Saurabh Kakkar
Independent Researcher
Dallas, Texas, United States
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Distinguished Regulatory Data & Risk Leader with over 12 years of international experience in regulatory
compliance, data analytics, AI/LLM-powered automation, and financial risk management. Recognized for
building Basel/CRR reporting platforms and pioneering the application of large language models (LLMs),
retrieval-augmented generation (RAG), and AI governance in high-stakes compliance environments.
Proven expertise in leading large-scale, cross-border programs, delivering innovative automation that has
transformed regulatory submissions and data governance across global financial institutions.
Area of Expertise
Topics
AI-Driven Credit Risk
Financial fraud is rising in the digital economy, causing major losses and weakening trust in banking systems. Conventional detection methods struggle with transaction data due to extreme class imbalance, high volume, and complexity, leading to delayed or inaccurate detection. This study proposes an integrated fraud-detection framework using LSTM-GRU (deep learning) and XGBoost (machine learning), supported by rigorous preprocessing: SMOTE for class balancing, outlier handling, normalization, and missing-data/feature treatment. Evaluation using accuracy, precision, recall, F1-score, confusion matrices, and ROC curves shows LSTM-GRU performs best (Acc 98%, Prec 95.19%, Rec 99%, F1 97.06%), outperforming XGBoost and traditional models (SVM, RF, KNN, GAT).
Trustworthy AI for Smart Cities: A Practical Governance Framework for High-Stakes Urban Systems
Artificial intelligence is rapidly becoming part of the operating layer of modern cities—from infrastructure monitoring and traffic optimization to energy management, digital public services, and predictive decision-making. As these systems become more autonomous and interconnected, the central challenge is no longer simply whether an AI model performs well. Cities must also determine whether its decisions can be trusted, governed, monitored, explained, and safely operated over time.
This session presents a practical framework for deploying trustworthy AI in mission-critical smart-city environments. Drawing on lessons from regulated financial systems, enterprise AI transformation, data governance, and research into AI/ML-enabled IoT for smart-city infrastructure, the presentation will examine how proven controls from highly regulated environments can be adapted to urban technology systems.
Attendees will learn how to structure AI governance across data quality, model validation, explainability, human oversight, continuous monitoring, accountability, and operational resilience. The session will also explore how these principles apply to connected infrastructure, predictive analytics, urban data platforms, digital twins, and AI-supported public services.
Rather than treating responsible AI as a compliance exercise, the session will show how governance can become an engineering discipline that enables cities to scale AI innovation while maintaining public trust, reliability, transparency, and measurable community outcomes.
2026 IEEE Smart City USA Conference Sessionize Event Upcoming
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