Temporal Feature Analysis of Transaction Sequences for Payment Fraud Identification in Small and Medium-Sized Enterprises

Abstract

Payment fraud detection in small and medium-sized enterprises demands sophisticated analytical frameworks capable of processing sequential transaction patterns. This investigation develops a temporal feature extraction methodology that analyzes transaction timing distributions, amount variation dynamics, and behavioral consistency metrics across payment sequences. Our framework integrates interval-based statistical measures with probabilistic modeling approaches to identify anomalous patterns in SME transaction data. The proposed system achieves 94.3% detection accuracy on real-world transaction datasets while maintaining false positive rates below 2.1%. Through systematic analysis of 847,000 transaction sequences from 3,200 SMEs across six industry sectors, we establish quantitative relationships between temporal feature combinations and fraud pattern manifestations. The methodology addresses three critical challenges: micro-payment splitting detection through entropy-based sequence analysis, account takeover identification via behavioral deviation metrics, and adaptive threshold calibration for industry-specific transaction characteristics. Experimental validation demonstrates 27.8% improvement in early fraud detection compared to amount-only baseline methods, with computational efficiency enabling real-time deployment in resource-constrained SME environments. The investigation provides empirical evidence for temporal feature primacy in sequential fraud pattern recognition, establishing foundations for next-generation payment security systems.