"The financial services sector stands at an inflection point where artificial intelligence transitions from experimental curiosity to operational necessity," reflects Tharakesavulu Vangalapat, Sr. Director of Data Science at Broadridge Financial Solutions. His observation captures the essence of a shift reshaping how capital markets process information, predict investor behavior, and navigate regulatory complexity. The transformation he describes has accelerated dramatically since 2023, with enterprise AI deployments in financial services growing from isolated pilot programs to mission-critical infrastructure supporting trillions of dollars in assets.
Vangalapat's nineteen-year career spans Fortune 500 companies and global technology leaders, where he has developed AI systems delivering measurable business outcomes. His work bridges academic research and industrial application, resulting in seven granted patents, peer-reviewed publications with sixteen citations, and AI platforms generating substantial revenue impact. At Broadridge Financial Solutions, where he has directed enterprise AI strategy since June 2021, his team engineered a Global Demand Forecasting Model incorporating generative AI and agentic frameworks that support $60 million in assets under management. The platform generated $4-5 million in annual recurring revenue during its first operational cycle, with long-term growth projections targeting $60 million.
The integration of generative AI capabilities enables the platform to process unstructured market sentiment data from news sources, analyst reports, and social media, while agentic AI components autonomously adjust forecasting parameters based on changing market regimes. Market research firm MarketsandMarkets projects the AI in fintech market will expand from $44.08 billion in 2024 to $98.43 billion by 2030, driven primarily by predictive analytics, automated risk assessment, and regulatory compliance tools.
Proxy Voting Systems and Document Processing Automation
Shareholder voting represents one of the most complex analytical challenges in corporate governance. Vangalapat designed and developed a Customer Policy Vote Prediction Engine that combines machine learning, natural language processing, and generative AI to automate shareholder voting analysis across large-scale proxy statements. This innovation generated over $100 million in cumulative client impact, according to performance metrics tracked since deployment. The system processes hundreds of pages of SEC filings, extracting relevant voting guidance and predicting institutional investor preferences with measurable reliability.
Financial institutions process enormous volumes of regulatory documents, from SEC filings to internal compliance reports. Vangalapat led the development of an Intelligent Document Processing framework for SEC filings, including Forms DEF 14A and 10-K, leveraging generative AI and agentic AI capabilities. The system automates data extraction across hundreds of pages using advanced natural language processing, large language models, and autonomous agent workflows that orchestrate multi-step document analysis tasks.
The innovation eliminated thousands of manual processing hours, saving $400,000 to $500,000 annually while reducing human error rates by over 90%. His optimization of AI pipelines and cloud architecture resulted in $25,000 monthly reductions in infrastructure costs. The document processing framework extends beyond simple optical character recognition to semantic understanding of regulatory language through generative AI models. Agentic AI workflows enable the system to autonomously navigate complex document structures, identify specific disclosure types, extract numerical data with appropriate context, and flag anomalies requiring human review.
Patent Portfolio and Academic Research Contributions
Vangalapat's industrial contributions rest on solid research foundations. His seven granted patents span AI and machine learning applications in lighting control, predictive maintenance, smart devices, and agricultural monitoring systems. Patent citations by independent researchers total sixteen, indicating meaningful influence on subsequent work in these areas. His publication record includes papers in IEEE journals and conferences, disseminating research to the global scientific community.
One particularly influential patent addresses predictive maintenance for smart lighting systems, technology he developed during tenure at Signify (formerly Philips Lighting) North America from November 2016 to June 2021. The system uses anomaly detection algorithms to proactively identify faults, firmware issues, and sensor malfunctions in connected lighting and IoT ecosystems. Vangalapat navigated this tension successfully through collaboration between Signify North America Research and MIT's Computer Science and Artificial Intelligence Laboratory, producing diagnostic models that reduced system downtime by approximately 25%.
Enterprise AI Infrastructure and Future Developments
Vangalapat led the development of a comprehensive MLOps infrastructure at Broadridge, building CI/CD pipelines, automated testing frameworks, and monitoring systems that improved deployment velocity by approximately 40%.
The emergence of large language models and generative AI systems after 2022 created opportunities for enterprise technology leaders to develop next-generation financial platforms. Vangalapat led the deployment of secure generative AI infrastructure at Broadridge, integrating the latest technologies, including OpenAI GPTs, Claude, AWS Bedrock, and Llama models with comprehensive audit logging, compliance frameworks, and guardrail mechanisms. His team developed cutting-edge agentic AI workflows that combine multiple specialized models, each handling specific tasks while maintaining security boundaries between components. These agentic systems represent a shift from simple question-answering to autonomous multi-step reasoning, where AI agents can plan actions, use tools, and iterate toward complex objectives.
Disclaimer: IBT does not endorse the above content.
