Predictive Analytics for Early Detection of Financial Fraud in Online Transactions
Abstract
Financial fraud poses a significant threat to online transactions, leading to substantial economic losses. This paper presents a predictive analytics framework for early detection of financial fraud using machine learning algorithms. We leverage transactional data, user behavior patterns, and network analysis techniques to identify fraudulent activities before they escalate. Experimental results demonstrate the effectiveness of our approach in reducing fraud-related losses.
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