Regulatory Compliance And Ai Governance In Finance

Regulatory Compliance And Ai Governance In Finance
Published 7/2026
Created by Uplatz Training
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 7 Lectures ( 3h 54m ) | Size: 1.4 GB​

AI regulation, risk, explainability, auditability, stress testing, and regulator-ready governance in financial services.
What you'll learn
Understand the importance of AI regulation, governance, and responsible AI in financial services.
Explain how global regulators assess AI-related risks, including high-risk and black-box models.
Apply Model Risk Management frameworks across the complete AI model lifecycle.
Establish clear ownership, accountability, model-risk tiering, validation, and documentation standards for AI systems.
Understand regulatory expectations for explainable AI and select suitable explainability approaches for financial use cases.
Design effective audit trails, traceability mechanisms, and documentation for AI-driven decisions.
Evaluate AI models through stress testing, robustness assessments, resilience testing, and ongoing monitoring.
Identify common regulatory concerns, governance failures, and red flags associated with AI deployment.
Prepare financial AI systems and governance processes for internal audits, supervisory reviews, and regulatory examinations.
Develop effective strategies for communicating AI models, risks, controls, and human oversight arrangements to regulators.
Build enterprise-wide AI governance frameworks covering policies, operating models, ethics, third-party AI, training, and organisational culture.
Create an end-to-end, regulator-ready AI governance programme for financial institutions.
Requirements
Enthusiasm and determination to make your mark on the world!
Description
A warm welcome toRegulatory Compliance and AI Governance in Financecourse byUplatz.
What Is Regulatory Compliance and AI Governance in Finance?
Regulatory compliance and AI governance in finance is the framework used to ensure that AI systems in banks, insurers, investment firms and FinTech companies operate lawfully, fairly, safely and responsibly.
It applies to AI used in areas such as
Credit scoring and lending
Fraud detection
Insurance pricing and claims
Trading and portfolio management
Financial advice
Risk management
Customer service
Regulatory Compliance vs AI GovernanceRegulatory compliance means meeting applicable laws, regulations and supervisory expectations.
It focuses on questions such as
Is the AI system being used legally?
Are customers treated fairly?
Is personal data protected?
Can important decisions be explained?
Are records available for audits and regulatory reviews?
AI governance is the organisation's internal system for controlling AI.
It defines
Who owns the AI system
Who approves its use
How risks are assessed
How models are tested
When human review is required
How performance is monitored
When a model should be updated, restricted or stopped
Compliance defines the obligations. Governance creates the structure for meeting them.
How It Works
1. Define the AI Use Case
The organisation clearly identifies what the AI system will do, what decisions it will influence and what harm could occur if it fails.
2. Classify the Risk
The system is classified according to factors such as customer impact, financial impact, data sensitivity, automation level and model complexity.
Higher-risk systems require stronger controls.
3. Assign Ownership
Responsibility is assigned to business owners, model owners, data teams, risk teams, compliance teams and senior management.
4. Govern the Data
The institution checks whether the data is accurate, lawful, representative, secure and free from unacceptable bias.
5. Develop and Document the Model
The model's purpose, data, assumptions, limitations, performance and intended use are documented.
6. Validate the Model Independently
A separate team tests accuracy, stability, bias, explainability, robustness and compliance before deployment.
7. Ensure Explainability
The institution must be able to provide meaningful reasons for important AI-supported decisions, especially when customers are affected.
8. Stress-Test the System
The model is tested under difficult conditions such as market volatility, incomplete data, unusual customer behaviour or system failure.
9. Design Human Oversight
Human reviewers must be able to understand, challenge, override or stop the AI system when necessary.
10. Approve the System
Risk, compliance, legal, technology and senior-management teams review the system before it is deployed.
11. Maintain Audit Trails
The organisation records the model version, input data, output, explanation, human review and final decision.
12. Monitor Continuously
The institution monitors accuracy, bias, complaints, model drift, data quality, operational failures and customer outcomes.
13. Manage Incidents
If the AI system produces harmful, inaccurate or unfair outcomes, the organisation investigates the problem and may restrict, retrain or stop the model.
14. Govern Third-Party AI
Financial institutions remain responsible even when AI models or services are provided by external vendors.
Simple Example: AI Credit Decisions
A bank using AI for loan applications would
Classify the system as high risk
Check the quality and fairness of customer data
Validate the model independently
Provide reasons for declined applications
Send unusual cases for human review
Record how each decision was made
Monitor approval rates, defaults and customer complaints
Stop or retrain the model if performance deteriorates
What Makes an AI System Regulator-Ready?
A regulator-ready AI system has
A clear purpose
Defined ownership
Strong data controls
Independent validation
Explainable decisions
Bias and stress testing
Human oversight
Reliable audit trails
Continuous monitoring
Incident-management procedures
In simple terms, regulatory compliance defines what financial institutions must do, while AI governance ensures that AI systems are controlled responsibly throughout their lifecycle.
Artificial intelligence is rapidly transforming financial services, but its use also creates new regulatory, operational, ethical and model-related risks.
This course by Uplatz provides a practical overview ofRegulatory Compliance and AI Governance in Finance, helping learners understand how financial institutions can develop, deploy and manage AI systems responsibly while meeting regulatory expectations.
The course begins with the foundations of AI regulation in financial services and explains why governance has become a board-level responsibility. You will learn how regulators assess AI systems, why black-box models create compliance challenges and how high-risk financial AI should be controlled. You will then explore model risk management for AI, including model ownership, risk classification, documentation, independent validation and lifecycle controls. The course also covers explainable AI, showing why financial institutions must be able to provide clear and defensible reasons for AI-supported decisions.
Additional topics include audit trails, traceability, reproducibility, stress testing and model robustness. You will understand how firms prepare AI systems for internal audits, supervisory reviews and regulatory examinations. The course also explains how financial institutions should approach AI deployment, communicate with regulators, establish meaningful human oversight and identify common regulatory red flags. You will learn how enterprise AI governance frameworks bring together policies, accountability, ethics, third-party risk management, training and ongoing monitoring across the complete AI lifecycle.
The final capstone module brings these concepts together through a regulator-ready AI governance blueprint and a practical case study involving AI-based credit decisioning. By the end of the course, learners will understand how to build AI governance systems that support regulatory compliance, responsible innovation, accountability and trust in financial services.
This course is suitable for compliance professionals, risk managers, model-risk teams, auditors, legal professionals, AI and data teams, financial-services leaders, consultants and anyone involved in the governance or deployment of AI in finance.
Regulatory Compliance and AI Governance in Finance - Course Curriculum
Module 1: Foundations of AI Regulation in Financial Services
This module introduces the regulatory foundations of AI in finance and explains why effective governance is essential for financial institutions.
1.1 Why AI Governance Matters in Finance
Understand why financial AI requires structured governance and how weak oversight can create regulatory, operational and reputational risks.
1.2 Evolution of AI Use in Financial Institutions
Explore how AI has evolved from traditional analytical models to machine learning, generative AI and increasingly autonomous systems.
1.3 How Regulators Think About AI
Learn how regulators assess AI through principles such as accountability, fairness, transparency, resilience and customer protection.
1.4 Global Regulatory Convergence
Examine how AI regulatory expectations are developing across major financial markets and where common principles are emerging.
1.5 The Regulatory Problem with Black-Box Models
Understand why complex and difficult-to-explain models create challenges for compliance, validation and legal defensibility.
1.6 High-Risk AI in Financial Services
Identify high-risk financial AI use cases and understand why they require stronger controls, validation and human oversight.
1.7 AI Governance as a Board-Level Responsibility
Explore the responsibilities of boards and senior management in overseeing AI strategy, risk and accountability.
1.8 Why Governance Matters More Than Compliance
Understand why simply meeting regulatory requirements is not enough and how strong governance supports trust and responsible innovation.
Module 2: Model Risk Management Frameworks for AI Systems
This module explains how traditional model risk management must evolve to address the complexity and changing behaviour of AI systems.
2.1 What Is Model Risk in the Age of AI?
Understand how AI creates new forms of model risk through complexity, automation, data dependency and unpredictable behaviour.
2.2 Classical MRM Frameworks: What Still Holds
Review the traditional principles of model risk management that remain relevant for AI systems.
2.3 Where Traditional MRM Breaks Down for AI
Explore the limitations of classical frameworks when applied to machine learning, generative AI and continuously evolving models.
2.4 Extending the Model Lifecycle for AI
Learn how governance controls should be applied across development, validation, deployment, monitoring and retirement.
2.5 Model Ownership, Accountability and Independence
Understand how responsibilities should be divided among model owners, developers, validators, risk teams and senior management.
2.6 Model Risk Tiering for AI Systems
Learn how AI systems can be classified according to their complexity, impact, materiality and level of automation.
2.7 Documentation Standards for AI Models
Explore the documentation required to support validation, auditability, governance and regulatory review.
2.8 Common Regulatory Findings in AI MRM
Review common weaknesses identified in model inventories, validation processes, documentation, monitoring and accountability.
2.9 Why Strong MRM Enables Innovation
Understand how effective model risk management allows institutions to adopt AI more confidently and responsibly.
Module 3: Explainable AI for Regulatory Compliance
This module explores why explainability is critical in financial AI and how institutions can make AI decisions understandable and defensible.
3.1 Why Explainability Is a Regulatory Requirement
Understand why regulators expect institutions to explain important AI-supported decisions.
3.2 Legal and Regulatory Drivers of Explainability
Explore the legal, conduct, data-protection and supervisory requirements that influence explainability.
3.3 What Regulators Mean by "Explainable"
Learn what a meaningful explanation looks like from the perspective of regulators, customers, auditors and senior management.
3.4 Types of Explainability in Financial AI Systems
Understand global, local, technical, operational and customer-facing explanations.
3.5 Explainability in Key Financial Use Cases
Examine explainability requirements in credit decisions, fraud detection, insurance, trading and financial advice.
3.6 Explainability Techniques: Conceptual Overview
Explore common methods used to interpret model behaviour and identify the factors influencing individual decisions.
3.7 Trade-Offs Between Accuracy and Explainability
Understand how institutions balance model performance with transparency and regulatory expectations.
3.8 Embedding Explainability into Governance
Learn how explainability can be incorporated into model approval, validation, documentation and monitoring processes.
3.9 Common Regulatory Findings Related to Explainability
Review common weaknesses such as unclear explanations, poor documentation and explanations unsuitable for customers.
3.10 Explainability and Legal Defensibility
Understand how clear explanations support complaints handling, regulatory investigations and legal defence.
3.11 Explainability as an Enabler of Trust
Explore how explainability can strengthen confidence among customers, employees, regulators and senior management.
Module 4: Audit Trails and Traceability for AI Decisions
This module explains how financial institutions can create reliable records that allow AI decisions to be examined and reconstructed.
4.1 Why Audit Trails Matter More for AI Than Any Other Technology
Understand why AI systems require detailed evidence of data, models, configurations and decision processes.
4.2 What Regulators Mean by "Auditability" for AI
Learn what regulators expect institutions to demonstrate during audits, examinations and supervisory reviews.
4.3 Why Traditional IT Logs Are Not Enough: Training Phase
Explore why standard system logs may not capture the training data, model versions, parameters and assumptions behind AI decisions.
4.4 Components of an AI Audit Trail
Identify the model, data, input, output, explanation, approval and human-intervention records required for effective auditability.
4.5 Audit Trails Across the AI Lifecycle
Understand how traceability should be maintained during development, validation, deployment, monitoring and retirement.
4.6 Traceability vs Reproducibility
Learn the difference between tracking how a decision was made and recreating the same result.
4.7 Internal Audit and Supervisory Reviews
Explore how internal auditors and regulators examine AI governance, evidence and control effectiveness.
4.8 Designing Exam-Ready AI Systems
Learn how to organise documentation, records and governance evidence for regulatory examinations.
4.9 Audit Trails and Third-Party AI
Understand the challenges of maintaining auditability when models, data or infrastructure are supplied by external vendors.
4.10 Why Audit Trails Enable, Not Hinder, AI Adoption
Explore how strong traceability supports faster approvals, greater confidence and safer AI deployment.
Module 5: Stress Testing and Robustness of AI Models
This module examines how financial institutions test whether AI systems remain reliable under difficult and unexpected conditions.
5.1 Why Stress Testing Matters More for AI Than Traditional Models
Understand why AI systems may fail unpredictably when market conditions, customer behaviour or data patterns change.
5.2 How Regulators Think About Robustness and Resilience
Learn how regulators evaluate the stability, reliability and operational resilience of AI systems.
5.3 Limitations of Traditional Stress Testing for AI
Explore why conventional model stress-testing approaches may be insufficient for complex AI systems.
5.4 Types of Stress Tests for AI Models
Understand scenario testing, sensitivity testing, adversarial testing, data-shift testing and operational resilience testing.
5.5 Stress Testing Across Financial Use Cases
Examine stress-testing requirements in lending, fraud detection, insurance, trading and wealth management.
5.6 Designing a Governance-Grade Stress-Testing Framework
Learn how to establish scenarios, thresholds, responsibilities, documentation and escalation procedures.
5.7 Interpreting Stress-Test Results
Understand how institutions evaluate failures, weaknesses, uncertainty and acceptable levels of model performance.
5.8 Stress Testing and Model Lifecycle Governance
Explore how stress-testing results influence model approval, monitoring, retraining and retirement decisions.
5.9 Common Regulatory Findings Related to Stress Testing
Review common problems such as unrealistic scenarios, weak documentation and inadequate follow-up actions.
5.10 Why Stress Testing Builds Regulatory Trust
Understand how robust testing demonstrates preparedness, control and responsible model management.
Module 6: Navigating Financial Regulators' Expectations on AI Deployment
This module explains how financial institutions can prepare for regulatory engagement before and after deploying AI systems.
6.1 How Regulators Actually Approach AI Supervision
Understand how regulators assess AI through existing principles covering governance, conduct, risk and accountability.
6.2 The Regulator's Core Questions About AI
Explore the questions regulators ask about purpose, ownership, data, validation, explainability and customer impact.
6.3 What Regulators Expect to See During Reviews
Learn what policies, documentation, testing evidence and governance records institutions should maintain.
6.4 AI Deployment and Supervisory Timing
Understand when institutions should engage regulators during the development and deployment process.
6.5 Communicating AI to Regulators Effectively
Learn how to explain complex AI systems clearly, accurately and without unnecessary technical language.
6.6 Human Oversight and Regulatory Comfort
Explore why meaningful human review, escalation and override mechanisms remain important.
6.7 Common Regulatory Red Flags in AI Deployment
Identify issues such as unclear ownership, poor explainability, weak validation and uncontrolled third-party dependencies.
6.8 Proactive Regulatory Engagement Strategies
Learn how early communication and transparent governance can reduce uncertainty and build supervisory confidence.
6.9 Preparing for AI-Focused Regulatory Exams
Understand how teams can prepare evidence, documentation, demonstrations and accountable personnel for examinations.
6.10 Why Regulatory Engagement Is a Strategic Capability
Explore how effective regulatory communication can support innovation, reduce delays and strengthen institutional credibility.
Module 7: Enterprise AI Governance Frameworks for Financial Institutions
This module brings together the organisational structures, policies and controls required to govern AI consistently across an enterprise.
7.1 What AI Governance Is and What It Is Not
Clarify the purpose and scope of AI governance and distinguish it from technical model management alone.
7.2 Core Principles of Enterprise AI Governance
Explore accountability, fairness, transparency, security, resilience, proportionality and human oversight.
7.3 The AI Governance Operating Model
Understand the roles of the board, senior management, business teams, technology teams, risk, compliance and internal audit.
7.4 AI Governance Across the Lifecycle
Learn how governance controls should operate from initial use-case approval through model retirement.
7.5 AI Ethics and Responsible AI in Governance
Explore how fairness, privacy, transparency and customer protection are embedded into governance processes.
7.6 Third-Party and Vendor AI Governance
Understand how institutions assess external models, cloud providers, data suppliers and AI vendors.
7.7 AI Policies and Standards
Learn how institutions create policies covering development, validation, use, monitoring, documentation and prohibited activities.
7.8 Culture, Training and Awareness
Explore the importance of employee education, responsible behaviour and organisational awareness.
7.9 Common Failures in AI Governance
Review failures such as fragmented ownership, uncontrolled experimentation, weak monitoring and ineffective governance committees.
7.10 Why Enterprise AI Governance Enables Scale
Understand how consistent governance allows institutions to expand AI adoption without losing control.
Module 8: Capstone - Building a Regulator-Ready AI Program
This final module integrates the course concepts into a practical framework for building and assessing a regulator-ready AI governance programme.
8.1 What "Regulator-Ready" Actually Means
Understand the capabilities, evidence and governance maturity required to demonstrate effective control of AI.
8.2 The End-to-End Regulator-Ready AI Blueprint
Explore a complete framework covering use-case approval, risk classification, validation, explainability, monitoring and reporting.
8.3 End-to-End Case Walkthrough: AI Credit Decisioning
Apply the governance framework to a practical financial-services use case involving AI-supported lending decisions.
8.4 Common AI Governance Failure Patterns
Review recurring problems that can undermine regulatory readiness and responsible AI adoption.
8.5 Preparing for Future AI Regulation
Understand how institutions can design adaptable governance frameworks that respond to evolving regulation and technology.
8.6 Skills and Capabilities for Learners
Identify the governance, risk, compliance, technical and communication skills required in financial AI roles.
8.7 Final Integration - The Mental Model
Bring together regulation, model risk, explainability, auditability, stress testing and enterprise governance into one practical mental model.
Who this course is for
Compliance, regulatory affairs, and financial crime professionals working with AI-enabled financial systems.
Risk management and Model Risk Management professionals responsible for assessing, validating, and monitoring AI models.
AI governance, responsible AI, and data governance specialists in banks, insurance companies, fintech firms, and other financial institutions.
Internal auditors, control professionals, and assurance teams reviewing AI systems and regulatory readiness.
Data scientists, machine learning engineers, and AI practitioners working in regulated financial environments.
Legal professionals and policy specialists dealing with AI regulation, explainability, accountability, and financial services compliance.
Senior managers, board members, and business leaders responsible for AI oversight, governance, and enterprise risk.
Product managers and programme managers involved in developing or deploying AI-powered financial products.
Consultants and advisors supporting financial institutions with AI governance, model risk, audit, and regulatory transformation.
Finance, risk, compliance, data science, and technology students seeking careers in AI governance and regulated financial services.
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