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
Technologies I use across production AI, graph analytics, GPU-accelerated computing, MLOps and scalable machine-learning systems.
Contributing to the research and standards community through program-committee service, peer review and trustworthy AI standards work.
- 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 →
Research profiles and author identifiers providing additional publication, citation and peer-review records across major academic databases.
Google Scholar · IEEE Xplore · ORCID · Scopus · Web of Science · ResearchGate · OpenReview · DBLP · Semantic Scholar · ACM Digital Library
Selected work in technical authorship, education and knowledge sharing, spanning a published book, online learning and practitioner-focused articles.
- 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 →
Selected writing, knowledge-sharing and developer-community profiles where I publish technical perspectives and participate in discussions across AI, data science, machine learning and high-performance computing.
Selected examples of external recognition, official nominations and independent references to my work and contributions across AI, open source, fintech and the broader technology community.
View independent industry recognition and external references →
Selected examples of my contributions to open-source AI and high-performance computing, including work related to NVIDIA RAPIDS and scalable graph analytics.
View open-source contribution details →
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.