Explainable AI (XAI) in Credit Approval and Risk Models

Deep model adoption in credit and risk raises concerns about transparency and compliance. This course empowers practitioners to implement explainable AI (XAI) solutions leveraging SHAP, LIME, counterfactuals, and rule extraction—to justify model decisions in underwriting and risk scoring. Delegates will explore best practices, regulatory trends (e.g., US ECOA, EU AI Act), and creative explanations tailored for credit teams and regulators.

TRAINING TOPICS

• Introduction to XAI in credit and risk modeling
• SHAP, LIME, and counterfactual explanations
• Rule extraction and surrogate models for transparency
• Interpretable scorecards vs black box models
• Fairness metrics and bias mitigation in explainable workflows
• AI compliance and audit trails under ECOA, Basel, and AI Act
• Visual and narrative explanation strategies for stakeholders
• Integrating XAI with loan origination systems
• Alerting and monitoring for changing risk patterns
• Governance frameworks for model approval and retraining

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

• Explain model outputs using SHAP, LIME, and counterfactuals
• Compare scorecard based models with black box alternatives
• Detect and mitigate bias within explainable workflows
• Generate narrative explanations for credit decision summaries
• Align XAI practices with regulatory requirements and audits
• Embed explainability into loan origination UIs and systems
• Implement model monitoring for drift and explanation deviations
• Build a governance playbook for XAI in credit systems

TRAINING IS TAILORED TO

• Credit risk analysts and model validation teams
• Data scientists in financial services
• Lending platforms and fintech decision teams
• Compliance, audit, and regulatory reporting specialists
• Model governance and AI ethics officers
• Credit operations, underwriting, and risk managers

TRAINING METHODOLOGY

• Instructor led walkthroughs of XAI techniques
• Hands on SHAP/LIME and counterfactual labs
• Scorecard vs black box model comparison exercises
• Bias testing and fairness evaluation workshops
• Integration labs with loan origination systems
• Case studies from global banks under ECOA & EU AI Act
• Peer groups to draft XAI compliance and governance plans

Day One:

FOUNDATIONS OF XAI IN CREDIT RISK

• Overview: need for explainability under modern AI adoption
• Key XAI methods: SHAP, LIME, counterfactual, rule extraction
• Lab: SHAP explanations on a simple credit scoring model
• Case study: regulatory dilemmas in black box lending

Day Two:

INTERPRETABLE VS BLACK BOX MODELS

• Building interpretable scorecards (logistic, decision trees)
• Creating surrogate models to explain complex classifiers
• Lab: rule extraction on an XGBoost model
• Discussion: transparency vs performance tradeoffs

Day Three:

BIAS MITIGATION & FAIRNESS IN XAI

• Defining fairness metrics: demographic parity, equal opportunity
• Using XAI to uncover biased feature importance
• Lab: detect and mitigate bias using threshold adjustments
• Group exercise: fairness audit report for a credit model

Day Four:

NARRATIVE EXPLANATIONS & SYSTEM INTEGRATION

• Translating model outputs into consumer‑friendly language
• Generating counterfactual 'what if' explanations
• Lab: build explanation UI sample for loan officer
• Workflow: embedding XAI insights into origination pipelines

Day Five:

GOVERNANCE, MONITORING & COMPLIANCE

• Regulatory lens: ECOA, Basel Principles, EU AI Act
• Explanation logging, model validation, and audit metrics
• Lab: build a drift monitoring and explanation check pipeline
• Final group task: compose XAI governance playbook