AI models show promise in detecting fraudulent banking operations

This paper explores the application of artificial intelligence, specifically machine learning models, to detect fraudulent banking operations. The study highlights the increased prevalence of such fraud due to the COVID-19 pandemic and the shift to online platforms. Researchers developed and compared various models, including artificial neural networks, logistic regression, and stacked generalization, to identify fraudulent transactions. The stacked generalization model achieved the highest AUC of 0.954, outperforming others in accuracy. AI

IMPACT This research could lead to improved security measures and reduced financial losses in the banking sector through more accurate fraud detection systems.

RANK_REASON The cluster contains a research paper detailing the application of AI models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• artificial intelligence
• artificial neural network
• arXiv
• COVID-19
• logistic regression model
• Stacked generalization

• paper
• product

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