Introduction

The increasing reliance on algorithmic credit models has introduced new challenges, particularly the potential for embedded biases that can lead to inequitable outcomes. While these models promise efficiency and speed, their reliance on historical data can perpetuate and even amplify existing societal biases, resulting in discriminatory lending practices. Identifying and mitigating these biases is not only a regulatory and ethical necessity but also a strategic business imperative for building trust and ensuring fair access to financial services for all consumers. This course is designed to provide you with the conceptual frameworks and practical tools to address this critical issue head-on.

This comprehensive program provides a deep dive into the theoretical and practical aspects of algorithmic fairness and its application in credit modeling. You will explore the interconnected nature of these fields, learning to assess the unique risks inherent in different modeling approaches and the nuances of various bias detection methodologies. The course emphasizes the quantitative and qualitative tools required to analyze a model's fairness, from advanced statistical tests to machine learning-based fairness metrics. By the end of this training, you will be proficient in using advanced analytics to ensure your credit models are both accurate and equitable, mitigating risk and promoting fairness in financial decision-making.

Duration: 10 Days

Target Audience

• Financial risk analysts
• Credit risk managers
• AI and machine learning engineers
• Data scientists
• Regulatory compliance officers
• Internal and external auditors
• CFOs and other senior finance executives
• Legal and compliance officers
• Financial technology (fintech) professionals
• Investment fund managers

Objectives

• Understand the core concepts of algorithmic bias and its risks.
• Master the principles of a fair and transparent model.
• Analyze the key fairness metrics in a credit model.
• Learn to build and validate advanced fairness models.
• Use quantitative tools for bias detection.
• Understand the regulatory framework governing these practices.
• Develop a framework for algorithmic fairness analysis.
• Assess the impact of model bias on a lending portfolio.
• Understand the ethical implications of financial transactions.
• Use machine learning for predictive modeling.

Course Modules

Module 1: Foundations of Algorithmic Fairness

• The importance of a transparent and fair model
• The unique challenges of digital banks
• The difference between traditional and algorithmic lending
• The role of technology in lending risk
• The impact of a changing economic environment

Module 2: Defining and Measuring Bias

• The principles of a fair model
• The use of internal and external data
• The role of decentralized finance (DeFi)
• The challenges of working with limited historical data
• The use of data visualization tools

Module 3: Data and Analytics for Fairness

• The importance of a clean and reliable dataset
• The use of internal and external data
• The role of alternative data
• The challenges of working with limited historical data
• The use of data visualization tools

Module 4: Econometric Models for Bias Detection

• The principles of an econometric model
• The use of time series analysis and regression
• The importance of a reliable dataset
• The validation and back-testing of models
• The challenges of working with limited historical data

Module 5: Machine Learning for Bias Detection

• The principles of a machine learning model
• The use of statistical methods and machine learning
• The importance of a reliable dataset
• The validation and back-testing of models
• The challenges of working with limited historical data

Module 6: Scenario Analysis and Stress Testing

• The concept of scenario analysis
• The role of stress testing in risk management
• The benefits for both lenders and regulators
• The importance of a robust technology platform
• The challenges of implementing these solutions

Module 7: The Regulatory and Legal Landscape

• An introduction to key financial regulations
• The role of regulators in overseeing digital banks
• The legal challenges of cross-border transactions
• The importance of a robust compliance program
• The ethical implications of financial transactions

Module 8: Bias Mitigation Strategies

• The types of bias mitigation instruments
• The use of credit insurance
• The importance of a robust bias mitigation strategy
• The challenges of implementing a dynamic bias mitigation program
• The role of derivatives in managing risk

Module 9: Portfolio Fairness Management

• The concept of portfolio fairness management
• The role of diversification in reducing risk
• The types of portfolio optimization techniques
• The importance of a data-driven approach
• The challenges of a rapidly changing market

Module 10: Capstone Project Part 1: Design

• Defining a specific lending portfolio for modeling
• Mapping the data requirements and model architecture
• Outlining the key technical and analytical requirements
• Creating a detailed project plan
• Presenting the design to a mock review board

Module 11: Capstone Project Part 2: Development

• Implementing a bias detection model for a chosen asset
• Using the model to evaluate a sample of transactions
• Analyzing the model's performance on key metrics
• Building a presentation to explain the analysis
• Documenting all assumptions and data sources

Module 12: Capstone Project Part 3: Presentation

• Presenting the full analysis of the chosen lending portfolio
• Discussing the risks and benefits of the model
• Proposing a plan for model improvement
• Q&A and peer feedback session
• Receiving expert recommendations and insights

Module 13: Emerging Trends

• The use of blockchain in lending
• The role of AI in risk assessment
• The evolution of lending instruments
• The impact of new data sources on risk assessment
• The future of lending risk

Module 14: Case Studies

• A case study of a major digital bank
• A case study of a fintech's risk management
• A case study of a financial institution's regulatory challenges
• The lessons learned from past credit cycles
• The importance of a robust framework

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.