The increasing adoption of complex, "black-box" AI and machine learning models in financial risk management has created a significant challenge for transparency and trust. While these models often achieve superior predictive accuracy, their opaque nature makes it difficult to understand why they arrive at certain decisions, a critical requirement for regulators, internal auditors, and customers. Explainable AI (XAI) is a rapidly evolving field that provides the tools and techniques to demystify these models, bridging the gap between predictive power and interpretability.

This 10-day intensive program is designed to equip you with the knowledge and practical skills to implement XAI principles across the entire risk management lifecycle. You will explore a wide range of techniques, from local explanations that shed light on individual decisions to global explanations that provide a holistic view of a model's behavior. Through hands-on exercises with real-world financial data, you will learn to select the right XAI method for a given problem, communicate complex model behavior to non-technical stakeholders, and build a framework for responsible AI governance. By the end of this course, you will be able to turn opaque AI models into transparent, trustworthy, and auditable assets.

Duration: 10 days

Target Audience:

• Risk Managers and Analysts
• Data Scientists and AI/ML Engineers
• Financial Regulators and Compliance Officers
• Internal and External Auditors
• Credit Portfolio Managers
• Students and Researchers in Finance and AI
• Leaders in Financial Technology (FinTech)
• Anyone responsible for the governance of AI models

Objectives:

• Understand the core concepts of Explainable AI (XAI)
• Identify the importance of transparency in risk management
• Learn to differentiate between different types of model explanations
• Gain hands-on experience with popular XAI tools and libraries
• Develop skills in interpreting and communicating model decisions
• Understand the regulatory landscape for AI in finance
• Learn to implement a responsible AI governance framework
• Explore the ethical implications of AI transparency
• Prepare for the challenges of managing black-box models
• Master the art of bridging the gap between data science and business

Course Modules:

Module 1: The AI Transparency Challenge

• The rise of "black-box" models in finance
• The need for explainability and interpretability
• The regulatory drivers for model transparency
• The difference between local and global explanations
• The ethical implications of a lack of transparency

Module 2: A Framework for Explainable AI (XAI)

• The role of a trustworthy AI framework
• The lifecycle of an explainable model
• The importance of a stakeholder-centric approach
• The role of a multidisciplinary team
• The business case for XAI

Module 3: Pre-Modeling Interpretability

• The importance of data understanding
• The role of exploratory data analysis
• Feature engineering for transparency
• The use of simple, interpretable models
• The trade-off between accuracy and interpretability

Module 4: Post-Modeling Local Explanations

• The LIME (Local Interpretable Model-agnostic Explanations) algorithm
• The SHAP (SHapley Additive exPlanations) framework
• The use of SHAP for individual credit decisions
• The challenges of local explanations
• Case studies in individual loan applications

Module 5: Post-Modeling Global Explanations

• The use of partial dependence plots (PDPs)
• The importance of feature importance rankings
• The use of ICE (Individual Conditional Expectation) plots
• The challenges of global explanations
• Case studies in portfolio-level risk

Module 6: Counterfactual Explanations

• The concept of a counterfactual
• Building a counterfactual for a credit decision
• The use of counterfactuals for fairness and bias
• The challenges of generating realistic counterfactuals
• The role of counterfactuals in customer communication

Module 7: Explainability for Deep Learning Models

• The challenges of explaining neural networks
• The use of integrated gradients
• The role of attention mechanisms in models
• The importance of model visualization
• A brief overview of other deep learning-specific methods

Module 8: The Regulatory Landscape

• Key regulations: GDPR, CCPA, and others
• The role of a model risk officer
• The importance of model documentation
• The need for a robust audit trail
• The future of AI regulation

Module 9: Building a Responsible AI Governance Framework

• The role of an AI ethics committee
• The importance of a model inventory
• The process of model risk assessment
• The role of continuous monitoring
• The importance of a feedback loop

Module 10: The Ethics of AI Transparency

• The concept of algorithmic bias
• The role of fairness in model explanations
• The risk of model manipulation
• The ethical implications of an opaque model
• The importance of a human-in-the-loop approach

Module 11: Application to Credit Risk

• Explaining a credit default prediction
• The use of XAI in a loan application workflow
• Communicating a denial to a customer
• The role of XAI in compliance
• The future of XAI in credit risk management

Module 12: Application to Market Risk

• Explaining a VaR or ES calculation
• The role of XAI in stress testing
• Understanding the drivers of market volatility
• The importance of a transparent market risk model
• The future of XAI in market risk

Module 13: Application to Fraud Detection

• Explaining a fraud alert
• The challenges of a high-speed, high-volume environment
• The importance of a low-latency explanation
• The role of XAI in a fraud investigation
• The future of XAI in fraud detection

Module 14: Case Studies and Practical Implementation

• A deep dive into successful XAI implementations
• The challenges of a real-world project
• The importance of a strong business-data science partnership
• The role of leadership in adopting XAI
• Lessons learned from the field

Module 15: Final Project and Discussion

• A hands-on project to build and explain a risk model
• Presenting your findings and recommendations
• A discussion of remaining challenges and open problems
• The importance of a continuous learning mindset
• The future of XAI in finance

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.