Deep Patel
Senior Data Engineer at Robinhood
San Francisco, California, United States
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Deep Patel is a senior data engineering and analytics leader with over a decade of experience designing and scaling large-scale data platforms across fintech and technology organizations. He has led enterprise-critical initiatives spanning executive metrics, real-time analytics, and AI-driven decision systems, helping leadership teams measure ROI, manage risk, and operate with data they can trust. Deep frequently speaks and writes on modern data infrastructure, AI governance, and translating complex systems into clear business outcomes.
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Decision Intelligence in Financial Services: Turning Data into Action, Not Reports
Most financial organizations are rich in data but poor in decisions. This session explores how decision intelligence moves analytics beyond dashboards and static reports into systems that actively guide, automate, and improve decisions at scale. We’ll break down the data, analytics, and AI foundations required to operationalize decisions across customer workflows, risk and fraud—highlighting where traditional BI falls short and how modern platforms combine metrics, models, and business logic to drive measurable outcomes.
What attendees will gain
A practical understanding of what decision intelligence is — and how it differs from traditional BI and analytics
Real-world patterns for turning data, metrics, and models into automated or guided decisions
Insight into how leading financial institutions operationalize decisions across fraud, risk, and customer journeys
A framework for identifying high-impact decisions worth automating versus those that require human judgment
Actionable ideas for moving from reporting-focused analytics to outcome-driven decision systems
Real-Time Personalization in Finance: Architecture, Tradeoffs, and Risk
Real-time personalization is reshaping financial services, from fraud prevention and credit decisions to customer engagement and pricing. This session breaks down the end-to-end data and AI architecture required to deliver low-latency, high-confidence personalization at scale, while balancing accuracy, cost, and regulatory risk. We’ll explore key tradeoffs across streaming data pipelines, feature stores, model serving, and human-in-the-loop controls, with a practical lens on governance, fairness, and explainability in highly regulated environments.
What attendees will gain
A clear understanding of how real-time personalization systems in finance are architected end to end
Practical tradeoffs between latency, accuracy, cost, and compliance when operating at scale
Concrete patterns for streaming data, feature engineering, and model serving in regulated environments
A risk-aware framework for deploying personalization responsibly, including governance, fairness, and explainability
Actionable insights attendees can apply to their own fraud, credit, or customer experience use cases
Proving AI ROI in the Enterprise: Metrics That Survive the Boardroom
As AI investments scale, many organizations struggle to clearly demonstrate business value beyond technical performance metrics. Model accuracy, experimentation velocity, and adoption rates rarely answer the questions executives and boards actually ask.
This session focuses on how enterprises can design AI ROI metrics that translate directly into financial impact, risk reduction, operational efficiency, and decision quality. We’ll explore why traditional analytics KPIs fall short for AI initiatives and share practical frameworks for measuring value across real-world use cases such as automation, fraud detection, personalization, and decision intelligence.
Attendees will learn how to align AI metrics with business strategy, communicate impact to senior leadership, and build the measurement foundations needed to scale AI responsibly and confidently.
Key Takeaways:
Why most AI ROI metrics fail in the boardroom
Practical frameworks for measuring enterprise AI value
How to communicate AI impact to executives
Proving AI ROI in the Enterprise: Metrics That Survive the Boardroom
As AI investments scale, many organizations struggle to clearly demonstrate business value beyond technical performance metrics. Model accuracy, experimentation velocity, and adoption rates rarely answer the questions executives and boards actually ask.
This session focuses on how enterprises can design AI ROI metrics that translate directly into financial impact, risk reduction, operational efficiency, and decision quality. We’ll explore why traditional analytics KPIs fall short for AI initiatives and share practical frameworks for measuring value across real-world use cases such as automation, fraud detection, personalization, and decision intelligence.
Attendees will learn how to align AI metrics with business strategy, communicate impact to senior leadership, and build the measurement foundations needed to scale AI responsibly and confidently.
Key Takeaways:
Why most AI ROI metrics fail in the boardroom
Practical frameworks for measuring enterprise AI value
How to communicate AI impact to executives
Analytics in the Age of LLMs: How AI Is Changing Metrics, Dashboards, and Decision-Making
As large language models become embedded in enterprise workflows, analytics is shifting from static dashboards to AI-driven, conversational decision systems. Traditional metrics and reports were designed for human interpretation, not for reasoning by intelligent agents.
This session explores how LLMs are transforming analytics by acting as a semantic and reasoning layer over governed data. We’ll examine how natural language queries, automated insights, and AI-generated explanations are changing how teams consume metrics and make decisions. The talk will also address critical challenges such as data trust, hallucination risks, metric consistency, and governance in AI-powered analytics systems.
Attendees will gain practical insights into building analytics platforms that remain accurate, auditable, and decision-ready in an AI-native enterprise.
Deep Patel
Senior Data Engineer at Robinhood
San Francisco, California, United States
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