In the landscape of modern finance, the ability to accurately assess and predict credit risk is paramount for the stability and profitability of lending institutions, including Savings and Credit Cooperative Organizations (SACCOs). Loan Default Prediction Using Machine Learning represents a revolutionary leap beyond traditional credit scoring models, leveraging advanced algorithms and vast datasets to identify patterns and indicators that signal a higher likelihood of loan default. This data-driven approach allows lenders to make more informed decisions during the loan application process, optimize their risk exposure, refine pricing strategies, and proactively engage with borrowers at risk, thereby minimizing potential losses and improving portfolio quality. By harnessing the power of machine learning, financial institutions can move from reactive risk management to proactive prediction, leading to more efficient capital allocation, enhanced regulatory compliance, and ultimately, a more resilient lending operation that better serves both the institution and its borrowers. Without embracing Loan Default Prediction Using Machine Learning, organizations risk operating with outdated risk models, suffering from higher non-performing loan rates, and losing competitive advantage in a data-centric financial world, underscoring the vital need for specialized expertise in this critical domain.

Duration: 5 Days

Target Audience

• Credit Risk Managers and Analysts
• Data Scientists and Data Analysts in Financial Institutions
• Loan Officers and Underwriters
• Portfolio Managers
• Business Intelligence Professionals
• IT Professionals supporting credit operations
• Compliance Officers (for model validation)
• Financial Modellers
• SACCO Managers and CEOs (for strategic oversight)
• Anyone involved in credit decision-making or risk management

Objectives

• Understand the fundamental concepts of machine learning for credit risk.
• Learn about various machine learning algorithms suitable for loan default prediction.
• Acquire skills in preparing and cleaning data for predictive modeling.
• Comprehend techniques for building, training, and evaluating machine learning models.
• Explore strategies for interpreting model results and identifying key risk drivers.
• Understand the importance of model validation and monitoring for ongoing accuracy.
• Gain insights into ethical considerations and regulatory compliance in AI/ML credit scoring.
• Develop a practical understanding of integrating ML models into the loan lifecycle.

Course Content

Module 1: Introduction to Loan Default Prediction and Machine Learning Basics

• The importance of credit risk management in lending.
• Limitations of traditional credit scoring methods.
• Introduction to machine learning: what it is and why it's revolutionizing finance.
• Supervised vs. Unsupervised learning in credit risk.
• Overview of the machine learning project lifecycle for prediction.

Module 2: Data Preparation and Feature Engineering for Credit Risk

• Identifying relevant data sources for loan default prediction (e.g., historical loan data, credit bureau data, demographic data).
• Data collection, cleaning, and preprocessing techniques (handling missing values, outliers).
• Feature engineering: creating new, predictive variables from raw data.
• Data scaling and transformation for machine learning models.
• Ethical considerations in data selection and bias prevention.

Module 3: Foundational Machine Learning Algorithms for Classification

• Logistic Regression: principles and application for binary classification.
• Decision Trees: intuitive models for decision rules.
• Random Forests: ensemble learning for improved accuracy and robustness.
• K-Nearest Neighbors (KNN): instance-based learning.
• Hands-on exercises with basic algorithm implementation (conceptual, not coding-intensive).

Module 4: Advanced Machine Learning Models for Prediction

• Gradient Boosting Machines (GBM): powerful algorithms (e.g., XGBoost, LightGBM).
• Support Vector Machines (SVM): effective for complex classification tasks.
• Neural Networks / Deep Learning (basic introduction to concepts for tabular data).
• Understanding the strengths and weaknesses of each algorithm for loan default.
• When to use which model based on data characteristics and problem complexity.

Module 5: Model Training, Evaluation, and Selection

• Splitting data: training, validation, and test sets.
• Model training process and hyperparameter tuning.
• Evaluation metrics for classification models: Accuracy, Precision, Recall, F1-Score.
• ROC curves and AUC (Area Under the Curve) for model comparison.
• Cross-validation techniques to ensure model robustness.

Module 6: Model Interpretation and Explainability (XAI)

• The "black box" problem of complex ML models.
• Techniques for interpreting model predictions: Feature Importance, SHAP values, LIME.
• Understanding why a model makes a particular prediction for a loan application.
• Communicating model insights to non-technical stakeholders.
• Building trust and transparency in AI-driven decisions.

Module 7: Model Validation, Monitoring, and Deployment

• Post-deployment monitoring of model performance: drift detection, re-calibration.
• Regulatory expectations for model validation in financial services.
• Stress testing and scenario analysis for predictive models.
• Integrating ML models into existing loan origination and risk management systems.
• MLOps (Machine Learning Operations) concepts for production deployment.

Module 8: Ethical AI, Bias, and Future Trends in Credit Risk

• Addressing algorithmic bias in credit scoring (e.g., fairness metrics, bias mitigation techniques).
• Data privacy and security in predictive modeling.
• Regulatory compliance for AI/ML in lending.
• The future of credit risk: alternative data sources, continuous monitoring, explainable AI.
• Building a responsible and ethical AI framework for credit decisions.

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.