AI has transformed credit decisioning by enabling faster, data-driven, 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 AI-driven credit evaluation systems that align with global regulatory standards.
- 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
- Real-time scoring APIs and system architecture
- Regulatory compliance: GDPR, ECOA, Basel guidelines
- Monitoring and retraining credit models over time
- 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 AI-driven scoring
- Deploy real-time scoring systems via APIs
- Align credit models with regulatory requirements and audit trails
- Monitor, recalibrate, and govern AI models for performance and risk
- 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
- Instructor-led presentations and model walkthroughs
- Hands-on Python notebooks using real credit datasets
- Peer code reviews and group discussions
- Labs for model building, fairness testing, and API deployment
- AI tools: Scikit-learn, SHAP, Lime, TensorFlow, Azure ML
- Live demonstrations with credit scoring platforms
- Case studies from global banks and fintechs
- 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
- Hands-on: build a logistic regression credit score model
- Case study: traditional bank vs. fintech approach
- 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, e-commerce, utility, telecom)
- Hands-on: credit data pipeline in Python
- Lab: build a feature set for an SME credit scoring model
- 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
- Hands-on: train and explain an XGBoost credit scoring model
- Group discussion: fairness tradeoffs and model auditability
- 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
- Hands-on: deploy a scoring model and test via REST API
- Case study: real-time scoring in digital banks
- 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
- Wrap-up: future-proofing AI decision systems in credit
Group & Corporate Discounts: Available for companies enrolling multiple participants to help maximize ROI. Individual Discounts: Offered to self-sponsored participants who pay in full and upfront. Registration Process: Corporate nominations must go through the client’s HR or Training department. Self-nominations must be prepaid via the “payment by self” option. Confirmation: All registrations are subject to DIXONTECH’s approval and seat availability. Refunds: Provided in case of course cancellation or no seat availability. Tax Responsibility: Clients are responsible for any local taxes in their country.