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Graph neural networks

Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

Judge a Transaction by the Company It Keeps — Somya Rai & MD Sharique, EXL

EXL developed an advanced fraud detection system that augments XGBoost with Graph Neural Network (GNN) embeddings. Traditional tabular models struggle to capture fraud signals residing in transaction neighborhoods. The system's innovative design includes meticulous sampling strategies, disjoint monthly graph construction for leakage control, and a GraphSAGE architecture that aggregates mean, max, and standard deviation of neighbor features to detect subtle fraud patterns. Emphasizing explainability for regulatory compliance and analyst trust, it also features a highly optimized serving architecture achieving 3ms P99 latency, pivoting to edge prediction to reduce cache footprint from 60GB to 2-3GB. This led to a 27-33% relative lift in true positive rates and a projected $65 million incremental fraud capture.

The Future of the Grid: How AI Transforms Power System Planning

The Future of the Grid: How AI Transforms Power System Planning

Microsoft Research introduces GridSFM, an open foundation model designed to accelerate power grid analysis. By combining open data, machine learning, and physics-based modeling, GridSFM provides millisecond-level approximations of AC Optimal Power Flow (ACOPF) solutions, enabling faster scenario evaluation, planning, and research for increasingly complex energy systems.

AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent

AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent

Varsha Shah's research introduces an AI-driven framework for enterprise financial compliance and fraud detection, overcoming the limitations of traditional systems that analyze documents in isolation. The framework combines graph-based entity correlation, adaptive probabilistic risk modeling, and cross-jurisdictional normalization to uncover hidden fraud patterns across payroll, tax, procurement, and financial records. Evaluated on 3 million anonymized records across four jurisdictions, it demonstrates significant improvements in detection accuracy (91% precision, 87% recall), reduces false positives by 76%, and lowers manual audit effort by 40%, ultimately transforming compliance from a reactive process into a proactive, intelligence-driven capability through continuous learning.

Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models

Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models

An introduction to Graph Neural Networks (GNNs), covering fundamental concepts like nodes, edges, and embeddings. This post delves into the core message-passing mechanism and provides a detailed overview of key architectures including GCN, GraphSAGE, GAT, GIN, and Graph Transformers, explaining their unique approaches and mathematical formulations.

Graph Neural Networks Just Solved Enterprise AI?

Graph Neural Networks Just Solved Enterprise AI?

Jure Leskovec introduces Relational Foundation Models (RFMs), a new class of models based on graph neural networks that learn directly from raw, multi-table enterprise data. This approach bypasses manual feature engineering, leading to more accurate, faster-to-deploy, and easier-to-maintain predictive models for tasks like churn prediction, fraud detection, and recommendation systems.