Risk And Ai (Rai): Garp Prep Course

Master the GARP Risk and AI Certification: Understand AI risks, governance, and applications in finance

What you'll learn

Understand the foundational concepts of Artificial Intelligence and Machine Learning

Analyze and evaluate the risks associated with AI models

Apply governance and risk management frameworks

Prepare effectively for the GARP Risk and AI Certification exam

Requirements

No prior experience with AI or risk management is required

A basic understanding of finance or risk concepts

Familiarity with business or technology terminology used in financial services will enhance the learning experience but is not a strict prerequisite.

Description

Are you ready to future-proof your career at the intersection of finance, risk management, and artificial intelligence?This course is your ultimate companion to prepare for the GARP Risk and AI (RAI) Certification—the world’s first global certification designed to equip professionals with a deep understanding of AI risks, governance, and regulatory expectations in the financial services industry.Whether you're a risk manager trying to keep pace with emerging technologies, a data scientist navigating model governance, a compliance officer concerned with responsible AI use, or student freshly embarked on an AI journey, this course is built for you.Through concise lessons, real-world case studies, practice questions, and exam-oriented guidance, you’ll gain:A strong grasp of AI/ML fundamentals tailored for financePractical insights into identifying, measuring, and mitigating AI-related risksFrameworks for ethical AI, model validation, and regulatory complianceA strategic study plan aligned with GARP’s official RAI syllabusNo prior technical or AI background? No problem. This course breaks down complex concepts into clear, actionable knowledge.Join now and take a confident step toward becoming a future-ready risk professional with GARP’s Risk and AI Certification. This course is taught by professionals working in the AI domain and have thousands of students across more than 100 countries!

Overview

Section 1: Welcome and Overview

Lecture 1 Course Overview

Lecture 2 Practice Test

Lecture 3 Join the Community for Live Classes and Q&A Sessions

Section 2: Module I

Lecture 4 Classical AI

Lecture 5 Specific Vs General AI

Lecture 6 Good Old Fashioned AI (GOFAI)

Lecture 7 Simple Reinforcement Learning

Lecture 8 Lookahead

Lecture 9 Search in AI

Lecture 10 Recursion

Lecture 11 Recursive Adversarial Tree Search in AI

Lecture 12 Complexity, Heuristics, and Reinforcement Learning

Lecture 13 Limits of Classical AI

Lecture 14 Introducing Neural Nets

Lecture 15 Artificial Neuron

Lecture 16 Connectionism and Its Early Challenges

Lecture 17 Deep Learning

Lecture 18 DL Beats Symbolic AI at Its Own Game

Lecture 19 Inscrutability of Deep Learning

Lecture 20 Dawn of AGI

Lecture 21 ML & Risks

Lecture 22 Examples of Unsupervised Learning - PCA

Lecture 23 Risks of Inscrutability

Lecture 24 Risks of Overreliance

Lecture 25 Risks to Us

Section 3: Module 2 - Chapter 1: Intro to Tools

Lecture 26 Introduction

Lecture 27 Types of ML

Lecture 28 Exploratory Data Analysis

Lecture 29 Data Cleaning

Lecture 30 Data Visualization

Lecture 31 Feature Extraction

Lecture 32 Data Scaling

Lecture 33 Data Transformation

Lecture 34 Dimensionality Reduction Techniques

Lecture 35 Training, Validation, and Testing

Lecture 36 Software for Machine Learning

Section 4: Module 2: Chapter 2 - Unsupervised Learning

Lecture 37 Introduction

Lecture 38 K-Means Algorithm

Lecture 39 Performance Management

Lecture 40 Selecting Centroids

Lecture 41 Selection of Centroids - Example

Lecture 42 Advantages and Problems of K-Means

Lecture 43 Fuzzy K-Means

Lecture 44 Hierarchical Clustering

Lecture 45 Density Based Clustering

Section 5: Module 2 - Chapter 3: Simple Linear Regression

Lecture 46 Introduction: Simple Linear Regression

Lecture 47 Multi Linear Regression

Lecture 48 Wage Rates Example

Lecture 49 Potential Problems in Regression

Lecture 50 Stepwise Regression Procedure

Lecture 51 Classification Problem

Lecture 52 Other Types of Limited Dependent Variable Models

Lecture 53 Linear Discriminant Analysis

Section 6: Module 2 - Chapter 4: Supervised Learning - Part II

Lecture 54 Introduction

Lecture 55 Regression Trees

Lecture 56 Classification Trees

Lecture 57 Pruning

Lecture 58 Ensemble Methods

Lecture 59 K-Nearest Neighbors

Lecture 60 Support Vector Machines

Lecture 61 SVM Example and Extensions

Lecture 62 Neural Networks

Lecture 63 Choice of Activation Function

Lecture 64 Numerical Example

Lecture 65 Backpropagation

Lecture 66 Architectural Issues

Lecture 67 Overfitting

Lecture 68 Advanced Neural Network Structures

Lecture 69 Autoencoders

Section 7: Module 2 - Chapter 5: Semi-Supervised Learning

Lecture 70 Introdution

Lecture 71 Techniques

Lecture 72 Self-Training

Lecture 73 Co-Training

Lecture 74 Unsupervised Preprocessing

Section 8: Module 2: Chapter 6 - Reinforcement Learning

Lecture 75 Intro to RL

Lecture 76 Multi-Arm Bandit

Lecture 77 Strategies in RL

Lecture 78 Markov Decision Process

Lecture 79 Approaches to RL

Lecture 80 The Bellman Equations

Section 9: Module 2: Chapter 7 - Supervised Learning - Model Estimation

Lecture 81 Ordinary Least Squares

Lecture 82 Non Linear Squares

Lecture 83 Hill Climbing

Lecture 84 The Gradient Descent Method

Lecture 85 Backpropagation

Lecture 86 Computational Issues

Lecture 87 Maximum Likelihood

Lecture 88 Overfitting

Lecture 89 Underfitting

Lecture 90 Bias-variance Trade Off

Lecture 91 Prediction Accuracy Versus Interpretability

Lecture 92 Regularization - Ridge Regression

Lecture 93 LASSO

Lecture 94 Elastic Net

Lecture 95 Regularization Example

Lecture 96 Cross Validation

Lecture 97 Stratified Cross-validation

Lecture 98 Bootstrapping

Lecture 99 Grid Searches

Section 10: Module 2: Chapter 8 - Supervised Learning - Model Performance Evaluation

Lecture 100 Introduction - Model Evaluation

Lecture 101 Model Performance Evaluation - Continuous Variable

Lecture 102 Classification Model Prediction

Lecture 103 Model Performance Evaluation - Classification

Section 11: Module 2: Chapter 9 - NLP

Lecture 104 Introduction

Lecture 105 Data Preprocessing

Lecture 106 NLP Models

Lecture 107 Vector Normalization

Lecture 108 Dictionary Comparison Approaches

Lecture 109 N Grams

Lecture 110 TF-IDF

Lecture 111 ML Approaches

Lecture 112 Naive Bayes

Lecture 113 Word Meaning

Lecture 114 NLP Evaluation

Section 12: Module 2: Chapter 10 - Generative AI

Lecture 115 Intro - GenAI

Lecture 116 Intro - Word Embeddings, Word2Vec, RNNs

Lecture 117 Word2Vec

Lecture 118 RNNs

Lecture 119 Transformers and LLMs

Lecture 120 LLMs

Lecture 121 Early LLMs

Lecture 122 Cloud-Based LLMs

Lecture 123 Evolution of GenAI

Section 13: Module 3 - Risk and Risk Factors

Lecture 124 Introduction

Lecture 125 Bias and Fairness

Lecture 126 Group Fairness

Lecture 127 Individual Fairness

Lecture 128 Demographic Parity

Lecture 129 Confusion Matrix

Lecture 130 Predictive Rate Parity

Lecture 131 Impossibility and Trade Offs

Lecture 132 Equal Opportunities

Lecture 133 Sources of Unfairness

Lecture 134 Data Collection and Composition

Lecture 135 Model Development

Lecture 136 Model Development

Lecture 137 Explainability, Interpretability, and Transparency

Lecture 138 Black Box Problem

Lecture 139 Opaqueness

Lecture 140 Explainable AI (XAI)

Lecture 141 Autonomy and Manipulation

Lecture 142 Safety and Well-Being

Lecture 143 Reputational Risks

Lecture 144 Existential Risks

Lecture 145 Global Challenges and Risks

Lecture 146 Misinformation Campaigns

Section 14: Module 4

Lecture 147 Introduction - Responsible and Ethical AI

Lecture 148 Practical Ethics

Lecture 149 Ethical Frameworks

Lecture 150 Deontology

Lecture 151 Virtue Ethics

Lecture 152 What can AI Ethics learn from Medical Ethics

Lecture 153 Principles of AI Ethics

Lecture 154 Bias and Discrimination

Lecture 155 Fairness in AI Systems

Lecture 156 Privacy and Cybersecurity

Lecture 157 Governance Challenges

Lecture 158 GC 2: Lack of AI Ethics Structures, Lack of Regulations

Lecture 159 GC 3: Unpredictability Issues, Lack of Truth Tracking Abilities, & Privacy

Section 15: Module 5: Data and AI Model Governance

Lecture 160 Intro - Data and AI Model Governance

Lecture 161 Data Governance

Lecture 162 Data Provenance

Lecture 163 Data Classification and Metadata Management

Lecture 164 Data Protection, Security, & Compliance

Lecture 165 Board Roles and Responsibilities

Lecture 166 Model Governance

Lecture 167 Model Development and Testing

Lecture 168 Testing Responsibilities

Lecture 169 Use Test in QRM

Lecture 170 Model Validation in QRMs

Lecture 171 Model Governance Policies

Lecture 172 Model Inventory and Landscape

Lecture 173 Model Validation Overview

Lecture 174 Model Design

Lecture 175 Numerical and Statistical Issues - Discretization

Lecture 176 Approximation

Lecture 177 Numerical Evaluation in QRMs

Lecture 178 Random Numbers

Lecture 179 Implementation, Software, and Data

Lecture 180 Processes and Misinterpretation I

Lecture 181 Processes and Misinterpretation II

Finance and risk professionals seeking to understand how AI is transforming risk management and aiming to earn the GARP Risk and AI (RAI) certification.,Compliance officers, auditors, and regulators who need a structured understanding of the risks and governance challenges posed by AI-driven systems in financial institutions.,Students, career switchers, and early-career professionals interested in entering the intersection of finance, risk, and emerging technologies—no prior AI or deep finance knowledge required.