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28 April 2024, Sunday

Welcome to Our Series of Webinars - Deep Dives with IIQF Experts

Indian Institute of Quantitative Finance

AI, Machine Learning and Data Science Applications Areas for Climate Risk
Date: 28 April 2024, Sunday | Time: 11:30

Program details

Session Focus:

Description: Tune into IIQF 2024 BFSI quants practitioner’s know-how series sharing insights into AIML & Data Science application use cases & research topics for Climate risk modelling & analytics.

Participants would gain practical insights into topical components of Climate Risk modelling & how AIML data-driven approaches are paving the way to solve high-dimensional, data intensive & big data modelling problems.

Session Coverage:
  • Climate & Emerging Risk Landscape - Modelling & Analytics Use-Cases
  • Climate Risk Data Characteristics & Data Mining
  • AIML Solution Kit - Approaches & Techniques
  • AIML & Data Science innovations & novel research areas for Climate Risk modelling
  • Future Outlook & Challenges

Speaker: Sanjay Bhatia

Sanjay has done his MBA-Finance & MSc in Machine Learning & Artificial Intelligence from Liverpool John Moores University (LJMU) and Post-Graduate Diploma in Machine Learning & Artificial Intelligence from IIIT-Bangalore. He is currently working as a Director with UBS - Risk Modelling & Analytics, Model Risk Management & Control, and Chief Risk Office (CRO) Function.

Sanjay is a Risk Management & Analytics professional with 15 years of mainstay experience in risk models & methodology space encompassing BASEL Advanced AIRB, IFRS-9, IMM & SACCR Exposure, XVA, FRTB, OTC Deri Pricing & other advanced modelling topics. Previously worked with various foreign banks (Barclays, RBS, Citi, Credit Suisse) and in Risk Consulting.

He is also very passionate about imparting practical model understanding & insights and has been a professional trainer for Corporate & Investment Banks. His areas of interest are Credit & Counterparty Credit Risk, Market Risk, Stress Testing, Stochastic Modelling, Quantitative Finance and Machine Learning.

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