Fraud Detection News and Research

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Fraud detection with AI involves utilizing machine learning algorithms to analyze patterns and anomalies in data to identify and prevent fraudulent activities or transactions. It enables the automated detection of potentially fraudulent behavior, providing timely alerts and improving the overall security of financial systems and processes.

Researchers Propose Global AI Framework To Tackle Rapid Tech Challenges

Research paper examines the complexities of global AI governance, proposing a cautious approach to developing an international regulatory framework that balances innovation with ethical and societal needs.

4 Sep 2024

Enhanced Dynamic Graph Learning with TASER

Researchers introduce TASER, a temporal adaptive sampling method, enhancing accuracy and efficiency in dynamic graph neural networks (TGNNs). TASER dynamically adjusts minibatch and neighbor selection, leveraging GPU-based tools for improved performance, effectively denoising training samples and supporting neighbors while achieving significant speedups in training time.

28 Mar 2024

Enhanced Workflow for High-Resolution Lithology Logs Using Multiple ML Algorithms

This article presents a novel workflow for generating high-resolution lithology logs from conventional well logs, addressing challenges in multiclass imbalanced data classification. The enhanced weighted average ensemble approach, incorporating error-correcting output code (ECOC) and cost-sensitive learning (CSL) techniques, outperforms traditional machine learning algorithms.

12 Dec 2023

Regulating Advanced AI: The Role of KYC Schemes in Ensuring Safety

In a proposal, researchers emphasize the need for the US government to mandate Know-Your-Customer (KYC) schemes for AI compute providers, especially cloud service providers, to address emerging security and safety risks associated with advanced AI models.

25 Oct 2023

Securing Food Supply Chains with AI and Federated Learning

Researchers demonstrate the potential of Artificial Intelligence (AI) and Federated Learning (FL) to predict and prevent food fraud while preserving data privacy in complex supply chains. Their framework, utilizing a data-driven Bayesian Network model, effectively integrated data from various sources and improved decision-making regarding food fraud control while upholding data confidentiality.

5 Sep 2023

Detecting Retail Crime with AI: A Game-Changing Strategy

Researchers proposed a machine learning strategy to identify and classify organized retail crime (ORC) listings on a well-known online marketplace. The approach utilizes supervised learning and advanced techniques, achieving high recall scores of 0.97 on the holdout set and 0.94 on the testing dataset.

6 Aug 2023

ThreatAdvice Unveils TAFraudSentry: AI-Powered Platform to Combat Check Fraud Surge

ThreatAdvice, a cybersecurity firm headquartered in Birmingham, Ala., has introduced TAFraudSentry, a fraud deterrence platform utilizing state-of-the-art artificial intelligence and image analysis techniques.

From ThreatAdvice 26 Jun 2023