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All Journal Jurnal Ilmiah Edunomika (JIE)
Tiolina Pardede2
Fakultas Ekonomi dan Bisnis, Program Studi Doktoral Ilmu Manajemen, Perbanas Institute Jakarta
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education

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KESIAPAN DETEKSI FRAUD BERBASIS KECERDASAN BUATAN PADA PERBANKAN INDONESIA: PERSPEKTIF TATA KELOLA RISIKO, EFISIENSI BIAYA, DAN KAPABILITAS ORGANISASI MENUJU PERBANKAN BERKELANJUTAN Easter B.U. Tobing; Tiolina Pardede2; Saripudin
JURNAL ILMIAH EDUNOMIKA Vol. 10 No. 2 (2026): EDUNOMIKA
Publisher : ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/jie.v10i2.20057

Abstract

The rapid digital transformation of Indonesia's banking sector has exponentially increased transaction volumes alongside escalating digital fraud complexity. This study examines how risk governance, cost efficiency, and organizational capability influence artificial intelligence (AI)-based fraud detection readiness, and analyzes the mediating role of AI readiness in its relationship with sustainable banking in Indonesian commercial banks. Grounded in the Technology-Organization-Environment (TOE) Framework, Resource-Based View, Dynamic Capabilities Theory, Institutional Theory, and ESG perspectives, this conceptual study proposes a structural model in which AI-based fraud detection readiness (M) mediates the relationships between three determinants — risk governance (X1), cost efficiency (X2), and organizational capability (X3) — and sustainable banking (Y). A quantitative survey targeting 250–300 Indonesian banking professionals is proposed, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0. Seven testable hypotheses are developed. This study contributes by introducing AI-based fraud detection readiness as a distinct multidimensional construct bridging AI adoption and banking sustainability, and offers actionable guidance for bank executives, risk managers, and regulators in designing responsible AI fraud detection programs aligned with ESG-sustainable banking objectives.
Co-Authors Easter B.U. Tobing Saripudin