AI for Risk Management in BFSI – Self-Paced US Edition

Learn at your pace with video-based content. For additional modules or mentor-led sessions, contact [email protected].

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★★★★★ 4.8/5

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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