Building production AI for digital payments, transaction-graph analytics, temporal machine learning, Generative AI and GPU-accelerated computing.
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 BigData
- Reviewer — IEEE DSAA, IEEE GSCon & IEEE ICMACC
- Reviewer — NeurIPS Workshops: VLM4RWD, JUDGe, AI and the Self & Who Verifies the Agents?
- 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 selected research highlights from 2025–2026 →
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
View NVIDIA RAPIDS cuGraph upstream contribution details →
- 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.