AI MODEL RISK IN FINANCE TRAINING COURSE

COURSE OVERVIEW

AI Model Risk in Finance Training Course is a comprehensive professional programme designed to equip finance professionals, banking executives, risk managers, financial analysts, auditors, compliance officers, data scientists, technology leaders, and policymakers with the knowledge and practical skills required to identify, assess, manage, monitor, and govern artificial intelligence model risk in financial services. The programme examines how AI and machine learning models are increasingly applied in credit scoring, fraud detection, algorithmic trading, financial forecasting, customer analytics, anti-money laundering, portfolio management, insurance underwriting, treasury management, and automated decision-making, while introducing new sources of model risk involving data quality, model uncertainty, bias, explainability, cybersecurity, model drift, validation, and governance.

The training provides an integrated understanding of AI model risk management, machine learning risk, financial model validation, algorithmic bias, explainable AI, model governance, data governance, AI compliance, model performance monitoring, model drift, scenario analysis, stress testing, and responsible AI. Participants will learn how AI models can generate financial, operational, regulatory, legal, ethical, reputational, and strategic risks when models are poorly designed, incorrectly implemented, inadequately validated, or used outside their intended purpose. Particular attention is given to the lifecycle of AI models, from data acquisition and development through validation, deployment, monitoring, modification, retirement, and documentation.

Participants will develop practical capabilities in AI model inventory, risk classification, model validation, data-quality assessment, performance testing, bias and fairness assessment, explainability analysis, robustness testing, sensitivity analysis, back-testing, stress testing, challenger models, model documentation, governance controls, and model-risk reporting. The programme also examines generative AI and large language model risks in financial environments, including hallucination, unreliable outputs, confidential-data exposure, prompt manipulation, third-party technology dependence, cybersecurity threats, and inappropriate automation. Participants will learn how to establish effective AI model governance frameworks aligned with organizational risk appetite and regulatory expectations.

Through practical exercises, financial AI case studies, model-risk scenarios, validation workshops, AI governance simulations, risk assessment exercises, and implementation planning, participants will learn how to establish a robust AI Model Risk Management Framework for Financial Services. The programme supports organizations in improving the reliability, transparency, accountability, security, and performance of AI-driven financial decisions while reducing financial losses, regulatory exposure, operational disruption, and reputational damage. It is particularly relevant to banks, insurance companies, fintech organizations, investment firms, pension funds, regulators, development finance institutions, and corporations deploying AI in financial decision-making.

COURSE OBJECTIVES

By the end of the training, participants will be able to:

1. Explain the principles of AI model risk in financial services.
2. Identify major sources of artificial intelligence and machine learning model risk.
3. Assess AI model risks across the complete model lifecycle.
4. Apply financial AI model validation and testing techniques.
5. Evaluate data quality, model bias, fairness, and reliability.
6. Apply explainable AI and model transparency techniques.
7. Assess model performance, stability, drift, and robustness.
8. Integrate AI model risk into enterprise risk management frameworks.
9. Develop effective AI model governance, monitoring, documentation, and reporting systems.
10. Design practical strategies for responsible and risk-based deployment of AI in finance.

ORGANIZATION BENEFITS

1. Strengthens AI model risk identification and management.
2. Improves reliability of AI-supported financial decisions.
3. Reduces model-related financial, operational, and regulatory risks.
4. Enhances data governance and model validation capabilities.
5. Improves transparency and explainability of AI systems.
6. Strengthens responsible AI and ethical decision-making.
7. Enhances fraud detection, credit risk, and financial analytics governance.
8. Improves model monitoring and early identification of model deterioration.
9. Strengthens regulatory compliance and audit readiness.
10. Supports secure, accountable, and sustainable AI adoption.

TARGET PARTICIPANTS

This course is designed for Chief Risk Officers, Chief Financial Officers, Risk Managers, Model Risk Managers, Banking Professionals, Credit Risk Managers, Financial Analysts, Investment Managers, Portfolio Managers, Treasury Managers, Data Scientists, Machine Learning Professionals, AI Specialists, IT Managers, Cybersecurity Professionals, Internal Auditors, Compliance Officers, Regulatory Professionals, Fintech Executives, Insurance Professionals, Quantitative Analysts, Data Governance Professionals, and senior executives responsible for financial modelling, AI implementation, risk management, or digital transformation.

COURSE OUTLINE

MODULE 1: FOUNDATIONS OF AI MODEL RISK IN FINANCE

1. Artificial intelligence, machine learning, and financial models
2. AI applications across banking, insurance, investment, and fintech
3. Definition and classification of AI model risk
4. Sources of financial AI model risk
5. AI model lifecycle and risk-management principles
6. AI risk appetite, policies, governance, and accountability
   General Case Study: A bank introduces an AI-based credit scoring system that produces inconsistent results for different customer groups. Participants identify potential model, data, governance, and financial risks.

MODULE 2: AI DATA RISK, BIAS & MODEL DEVELOPMENT

1. Data governance and AI model development
2. Data quality, completeness, accuracy, and representativeness
3. Training, validation, and testing datasets
4. Algorithmic bias and fairness in financial decisions
5. Feature selection, data leakage, and model assumptions
6. Data privacy, confidentiality, and security risks
   General Case Study: An automated lending model performs significantly better for some customer segments than others. Participants assess data quality, potential bias, fairness, and the financial consequences of inaccurate credit decisions.

MODULE 3: AI MODEL VALIDATION, PERFORMANCE & ROBUSTNESS

1. Principles of AI model validation
2. Model performance metrics and benchmarking
3. Back-testing, sensitivity analysis, and validation testing
4. Model robustness and stability assessment
5. Model drift, concept drift, and performance deterioration
6. Challenger models and independent model validation
   General Case Study: An AI fraud-detection model initially performs well but experiences declining accuracy as customer transaction patterns change. Participants design a model monitoring and validation framework.

MODULE 4: EXPLAINABLE AI, TRANSPARENCY & RESPONSIBLE AI

1. Explainable artificial intelligence in financial services
2. Model interpretability and transparency
3. Explainability techniques for complex machine learning models
4. Fairness, accountability, and responsible AI principles
5. Human oversight and automated decision-making
6. Ethical, legal, regulatory, and reputational AI risks
   General Case Study: An insurance company uses an AI model to determine customer premiums but cannot adequately explain some pricing decisions. Participants develop an explainability and human-oversight framework.

MODULE 5: GENERATIVE AI, CYBER RISK & EMERGING AI MODEL RISKS

1. Generative AI and large language models in finance
2. AI hallucinations, unreliable outputs, and decision risk
3. Prompt manipulation and adversarial AI risks
4. Confidential data, privacy, and intellectual property risks
5. Third-party AI platforms, cloud dependencies, and vendor risk
6. Cybersecurity and operational resilience of AI systems
   General Case Study: A financial institution deploys a generative AI assistant for investment research and discovers that the system occasionally generates inaccurate financial information. Participants develop controls for verification, human review, data protection, and model-risk escalation.

MODULE 6: AI MODEL GOVERNANCE, MONITORING & STRATEGIC IMPLEMENTATION

1. AI model governance frameworks and organizational responsibilities
2. AI model inventory, classification, documentation, and approval
3. Continuous monitoring, model performance indicators, and escalation
4. Model risk reporting, audit, compliance, and regulatory oversight
5. Integrating AI model risk into enterprise risk management
6. Developing an AI Model Risk Management implementation roadmap
   General Case Study: A financial group operates hundreds of AI and machine learning models across credit, fraud, investment, and customer-service functions without a centralized governance system. Participants design an enterprise-wide AI model inventory, risk classification, validation, monitoring, and reporting framework.

GENERAL INFORMATION

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
14. Website: Visit our website at www.fdc-k.org for more information.