Indian Institute of Technology Kanpur

AI in Finance and Risk Management

Indian Institute of Technology Kanpur

Fintech

Application Deadline Approaching

Last date to apply: January 8, 2027 · Program starts on January 9, 2027

About Programme

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.

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

Apply ML/GenAI to Real Investment ProblemsCritically Evaluate and Lead GenAI Initiatives in Asset ManagementDesign RAG-Based GenAI Agents for Investment Research and MonitoringBuild Bridges Between Investment Teams, Quants, Data Scientists, and ComplianceMeasure and Present ROI of AI in Investment Processes

Programme Content

Topics Covered

  • Global risk landscape and evolving financial risks
  • Basel IV and IFRS 9 evolution in risk frameworks
  • Rise of conduct and non-financial risks
  • Limitations of traditional rules-based risk systems
  • Shift toward AI-driven and data-centric risk approaches

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    Topics Covered

  • Credit risk modeling: PD, LGD, EAD
  • Market risk measures: VaR and Expected Shortfall (ES)
  • Stress testing and scenario-based risk assessment
  • Counterparty credit risk fundamentals
  • Backtesting and model validation approaches

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    Topics Covered

  • Supervised learning techniques for credit risk modeling
  • XGBoost and LightGBM for PD/LGD estimation
  • Unsupervised learning for anomaly and fraud detection
  • Isolation Forest and Autoencoders for risk signals
  • Time-series modeling using LSTM and Transformers
  • Model evaluation, performance, and deployment considerations

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    Topics Covered

  • Prompt engineering for financial and risk use cases
  • Retrieval-Augmented Generation (RAG) for regulatory interpretation
  • Hallucination mitigation in high-stakes risk environments
  • Agentic AI for alert triage and automated workflows
  • Scenario generation using GenAI systems

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    Topics Covered

  • Financial Knowledge Graphs and entity resolution
  • Relationship mapping and graph-based data modeling
  • GraphRAG vs traditional RAG architectures
  • Network analytics for fraud, insider trading, and money laundering
  • Community detection and graph-based risk intelligence

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    Topics Covered

  • ML-enhanced PD and LGD modeling approaches
  • Neural networks for covariance modeling and VaR estimation
  • Reinforcement learning for dynamic risk limits
  • GenAI-driven adverse scenario generation

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    Topics Covered

  • SR 11-7 and global model risk management frameworks
  • TRIM and equivalent regulatory expectations
  • Bias, fairness, and ethical considerations in credit models
  • Explainability techniques: SHAP, LIME, attention mechanisms
  • Regulatory alignment across global financial systems (RBI, SEBI, MAS, FCA, ECB)

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    Topics Covered

  • Scaling AI capabilities within risk functions
  • Agile operating model: risk, quant, tech, and compliance integration
  • Talent strategy: hiring, upskilling, and team structuring
  • Measuring ROI of AI initiatives in financial institutions
  • Communicating AI impact at leadership and board level

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    Topics Covered

  • Designing GenAI agents for risk processes
  • Fraud detection and monitoring use cases
  • Credit underwriting and risk assessment use cases
  • Conduct risk monitoring applications
  • Stress testing and scenario design using AI

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    Program Certificate

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