Introduction

In the complex and interconnected world of global supply chains, traditional credit risk models often fall short in capturing the intricate web of dependencies and dynamic relationships that can lead to financial instability. GAN-Based Credit Risk Forecasting represents a revolutionary leap forward, leveraging Generative Adversarial Networks to create synthetic, yet highly realistic, financial data that uncovers hidden patterns and vulnerabilities. This Predictive Power: Training Course on GAN-Based Credit Risk Forecasting in Supply Chains is a 10-day program designed to equip finance, supply chain, and data science professionals with the advanced skills needed to build, train, and deploy these cutting-edge models to predict and mitigate credit risk with unprecedented accuracy.

This course will guide you through the theoretical foundations of Generative Adversarial Networks (GANs) and their practical application in a financial context. You will learn how to generate synthetic financial data, simulate market shocks and black swan events, and integrate the output into your credit risk models. By the end of this training, you will be able to move beyond traditional forecasting methods, enabling you to stress-test your supply chain against a multitude of scenarios and secure your organization's financial health in an increasingly volatile global market.

Duration: 10 Days

Target Audience

• Financial analysts and managers
• Supply chain strategists
• Data scientists and machine learning engineers
• Credit risk analysts
• Quantitative researchers
• Financial risk managers
• Business intelligence professionals
• Academics and students in finance
• FinTech professionals
• Anyone involved in financial forecasting

Objectives

• Understand the foundational principles of Generative Adversarial Networks.
• Differentiate between traditional and GAN-based credit risk models.
• Acquire and preprocess financial data for machine learning models.
• Build and train a basic GAN for financial data generation.
• Evaluate the quality of generated synthetic data.
• Apply GANs to simulate credit default scenarios.
• Integrate GAN-generated data into a credit risk forecasting framework.
• Validate model performance and interpret results.
• Understand the ethical considerations of using GANs in finance.
• Develop a strategy for deploying GAN-based models in a business environment.

Course Modules

Module 1: Introduction to Credit Risk in Supply Chains

• The interconnected nature of supply chain finance
• Key drivers of credit risk in a global context
• Traditional credit risk models and their limitations
• The need for advanced, predictive analytics
• The role of data in modern credit risk assessment

Module 2: Generative AI and GAN Fundamentals

• What are Generative Adversarial Networks (GANs)?
• The architecture: Generator and Discriminator
• The adversarial training process
• Types of GANs and their applications
• Key concepts: loss functions, mode collapse

Module 3: Data Collection and Preprocessing

• Sourcing financial data for supply chain analysis
• Handling missing and imbalanced data
• Feature engineering for credit risk
• Normalization and data scaling techniques
• The importance of data privacy

Module 4: Building a Basic GAN Model

• Setting up the development environment
• An overview of deep learning frameworks
• Designing the Generator and Discriminator networks
• Defining the loss function and optimizer
• Initializing the model for training

Module 5: Training a GAN

• The training loop and best practices
• Monitoring training progress and convergence
• Dealing with common training challenges
• Fine-tuning hyperparameters for performance
• Practical exercises with a simple GAN

Module 6: Evaluating Generated Financial Data

• Metrics for assessing the quality of synthetic data
• Statistical comparison with real data
• Visualizing and interpreting generated distributions
• The importance of data fidelity
• Using a "realness" score

Module 7: GAN-Based Scenario Simulation

• Using GANs to simulate credit shocks
• Generating data for "black swan" events
• The value of synthetic data in stress-testing
• Creating realistic default scenarios
• Simulating the impact of external factors

Module 8: Credit Risk Forecasting with GANs

• Integrating synthetic data into risk models
• Building a predictive model on generated data
• Comparing performance with traditional models
• The role of GANs in a hybrid forecasting framework
• Case studies of GANs in financial forecasting

Module 9: Model Validation and Interpretation

• Techniques for validating model performance
• The importance of explainable AI (XAI)
• Interpreting model predictions and insights
• Communicating complex results to stakeholders
• The concept of model reliability

Module 10: Ethical Considerations and Bias

• The risk of perpetuating bias in GANs
• Fair lending and responsible AI practices
• The importance of transparency and accountability
• Mitigating bias in data and models
• The future of ethical AI in finance

Module 11: Deployment and Scalability

• Strategies for deploying GAN models in production
• Managing computational resources
• Integrating models into existing systems
• Continuous monitoring and retraining of models
• Scalability for large-scale supply chains

Module 12: Advanced GAN Architectures

• Conditional GANs (CGANs) for specific scenarios
• Wasserstein GANs (WGANs) for stability
• Self-Attention GANs (SAGANs)
• Practical applications of advanced GANs
• Emerging trends in generative AI

Module 13: The Future of Credit Risk

• The role of big data and real-time analytics
• The impact of emerging technologies
• How regulations will shape the market
• The growing importance of supply chain intelligence
• The transition to a more predictive approach

Module 14: Risk Mitigation and Strategy

• Developing a proactive risk mitigation plan
• The role of insurance and hedging strategies
• Using GAN insights to inform business decisions
• Building a resilient supply chain
• The link between credit risk and operational strategy

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.