Suresh Nageswaran
AI in Finance and Open Source Evangelist
New York City, New York, United States
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Suresh Nageswaran is a hands-on engineer with over 25 years of building solutions for wealth management and investment banking. An AWS-certified architect, he codes in Rust to deliver real-time payment systems, AI-driven advisor tools, and regulatory compliance platforms for Fortune 50 clients. Also a Linux enthusiast and open source contributor, Suresh collaborates with business and technology leaders to solve business challenges with practical, high-impact technology platforms.
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Topics
From Silos to Total Portfolio Approach: Building a Real‑Time Analytics Backbone for Asset Owners
Buy-side organizations are trapped in Strategic Asset Allocation (SAA) silos where separate teams manage asset classes with slow decisioning, conflicting mandates and weeks-long rebalancing cycles.
Chief Investment Officers (CIO) of pensions, sovereign‑wealth funds & large asset management complexes are pivoting to a Total Portfolio Approach (TPA) that demands live cross‑asset views, intraday liquidity stress‑tests & dynamic factor budgets at petabyte scale and under regulator scrutiny, but legacy systems can't deliver.
This talk presents a production-ready, open-source analytics backbone enabling SAA-to-TPA transition. Built with Apache Kafka+Flink (sub-second ingestion), XTDB (reg-compliant bitemporal data backbone), Druid/ClickHouse (ms OLAP), Spark+OR-Tools (real-time optimization), the architecture delivers <200ms end-to-end analytics at petabyte scale.
Asset owners can
(1) break down silos with unified portfolio views
(2) detect correlation breakdowns during regime changes
(3) execute liquidity-aware rebalancing in hours not weeks
(4) reduce TCO vs proprietary solutions
Attendees get deployment checklists, GitHub repos, migration roadmaps to transform investment operations.
Building an Open Source Agentic AI Platform for Financial Regulatory Compliance
Financial institutions spend millions on proprietary compliance solutions, locked into expensive vendor ecosystems. When regulators send ad hoc inquiries via email/PDF, legal teams face manual, error-prone processes taking weeks to interpret requests & compile responses from multiple data sources.
We present open source, agentic AI platform for regulatory compliance using NLP & bitemporal data.
Core architecture: XTDB provides immutable bitemporal storage for audit trails, answering "What did you know and when?" Open LLMs via Ollama enable natural language understanding, with FSI terminology dictionaries (NAICS, GICS). LangChain agents translate compliance questions to precise queries.
"Reason and Act" architecture Reason Agent interprets inquiries & identifies data sources; Query Agent generates validated queries & synthesizes regulatory-compatible responses. Stack: Kubernetes, OpenFaaS, Kafka, S3, HashiCorp Vault, OpenTelemetry, Redis, Spark, Keycloak, NiFi,Kubeflow.
AWS ref architecture with optional managed services (RDS, MSK, EKS) for faster deployment.
Attendees get blueprint for building AI systems in regulated environments using open source components, full audibility.
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