Introduction

In an increasingly data-driven world, a new frontier in financial analysis is emerging: the integration of geospatial data into credit risk models. Beyond traditional financial metrics, a borrower's location, and the surrounding geographic context, can reveal powerful insights into creditworthiness and risk. Factors such as local economic conditions, demographic trends, and proximity to resources all play a crucial role. This course is designed to provide you with the strategic insights and practical tools to effectively leverage geospatial data to build more precise, predictive, and profitable credit models.

This comprehensive program provides a deep dive into the theoretical and practical aspects of credit analytics and its application in geospatial data. You will explore the interconnected nature of these fields, learning to assess the unique risks inherent in different lending portfolios and the nuances of various credit scoring methodologies. The course emphasizes the quantitative and qualitative tools required to analyze a portfolio's financial health, from advanced econometric models to machine learning-based forecasting. By the end of this training, you will be proficient in using advanced analytics to make smarter decisions, manage credit risk, and unlock significant value in your financial institution's risk management activities.

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 credit analytics and geospatial data.
• Master the principles of geographic credit scoring.
• Analyze the key risk variables in a lending portfolio.
• Learn to build and validate advanced credit risk models.
• Use quantitative tools for portfolio optimization.
• Understand the regulatory framework governing these practices.
• Develop a framework for credit risk analysis.
• Assess the impact of macroeconomic shocks on a lending portfolio.
• Understand the ethical implications of financial transactions.
• Use machine learning for predictive modeling.

Course Modules

Module 1: Foundations of Credit Risk

• The importance of credit risk in financial reporting
• The unique challenges of digital banks
• The difference between traditional and digital credit scoring
• The role of technology in credit risk
• The impact of a changing economic environment

Module 2: Geospatial Data and Their Risks

• The principles of a geospatial dataset
• The use of internal and external data
• The role of satellite data and imagery
• The challenges of working with limited historical data
• The use of data visualization tools

Module 3: Data and Analytics for Credit Risk

• 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 Credit Risk

• 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 Credit Risk

• 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: 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 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 credit risk 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 credit 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

Module 15: 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

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.