Candidates who successfully pass the RAI examination receive the GARP Risk and AI Certificate. Unlike the FRM certification, RAI does not require two years of professional work experience.

Learning Objectives

Studying RAI develops the ability to:

• Understand fundamental artificial intelligence and machine learning principles.
• Identify different types of AI technologies and their applications.
• Understand machine learning algorithms, neural networks and generative AI.
• Evaluate AI-based business applications and decision-making systems.
• Identify and assess AI model risks, data risks and operational vulnerabilities.
• Understand algorithmic bias, fairness, transparency and explainability.
• Apply responsible and ethical AI principles.
• Evaluate data governance and AI model management frameworks.
• Understand AI model validation, monitoring and risk mitigation techniques.
• Develop professional judgement in AI risk management and governance.

Practical Applications of RAI

The knowledge gained through RAI is applicable across several financial, technological and risk management disciplines:

1. Banking & Financial Institutions:
   Evaluating artificial intelligence applications in credit risk assessment, fraud detection, customer analytics, financial forecasting and automated decision-making.
2. Insurance & Actuarial Risk Management:
   Understanding AI applications in insurance pricing, underwriting, claims management, predictive modelling, reserving and actuarial risk assessment.
3. Enterprise Risk Management:
   Identifying AI-related operational, technological, model, cybersecurity and reputational risks and developing appropriate mitigation frameworks.
4. Data Science & AI Governance:
   Evaluating machine learning models, model validation, explainability, algorithmic fairness, responsible AI practices and governance requirements.
5. Risk Consulting & Regulatory Compliance:
   Supporting AI risk management frameworks, reviewing governance policies, assessing AI-related risks and advising organisations on responsible AI implementation.

RAI develops an understanding of artificial intelligence technologies and their associated risks, supporting professional applications in AI governance, model risk management, enterprise risk management and responsible AI adoption.

Technical Requirements – AEI Learning Application

To access online classes, recorded lectures and digital study resources, students must ensure their devices meet the following minimum technical requirements of the Actuators Educational Institute (AEI) Learning Application.

Students are advised to maintain a stable internet connection and verify device compatibility before enrolment. Access may be restricted on organisational devices or networks due to firewall, proxy, VPN or administrative security policies.

Note: These requirements apply to the AEI Learning Application. The official GARP RAI examination has separate technical requirements prescribed by GARP and Pearson VUE.