Mr. Rohit Nimmala

Harnessing Machine Learning for Climate Risk Forecasting in Financial Systems

Abstract:

Climate change is now a central risk factor for banks and financial institutions. As a Data Engineer working on climate risk analytics, I’ve seen firsthand how machine learning is changing the game for forecasting and managing these new challenges. In this talk, I’ll walk through what the climate Risks are, practical ways we can use machine learning to spot climate-related risks early, model their financial impact, and help institutions make smarter decisions. I’ll share real-world examples where we built data pipelines and models that bring together everything from company financials to emissions data and climate scenarios. We’ll look at how these tools help institutions in stress-testing portfolios, meet regulatory requirements, and stay ahead of climate-driven market shifts. Whether you’re a data scientist, a finance professional, or just curious about the future of risk management, you’ll leave with a clear sense of how machine learning can help us all build a more resilient financial system in the face of climate change.

Profile:

I'm a Senior Data Engineer with over 10 years of experience in Data Engineering and Machine Learning, currently working at Bank of America in Charlotte, NC. I hold a Master's in Information Technology from the University of Cincinnati and specialize in financial risk analytics, climate risk forecasting, and AI/ML applications in the financial sector. I've authored over 20 White papers focusing on Machine Learning applications in financial risk management and climate analytics. I actively contribute to the academic community through editorial board memberships with several journals and have conducted peer reviews for publications. My technical expertise spans big data technologies like Spark and Hadoop, real-time data processing, and advanced analytics. I'm passionate about leveraging data engineering to solve complex financial challenges. I believe in giving back to the professional community through mentorship and knowledge sharing.