Introduction

The proliferation of fintech has dramatically reshaped the credit landscape, moving beyond traditional banking models to create a dynamic and interconnected ecosystem. This evolution introduces new data sources, algorithmic underwriting, and novel business models, but it also brings unique and complex risks. Understanding the entire credit risk ecosystem—from the origination of a loan to its servicing and potential default—is paramount for anyone operating in this space. A holistic perspective is essential for identifying vulnerabilities, building resilient products, and ensuring sustainable growth in a rapidly changing market.

This training course provides a comprehensive exploration of the fintech credit risk ecosystem. You will gain a deep understanding of how to analyze credit risk in a non-traditional context, leveraging alternative data and advanced analytical techniques. We will cover the entire lifecycle of a digital loan, from customer acquisition to collection, and examine the unique risks at each stage. Through practical exercises and case studies, you will develop the skills needed to design, implement, and manage a robust credit risk framework that is both innovative and secure.

Duration: 10 Days

Target Audience

• Financial risk analysts
• Credit risk managers
• Data scientists
• Fintech product managers
• Internal and external auditors
• Regulatory compliance officers
• Investment analysts
• Operations managers
• Cybersecurity professionals
• Legal and compliance officers

Objectives

• Understand the core concepts of a fintech credit risk ecosystem.
• Master the principles of a data-driven risk framework.
• Analyze the key risks in an interconnected financial ecosystem.
• Learn to build and validate advanced risk models.
• Use quantitative tools for risk detection.
• Understand the regulatory framework governing these practices.
• Develop a framework for ethical data use.
• Assess the impact of new technology on risk.
• Understand the ethical implications of financial transactions.
• Use machine learning for predictive modeling.

Course Modules

Module 1: The Fintech Credit Ecosystem

• The ecosystem of digital lending.
• Common types of digital lending models.
• The importance of a risk-based approach.
• Understanding the regulatory lifecycle.
• The role of technology in compliance.

Module 2: Alternative Data and Credit Scoring

• The importance of a transparent and fair credit risk framework.
• The unique challenges of using alternative data.
• The difference between traditional and algorithmic lending.
• The role of technology in lending risk.
• The impact of a changing economic environment.

Module 3: Cybersecurity Risks

• 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: Underwriting and Origination

• 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: Credit Risk Modeling

• 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: Portfolio and Concentration Risk

• 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: Ethical AI and Bias Mitigation

• The principles of ethical AI in lending.
• 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 9: 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 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 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.