AI for Credit Scoring & Decision Automation

AI has transformed credit decisioning by enabling faster, datadriven, and more accurate assessments of creditworthiness. This course explores how AI models especially machine learning and explainable AI—are applied in credit scoring, loan underwriting, and automated decision workflows. Participants will learn to design fair, compliant, and scalable AIdriven credit evaluation systems that align with global regulatory standards.

TRAINING TOPICS

• AI and ML foundations in credit risk modeling
• Feature engineering and data pipelines for credit scoring
• Supervised learning techniques: logistic regression, XGBoost, neural networks
• Explainable AI (XAI) for credit transparency and compliance
• Alternative data integration (e.g., mobile, utility, social data)
• Automated credit decisioning workflows and loan origination
• Bias mitigation techniques and fairness metrics
• Realtime scoring APIs and system architecture
• Regulatory compliance: GDPR, ECOA, Basel guidelines
• Monitoring and retraining credit models over time

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO

• Build machine learning models for credit scoring
• Apply AI tools to automate credit decision workflows
• Design and evaluate explainable AI for credit transparency
• Leverage traditional and alternative data sources effectively
• Mitigate bias and measure fairness in AIdriven scoring
• Deploy realtime scoring systems via APIs
• Align credit models with regulatory requirements and audit trails
• Monitor, recalibrate, and govern AI models for performance and risk

TRAINING IS TAILORED TO

• Credit risk analysts and credit managers
• Data scientists in financial services
• Banking product managers and loan officers
• Fintech product and compliance teams
• Underwriters and decision model designers
• Risk management and internal audit professionals

TRAINING METHODOLOGY

• Instructorled presentations and model walkthroughs
• Handson Python notebooks using real credit datasets
• Peer code reviews and group discussions
• Labs for model building, fairness testing, and API deployment
• AI tools: Scikitlearn, SHAP, Lime, TensorFlow, Azure ML
• Live demonstrations with credit scoring platforms
• Case studies from global banks and fintechs

Day One:

FOUNDATIONS OF AI IN CREDIT RISK

• Evolution of credit scoring: from FICO to AI
• Core datasets and credit attributes
• Introduction to ML techniques: decision trees, logistic regression
• Performance metrics: AUC, confusion matrix, precision/recall
• Handson: build a logistic regression credit score model
• Case study: traditional bank vs. fintech approach

Day Two:

FEATURE ENGINEERING & DATA PIPELINES

• Creating predictive features from raw financial data
• Feature selection, scaling, binning, and encoding
• Dealing with missing data and outliers in credit datasets
• Alternative data (mobile, ecommerce, utility, telecom)
• Handson: credit data pipeline in Python
• Lab: build a feature set for an SME credit scoring model

Day Three:

AUTOMATED DECISIONING & XAI

• Workflow automation for loan origination decisions
• Building credit scorecards with XGBoost and ensemble models
• Interpreting models with SHAP, Lime, and partial dependence plots
• Bias detection and mitigation: demographic parity, equal opportunity
• Handson: train and explain an XGBoost credit scoring model
• Group discussion: fairness tradeoffs and model auditability

Day Four:

DEPLOYMENT, API INTEGRATION & MONITORING

• Deploying ML models with FastAPI, Flask, or Azure Functions
• Creating scoring APIs for digital lending platforms
• Model monitoring: drift detection and performance checks
• Governance frameworks for credit AI systems
• Handson: deploy a scoring model and test via REST API
• Case study: realtime scoring in digital banks

Day Five:

COMPLIANCE, AUDIT & FUTURE TRENDS

• Regulatory frameworks: GDPR, ECOA, Basel, AI Act
• Documentation, traceability, and AI audit practices
• AI governance tools and risk scoring dashboards
• Emerging trends: federated learning, synthetic data
• Final project: build and explain a compliant credit scoring system
• Wrapup: futureproofing AI decision systems in credit