Traditional credit scoring models are fundamentally ill-equipped to assess the creditworthiness of a large segment of the global population. Relying on formal financial histories that simply do not exist for the unbanked and underbanked, these systems have inadvertently created a major barrier to financial inclusion. Artificial intelligence (AI) offers a revolutionary solution by enabling lenders to move beyond a limited data set and tap into a wealth of alternative data—from mobile phone usage to social media activity. This shift empowers financial institutions to make more accurate and fair lending decisions, unlocking credit for millions who were previously excluded from the formal financial system.

This course provides a comprehensive and practical guide to designing, building, and implementing AI-powered microcredit risk scoring models. You will learn to source and analyze non-traditional data sets, apply machine learning algorithms to predict default risk, and create a system that is both highly predictive and ethically sound. By mastering these cutting-edge techniques, you will be prepared to lead the charge in a new era of lending—one that is smarter, more efficient, and committed to promoting equitable access to financial services.

Duration: 5 days

Target Audience:

• Data scientists and machine learning engineers
• Risk analysts and credit officers
• Microfinance institution (MFI) managers
• FinTech startup founders
• Financial inclusion practitioners
• Business analysts and product managers
• Regulators and policy advisors
• Professionals in telecommunications and mobile network operations
• Academics and researchers in financial technology

Objectives:

• Understand the limitations of traditional credit scoring for micro-lending.
• Master the process of sourcing and leveraging alternative data.
• Apply machine learning algorithms for predictive credit scoring.
• Build and validate an AI-powered risk scoring model.
• Analyze and interpret model outputs using Explainable AI (XAI).
• Identify and mitigate algorithmic bias in lending decisions.
• Grasp the ethical and privacy considerations of using AI for credit scoring.
• Develop a strategy for integrating an AI model into a lending workflow.
• Evaluate the business impact of implementing AI for risk scoring.
• Explore future trends and technologies in AI-driven lending.

Module 1: The Credit Scoring Challenge

• The importance of microcredit for economic empowerment
• Why traditional credit scoring fails the unbanked
• The role of financial inclusion in global development
• The need for new, innovative risk assessment methods
• A case for AI and alternative data

Module 2: Foundations of AI and Machine Learning

• Introduction to AI, machine learning, and deep learning
• Key machine learning concepts (supervised vs. unsupervised learning)
• Common algorithms used in credit scoring (e.g., logistic regression, random forest)
• The model development lifecycle
• Tools and platforms for building AI models

Module 3: Sourcing Alternative Data

• What constitutes alternative data (e.g., mobile, psychometric, e-commerce)
• Data collection and aggregation methods
• Challenges of data quality and reliability
• The importance of a well-defined data strategy
• Legal and ethical considerations of data collection

Module 4: Data Preparation and Feature Engineering

• Data cleaning and normalization techniques
• Handling missing and imbalanced data sets
• Feature engineering for predictive power
• Creating meaningful variables from raw data
• The role of domain expertise in feature creation

Module 5: Building a Predictive Model

• Splitting data for training and testing
• Training a machine learning model
• Model tuning and hyperparameter optimization
• The importance of cross-validation
• Evaluating model performance (e.g., accuracy, precision, recall)

Module 6: Model Validation and Deployment

• Testing the model's performance on unseen data
• Validating model against business objectives
• Deploying the model into a production environment
• Monitoring model performance over time
• The importance of an automated pipeline for updates

Module 7: The Ethical AI Framework

• Algorithmic bias and its sources
• The dangers of perpetuating historical discrimination
• Fairness metrics and bias detection techniques
• Strategies for bias mitigation in the model
• The importance of transparent and explainable AI (XAI)

Module 8: Explainable AI in Practice

• What is Explainable AI (XAI)
• Methods for interpreting model decisions (e.g., SHAP, LIME)
• Communicating model results to non-technical stakeholders
• Using explanations for risk management and client feedback
• Building trust with clients through transparency

Module 9: Business Integration & Strategy

• Integrating the AI model into the lending workflow
• Creating a new credit decisioning process
• The impact on loan officer roles and responsibilities
• Measuring the business impact (e.g., lower default rates, increased approvals)
• Developing a long-term AI strategy for your organization

Module 10: Future of AI in Lending

• The rise of deep learning and its applications
• Using blockchain for secure data sharing
• The role of open banking and APIs
• The potential of AI to promote global financial inclusion
• The evolving regulatory landscape for AI in finance

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 7 working days before commencement of the training.