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AI for Supply Chain Management: Optimizing Logistics with Artificial Intelligence Course
USD $59.00 USD $249.00Price range: USD $59.00 through USD $249.00
Course Overview
AI for Supply Chain Management: Optimizing Logistics with Artificial Intelligence is an 8-week advanced program designed for M.Tech and M.Sc students, as well as professionals in BFSI, IT services, and consulting. This course explores AI applications in areas like forecasting, inventory management, and transportation logistics. Participants will learn to develop and implement AI models to optimize supply chains, enhancing operational efficiency and decision-making.
AI in Risk Management: Advanced Techniques for Financial Stability
Learn how AI supports risk functions—credit, fraud, market, operational, and compliance—using practical modeling, monitoring, and governance for reliable decision-making.
Aim
Equip participants to apply AI for early risk detection, loss reduction, and stronger controls—backed by explainable models, monitoring, and audit-ready governance.
What You’ll Learn
- Risk types and where AI adds value (detection, scoring, alerts)
- Data quality, bias, leakage, and explainability requirements
- Credit risk: scoring, PD/LGD/EAD concepts and ML approaches
- Fraud analytics: anomaly detection, behavior signals, network patterns
- Stress testing: scenario design and sensitivity analysis
- Monitoring: drift, stability, thresholds, and segment performance
- Governance: validation, documentation, approvals, audit trails
Who This Course Is For
- Risk, compliance, audit, and governance teams
- Banking/FinTech professionals in credit and fraud
- Analysts and managers building risk systems
Prerequisites
- Basic finance/risk familiarity is helpful
- No coding required (optional demos can be included)
Outcomes
- Risk use-case map + model selection checklist
- Monitoring plan (drift, thresholds, reporting)
- Governance checklist for audit readiness
Program Structure
- Module 1: Risk fundamentals and AI applications
- Module 2: Data, features, bias, and explainability
- Module 3: Credit risk modeling (scoring + PD/LGD/EAD)
- Module 4: Fraud & AML analytics (anomalies + networks)
- Module 5: Market and portfolio risk signals (conceptual)
- Module 6: Stress testing and scenario analysis
- Module 7: Monitoring, drift management, and alerting
- Module 8: Governance, validation, and documentation
Q&A
How do we keep models explainable for approvals?
Use clear features, reason codes, consistent validation reports, and segment-wise performance tracking. Add explainability checks to both approval and monitoring stages.
How do we manage drift and changing behavior?
Monitor drift metrics, stability indices, and performance by segment. Set alert thresholds and run controlled retraining with documentation and sign-off.
What are common implementation risks?
Data leakage, biased samples, weak monitoring, and over-automation without controls. Use validation, audit trails, and human review for high-impact decisions.
| Category | E-LMS, E-LMS + Videoes, E-LMS + Videoes + Live Lectures |
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