Overview
American banks and regulators (Fed, OCC, FDIC) now expect rigorous model-risk governance for every AI system touching credit, market, or fraud workflows. This self-paced, video-based course gives you the hard skills to build, benchmark and defend AI risk models that pass internal audit and SR 11-7 guidelines.
You will learn to
- Map legacy risk KPIs to machine-learning objectives.
- Engineer features from transactional & alt-data using Python.
- Train and tune credit-risk, market-risk & fraud-detection models.
- Apply SHAP & LIME to meet model-explainability mandates.
- Stress-test & back-test models under Fed CCAR-style scenarios.
- Design monitoring dashboards that flag model drift in real time.
Who should attend?
Chief Risk Officers · Quantitative Analysts · Compliance Officers · FinTech Data Scientists · Auditors and Consultants serving US-based BFSI clients.
Syllabus
Module 1 · AI Landscape & US Regulatory Context
Key frameworks, SR 11-7, model governance lifecycle, sample OCC findings.
Module 2 · Data Ingestion & Feature Engineering for Credit-Risk
Data pipelines, feature stores, handling class imbalance.
Module 3 · Machine-Learning Algorithms for PD, LGD & EAD
Logistic regression, XGBoost, neural nets, calibration.
Module 4 · Market-Risk Models (VaR, ES) with Deep Learning
LSTM for time series, GARCH vs neural solutions.
Module 5 · AI-Powered Fraud Detection & Anomaly Analytics
Graph neural networks, real-time scoring, alert thresholds.
Module 6 · Model Explainability: SHAP, LIME, Counterfactuals
Global vs local explanations, regulator-friendly reporting.
Module 7 · Stress-Testing & CCAR Scenario Modelling
Macroeconomic scenarios, challenger models, capital impact.
Module 8 · Deployment, Monitoring & Model-Risk Governance
CI/CD pipelines, model-drift alerts, governance dashboards.
Capstone Project
Build and present an end-to-end AI credit-risk model for a mid-tier US retail bank, including feature notebook, training pipeline, SHAP report, and a Power BI dashboard that meets OCC validation checklists.
Meet Your Mentors
Fees & Financing
- Fee: $112 USD (one-time)
- Payment: Razorpay & PayPal available
- Certificate: PDF certificate & LinkedIn badge upon completion
Frequently Asked Questions
Do I need prior coding experience?
Basic Python is recommended; a pre-course primer is included.
Will this help with SR 11-7 compliance?
Yes — the syllabus maps directly to SR 11-7 and OCC Bulletin 2011-12.
Are sessions recorded?
All content is video-based and available on-demand; transcripts provided within 12 hours after release.
Is there a certificate?
Participants who score ≥ 70 % on labs and capstone receive a verifiable PDF certificate and LinkedIn badge.
Can I expense this?
The program qualifies as CPD/CE for FRM, CFA and CPA accreditations — check your employer’s policy.
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