TEMPORAL AND CHANNEL-SPECIFIC PATTERNS IN NIGERIAN FRAUD: INTERPRETABLE MACHINE LEARNING ON A LARGE-SCALE SYNTHETIC DATASET

GBOLAHAN ADENIRAN  IDOWU; JOSIAH ENDURANCE  OWOLABI

GBOLAHAN ADENIRAN IDOWU DEPARTMENT OF MATHEMATICS, LAGOS STATE UNIVERSITY, OJO, LAGOS STATE, NIGERIA.
JOSIAH ENDURANCE OWOLABI DEPARTMENT OF STATISTICS, UNIVERSITY OF LAGOS, AKOKA, LAGOS STATE, NIGERIA.

Keywords: Synthetic data, Interpretability, Temporal analysis, Channel-specific fraud, Nigerian banking

Abstract

An examination of temporal and channel-specific fraud patterns is conducted using a large-scale synthetic dataset (1 million records) calibrated to Nigerian (NIBSS 2023) fraud distributions. Comparative evaluation of Logistic Regression, Random Forest, and XGBoost models, supported by SHAP interpretability, reveals that the Web (0.34%) and Mobile (0.33%) channels has highest risk. January (0.53%) and 01:00 (0.36%) are identified as peak fraud periods. Analysis confirms that there exist negligible linear correlation between temporal features and fraud, validating the need for non-linear ensemble ap-proaches. The study concludes by proposing an interpretable, channel-aware framework for real-time risk scoring applicable to emerging markets.

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TEMPORAL AND CHANNEL-SPECIFIC PATTERNS IN NIGERIAN FRAUD: INTERPRETABLE MACHINE LEARNING ON A LARGE-SCALE SYNTHETIC DATASET

Abstract

References

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