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All Journal Journal of Management and Informatics JIMEB: Jurnal Ilmiah Manajemen, Ekonomi dan Bisnis
Faradila, Silvia
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Enhancing Decision Quality and Transparency via Machine Learning-Based Goodwill Impairment Estimation in Banks Wibisono, Gunawan; Nikhlis, Neilin; Wicaksono, Yosep Aditya; Faradila, Silvia
Journal of Management and Informatics Vol. 4 No. 3 (2025): December Season | JMI: Journal of Management and Informatics
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jmi.v4i3.233

Abstract

Goodwill impairment assessment remains a judgment-intensive process in banking institutions, where managerial discretion, information asymmetry, and regulatory complexity often challenge the quality of decisions and transparency. While prior studies have widely applied machine learning to financial risk assessment and credit analytics, they have paid limited attention to its role in improving managerial accountability in goodwill impairment decisions. This study aims to address this gap by developing and evaluating a machine-learning–based estimation framework to enhance the quality of decisions and transparency in bank-level goodwill impairment assessments. Using simulation-based analysis on synthetic financial statements, the proposed framework evaluates the performance of impairment estimation using quantitative metrics that capture predictive accuracy, decision consistency, and traceability. The findings demonstrate that ML-assisted estimation can systematically improve decision quality while strengthening transparency and accountability compared to traditional judgment-driven approaches. Beyond technical performance, the results indicate that machine learning can function as a governance-supporting mechanism by enabling more traceable and internally auditable impairment decisions. The study contributes theoretically by operationalizing transparency and accountability as measurable decision outcomes in corporate finance, and practically by offering banks a simulation-based tool for internal evaluation that does not rely on field experiments or sensitive proprietary data. Overall, the research highlights the potential of ML-enabled decision support systems to enhance both the quality and governance of goodwill impairment practices in the banking sector.
People Analytics To Mitigate Managerial Decision Bias In Modern Organizations Setyawan, Rachmad; Faradila, Silvia
Jurnal Ilmiah Manajemen, Ekonomi dan Bisnis Vol. 3 No. 2 (2024): :Mei : Jurnal Ilmiah Manajemen, Ekonomi dan Bisnis
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/vdb28v29

Abstract

The increasing complexity of contemporary organizations creates greater possibilities for cognitive bias to affect executive decision processes. The People Analytics method provides organizations with a data-based solution that improves their ability to make unbiased decisions. The research study investigates how People Analytics technology helps reduce inaccuracies in managerial decision-making. The research uses a quantitative explanatory method to investigate middle and upper management personnel who use human resource information, using a survey. The researchers employed linear regression analysis to examine their data. The results of the study show that People Analytics usage leads to a significant reduction in executive decision-making biases, helping organizations make more unbiased and consistent decisions. The research study establishes its value through the combination of People Analytics and decision bias theory within a universal management framework. The research findings demonstrate that People Analytics serves as a strategic tool that enhances both the quality and accountability of executive decisions.
Co-Authors Gunawan Wibisono Nikhlis, Neilin Setyawan, Rachmad Wicaksono, Yosep Aditya