I am Saurav Singla, Head of Data Science and an AI leader with 20+ years of experience translating research into scalable, production-ready systems. I specialise in Graph AI, temporal learning, fraud intelligence, money-mule detection and transaction-graph analytics for large digital ecosystems. My work combines AI leadership, applied research and hands-on engineering across production machine learning, scalable analytics and responsible AI.

• 20+ years across AI, data science, machine learning and analytics leadership.
• Lead AI and data science initiatives for one of the world's largest real-time digital payments ecosystems.
• Built production AI systems for fraud intelligence, money-mule detection, anomaly detection, graph analytics, federated AI and synthetic data.
• Published peer-reviewed research in Graph AI, temporal transaction graphs, adaptive fraud detection and high-performance analytics.
• Author of Machine Learning for Finance and educator to 21,000+ learners.
• Contribute to the international AI research and standards community through IEEE program committee service, peer reviewing and trustworthy AI standards work.

• Graph AI and financial crime: graph machine learning, graph neural networks, temporal graphs, transaction-network analysis, fraud detection and money-mule detection
• Scalable production AI: production machine learning, real-time analytics, MLOps, LLMOps, observability, testing and responsible AI governance
• GPU-accelerated analytics: CUDA, NVIDIA RAPIDS, cuGraph and high-performance graph computing
• Generative and agentic AI: LLMs, retrieval-augmented generation, agentic workflows and enterprise GenAI
• Applied machine learning: anomaly detection, time-series forecasting, incremental learning, reinforcement learning and knowledge distillation

• Program Committee Member — IEEE Big Data
• Reviewer — IEEE DSAA & NeurIPS
• Participant — IEEE P7022 TrustGenAI Working Group

My research focuses on graph machine learning, temporal transaction graphs, fraud intelligence, scalable AI, incremental learning, knowledge distillation, anomaly detection, reinforcement learning and high-performance computing.

Explore the GRADES-NDA 2026 and IEEE ICDE 2026 research highlights →

IEEE International Conference on Big Data (IEEE BigData 2025) · IEEE
DOI: 10.1109/BigData66926.2025.11402449

Proposes an adaptive fraud detection framework combining meta-learning, Kolmogorov–Arnold Networks (KAN) and ensemble learning to improve generalization against evolving fraud patterns in financial transaction systems.

Research Areas: Fraud Detection · Financial AI · Meta-Learning · KAN · Ensemble Learning

IEEE 32nd International Conference on High Performance Computing, Data and Analytics Workshops (HiPCW 2025) · IEEE
DOI: 10.1109/HiPCW66559.2025.00053

Presents a scalable framework for temporal graph motif mining over large-scale financial transaction networks, enabling efficient discovery of transaction patterns for anti-money laundering (AML) investigations and graph intelligence. Evaluated on public benchmark graphs and billion-scale UPI transaction data.

Research Areas: Graph AI · Temporal Graphs · AML · Transaction Intelligence · High-Performance Computing

Selected examples of how my published work has been independently reviewed, cited and extended by international researchers across healthcare simulation and natural-language processing.

View detailed research-impact evidence →

More research: Google Scholar · IEEE Xplore · ORCID · Scopus · Web of Science · ResearchGate · OpenReview · DBLP · Semantic Scholar · ACM Digital Library

• NVIDIA RAPIDS cuGraph — Upstream Contributor: Authored PR #5584 — Fix multi-seed ego_graph offset handling, merged August 2026 into the official rapidsai/cugraph repository; addressed issue #4191 by fixing multi-seed ego_graph handling and adding regression coverage across weighted, renumbered, directed and multi-column graph cases.

• Book: Authored Machine Learning for Finance: Beginner's Guide to Explore Machine Learning in Banking and Finance, published by BPB Publications in 2021. View book overview and author contribution →
• Course: Created Data Analysis for Business and Finance, reaching 21,000+ learners across statistics, probability, regression and time-series analysis. View course and educational impact →
• Technical articles: Explore selected articles published on Towards Data Science, HackerNoon and KDnuggets →
• Quora answers: Explore selected educational answers on AI, probability and machine learning →

• Global Fintech Fest 2025 AI Report — Featured expert cited for perspectives on Graph AI-based money-mule detection, model drift, retraining and false-positive reduction (page 11).

I welcome conversations around Graph AI, financial-crime intelligence, scalable machine learning, applied research and responsible production AI. Connect with me on LinkedIn for research collaboration, technical discussions and industry knowledge exchange.

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