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All Journal MUQADDIMAH: Jurnal Ekonomi, Manajemen, Akuntansi dan Bisnis
Rachmawati Rachmawati
STIE Enam Enam Kendari
Economics, Econometrics & Finance

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Meta-Learned Basis Reconfiguration: Drift-Aware Hypernetwork Ensembles for Real-Time Financial Fraud Detection Rachmawati Rachmawati; Ibnu Hajar; Ummy Kalsum; Wahyuni Rahmah
MUQADDIMAH: Jurnal Ekonomi, Manajemen, Akuntansi dan Bisnis Vol. 3 No. 1 (2025): Jurnal Ekonomi, Manajemen, Akuntansi dan Bisnis
Publisher : LP3M INSTITUT KH YAZID KARIMULLAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59246/3kz0zg73

Abstract

Financial fraud detection in transaction streams faces distributional change, severe class imbalance, delayed outcome labels, and strict inference-time constraints. This study presents a drift-aware ensemble architecture in which ADWIN and Page–Hinkley statistics condition a hypernetwork that generates low-dimensional adaptation coefficients and detector-gating weights. Episodic meta-training is designed to expose the controller to simulated changes in fraud behavior, while a dual-timescale mechanism separates rapid configuration updates from slower controller refinement. The architecture further specifies versioned logging for decision traceability. An evaluation protocol is described for chronological transaction streams, including static and adaptive baselines, precision–recall performance, adaptation delay, serving latency, and component ablations. Numerical results presented in this manuscript require verification against the original experiment outputs and implementation before they can be treated as empirically established findings. The central contribution is a testable architecture for conditional ensemble adaptation; its comparative effectiveness and deployment readiness remain subject to reproducible validation.
Co-Authors Ibnu Hajar Kalsum, Ummy Wahyuni Rahmah