Introduction

In the face of increasing market volatility and complex risk factors, traditional credit models are often insufficient to provide a complete picture of a portfolio’s health. Digital twins offer a revolutionary solution, creating a virtual, dynamic replica of a credit portfolio that can be used to simulate various scenarios in real-time. By integrating a wide range of data, from individual borrower behavior to macroeconomic trends, digital twins enable financial institutions to move beyond static risk assessments and into a world of proactive, predictive analysis. This course will provide you with the conceptual and technical skills to build and leverage these powerful models.

This comprehensive program is designed to guide you through the process of building, validating, and using digital twin models for credit risk management. You will gain a deep understanding of the data pipelines and analytical frameworks required to create a living, breathing representation of your credit portfolio. The course will also cover the latest in simulation techniques and visualization tools, allowing you to run complex "what-if" scenarios and gain unparalleled insights into your portfolio's performance under stress. By the end of this training, you will be proficient in using advanced analytics to ensure your credit models are both accurate and predictive, mitigating risk and promoting fairness in financial decision-making.

Duration: 10 Days

Target Audience

• Financial risk analysts
• Credit risk managers
• Data scientists
• AI and machine learning engineers
• 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 a digital twin and its applications.
• Master the principles of a fair and transparent model.
• Analyze the key metrics in a credit model.
• Learn to build and validate advanced models.
• Use quantitative tools for risk detection.
• Understand the regulatory framework governing these practices.
• Develop a framework for credit portfolio analysis.
• Assess the impact of market volatility on a lending portfolio.
• Understand the ethical implications of financial transactions.
• Use machine learning for predictive modeling.

Course Modules

Module 1: Foundations of Digital Twin Modeling

• 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: Data and Analytics for Digital Twins

• 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 3: Econometric Models for Digital Twins

• 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 4: Machine Learning for Digital Twins

• 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 5: 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 6: 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 7: Risk Mitigation Strategies

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

Module 8: Portfolio Risk Management

• The concept of portfolio risk 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 9: 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 10: Capstone Project Part 2: Development

• Implementing a digital twin 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 11: 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 12: 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 13: 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

Module 14: Course Certification

• A final comprehensive assessment
• A final review of key concepts and objectives
• Issuance of a certificate of completion
• Post-course career guidance and networking
• A final Q&A with instructors

Module 15: Additional Tools and Techniques

• An introduction to model explainability tools
• The use of counterfactual explanations
• The importance of a human-in-the-loop
• The challenges of using these tools in production
• The future of ethical AI

Module 16: Risk Mitigation for Digital Twin Models

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

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.