Rohit Nimmala
Bank of America, Senior data Engineer
Charlotte, North Carolina, United States
Actions
Rohit Nimmala is a Senior Data Engineer at Bank of America specializing in machine learning and big data technologies. He holds an MS in Information Technology from University of Cincinnati and has published 24 peer-reviewed papers on ML applications in climate risk analytics and financial systems. He serves on editorial boards of several international journals and has received multiple honors. A sought-after speaker at international conferences, he actively contributes to the developer community with a 1560+ Stack Overflow reputation.
Links
Area of Expertise
Topics
Integrating Model Context Protocol with Oracle Select AI for Agentic BI on Autonomous Database
The rise of AI-assisted tooling has introduced new pathways for natural language access to enterprise databases, beyond the classical NL2SQL paradigm. Oracle Autonomous Database supports two distinct mechanisms for natural language BI: Oracle Select AI, which embeds AI reasoning natively within the database using DBMS_CLOUD_AI, and Oracle SQLcl MCP Server, which externalizes database access to AI agents through a standardized protocol. Both reflect different philosophies regarding where intelligence should reside—inside the data platform or within the orchestrating agent. This paper presents a comparative architectural analysis examining four governance dimensions: reasoning locus, trust boundary placement, audit completeness, and workload fit. Drawing on deployment experience across mortgage lending and healthcare analytics use cases, we propose a practitioner decision framework for enterprise architects evaluating natural language BI strategies on Oracle Autonomous Database 26ai. We further examine hybrid patterns wherein the Oracle Autonomous Database MCP Server exposes Select AI Agent tools to external MCP clients, offering a convergence pathway between the two models.
Deploying ML for Climate Risk Forecasting in Finance
Climate-related financial losses hit $145 billion in the US in 2024, up 40% from the previous year. Most financial institutions still use static, rule-based risk models built for yesterday's weather. This talk shows how leading banks are using machine learning to turn climate risk into a competitive advantage.
The Problem:
Traditional models use linear projections and fixed thresholds that miss the exponential nature of climate impacts. Financial institutions face three risk types: physical (your data center underwater), transition (your clients' assets stranded), and liability (your disclosure challenged in court). Each needs a different analytical approach. The real challenge is processing 10TB of daily satellite imagery, a million IoT readings per second, and tens of thousands of news articles and company reports into something actionable.
What We Built:
I'll walk through a production ML pipeline running at major financial institutions. We use an ensemble approach: Random Forest handles missing data and achieves 95% accuracy on default prediction. XGBoost captures non-linear patterns and detects 90% of risk events. LSTM networks provide 6-month early warning signals. Dynamic Beta models update climate sensitivity every trading day.
I'll cover two case studies. JPMorgan Chase's Carbon Assessment Framework processes 500 petabytes annually across 50,000 corporate clients, creating $1.5B in value. BNP Paribas built satellite-based ESG monitoring that detects methane leaks from space, saving €50M through early detection.
Results That Matter:
25% fewer climate-related losses. 60% faster risk assessments. 40% lower compliance costs. 50,000 analyst hours saved annually. Over $100B in green finance opportunities identified.
Attendees will leave with a practical framework for evaluating ML-readiness in their own climate risk work. The session works for data scientists looking at deployment patterns, risk managers exploring AI, and business leaders weighing the ROI of climate analytics.
Rohit Nimmala
Bank of America, Senior data Engineer
Charlotte, North Carolina, United States
Links
Actions
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top