Artificial Intelligence (AI) is transforming the financial sector, with credit scoring being one of the most impactful areas. The AI-Powered Credit Scoring Models Training Course equips participants with the knowledge and skills to design, implement, and evaluate advanced AI-driven credit assessment systems. Through hands-on learning, participants will explore machine learning algorithms, predictive analytics, and alternative data sources to enhance credit risk modeling and decision-making accuracy. This training goes beyond traditional methods, enabling professionals to adopt smarter, data-driven approaches to credit scoring while reducing default risks and improving financial inclusion.

This intensive program blends theory with real-world applications, covering innovative AI techniques, ethical considerations, model validation, and regulatory compliance. Designed for financial sector leaders, risk managers, data scientists, and policymakers, the course highlights practical strategies for using AI responsibly in lending ecosystems. By the end of the program, participants will gain the tools to build transparent, explainable, and scalable credit scoring models that support sustainable financial growth and mitigate risks in a rapidly evolving digital economy.

Duration: 10 Days

Target Audience

• Credit risk managers and analysts
• Banking and microfinance professionals
• Financial regulators and supervisors
• Risk and compliance officers
• Data scientists and AI specialists
• Fintech innovators and developers
• Credit portfolio managers
• Auditors and internal control experts
• Policy and decision-makers in finance
• Consultants in credit risk and digital finance

Objectives

• Understand the fundamentals of AI in credit scoring
• Apply machine learning algorithms to credit data
• Utilize alternative and non-traditional data sources
• Enhance risk prediction accuracy and decision-making
• Build explainable and transparent AI models
• Ensure compliance with global regulatory standards
• Integrate ethical AI practices into financial systems
• Apply validation and monitoring techniques for credit models
• Explore case studies of AI adoption in credit risk management
• Strengthen institutional capacity for AI-driven lending practices

Course Modules

Module 1: Introduction to AI in Credit Scoring
• Evolution of credit scoring methods
• Role of AI and machine learning
• Limitations of traditional scoring models
• Emerging trends in credit assessment
• Global adoption of AI in credit scoring

Module 2: Data Foundations for AI Credit Models
• Types of credit data
• Data quality and preprocessing
• Feature engineering techniques
• Handling missing and unstructured data
• Data ethics and governance

Module 3: Machine Learning Algorithms in Credit Scoring
• Logistic regression and decision trees
• Random forests and gradient boosting
• Neural networks for credit prediction
• Model selection and tuning
• Pros and cons of different approaches

Module 4: Alternative Data in Credit Scoring
• Use of mobile money and transaction data
• Social media and behavioral analytics
• Utility payments and digital footprints
• Geospatial and demographic data
• Opportunities and challenges of alternative data

Module 5: AI Model Development Workflow
• Defining objectives and scope
• Data collection and preparation
• Model training and testing
• Validation strategies
• Deployment considerations

Module 6: Explainable AI in Credit Scoring
• Importance of model interpretability
• SHAP and LIME techniques
• Transparency in automated decisions
• Building trust with stakeholders
• Communicating model outputs

Module 7: Risk Assessment and Prediction Accuracy
• Accuracy vs fairness in credit models
• Reducing false positives and negatives
• Sensitivity and specificity measures
• Performance metrics for credit scoring
• Model robustness under stress conditions

Module 8: Ethical and Responsible AI in Lending
• Avoiding algorithmic bias
• Ensuring fair lending practices
• Ethical use of consumer data
• Balancing innovation and responsibility
• Case studies of ethical challenges

Module 9: Regulatory Compliance in AI Credit Scoring
• Basel III/IV frameworks
• Data privacy and protection regulations
• IFRS 9 and credit loss provisioning
• Compliance with anti-discrimination laws
• Supervisory expectations for AI models

Module 10: Stress Testing AI Credit Models
• Scenario analysis and simulations
• Macroeconomic stress testing
• Back-testing methodologies
• Adjusting models under shocks
• Integrating stress tests into governance

Module 11: Fraud Detection with AI Models
• Identifying fraudulent credit applications
• Transaction monitoring systems
• Real-time anomaly detection
• Combining fraud prevention with credit scoring
• Emerging fraud risks in digital finance

Module 12: Model Validation and Monitoring
• Best practices in validation
• Back-testing vs out-of-sample testing
• Ongoing monitoring frameworks
• Benchmarking against industry standards
• Documentation and audit trails

Module 13: Case Studies of AI Credit Scoring
• Successful fintech adoption stories
• Global banking sector experiences
• Microfinance and AI integration
• Lessons learned from failures
• Innovations driving financial inclusion

Module 14: Practical Lab Sessions
• Hands-on machine learning projects
• Building credit risk models in Python
• Using open-source AI tools
• Working with alternative datasets
• Group exercises and peer reviews

Module 15: Future of AI in Credit Risk Management
• Quantum computing and credit modeling
• Integration with blockchain technology
• AI in real-time lending decisions
• Predictive risk analytics in open banking
• Next-generation credit ecosystems

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.