AI in Finance and Risk Management
Master GenAI, Machine Learning, and Financial Risk Modeling for BFSI Professionals
- 9 focused modules covering GenAI, Knowledge Graphs, ML, and multi-regulator risk frameworks - all in a single program.
- Capstone project: Design and present a GenAI risk agent for a real-world use case at your firm.
- EICTA consortium Certification on successful completion of the course.
Online
30+ Hours
₹45,000+GST
6th June, 2026
What you'll learn
- Apply machine learning and generative AI to real investment problems including security selection, alpha signal generation, and financial risk management
- Critically evaluate and lead generative AI initiatives in asset management and BFSI risk functions
- Design RAG-based generative AI agents for investment research, regulatory compliance, and real-time risk monitoring
- Build effective collaboration between investment teams, quantitative analysts, data scientists, and compliance professionals
- Measure and present the ROI of AI in investment and risk management processes to leadership and regulatory boards
Skills you'll gain
Tools
Campus Immersion Program
Immersive Learning Experience at IIT Kanpur
Faculty Interaction & Academic Engagement
Peer Networking & Collaborative Discussions
Access to EICTA Connect Events
Course Instructor
Vipin Chaudhary
Industry Expert
Vipin Chaudhary is the Head of Data and Analytics for Non-Financial Risk at a leading American bank in Mumbai, specializing in Generative AI, Machine Learning, Semantics, and Business Intelligence. He is a graduate of the Indian Institute of Technology Kanpur and holds a postgraduate degree from the Indian Institute of Management Indore, with over 5 years of teaching experience.
Hands-On Capstone Project
Apply your learning by building a real-world AI-driven solution for finance or risk use cases.
Integrated AI + Finance Curriculum
Learn how Machine Learning and GenAI are applied across credit, market, and operational risk.
GenAI & Agentic AI Workflows
Gain practical exposure to prompt engineering, RAG pipelines, and AI-driven automation in finance.
Industry Tools & Practical Exposure
Work with Python, Excel, ML models, and GenAI platforms used in real financial environments.