PERAN ANALISIS LAPORAN KEUANGAN SEBAGAI INSTRUMEN DETEKSI DINI RISIKO KEBANGKRUTAN PERUSAHAAN: A SYSTEMATIC LITERATURE REVIEW

Abstract

The increasingly dynamic and uncertain business environment has increased the risk of financial distress and corporate bankruptcy. Therefore, companies require effective early detection instruments to identify financial problems and take preventive actions. This study aims to analyze the role of financial statement analysis as an early warning system for detecting bankruptcy risk, identifying key financial indicators, and reviewing the development of bankruptcy prediction models. This study employs a Systematic Literature Review (SLR) approach based on the PRISMA 2020 guidelines by reviewing scientific articles published during 2021-2025. The collected data were analyzed using content analysis with a qualitative descriptive approach to identify trends, indicators, and prediction models related to bankruptcy risk. The results show that profitability, liquidity, solvency, activity, and cash flow ratios are important indicators in detecting financial distress. The Altman Z-Score model is the most frequently used prediction model, although its effectiveness depends on company characteristics, economic conditions, and financial reporting quality. Recent studies also indicate the increasing use of machine learning, artificial intelligence, and big data analytics to improve prediction accuracy. This study contributes to the development of bankruptcy prediction literature and provides practical insights for managers, investors, and stakeholders in making informed decisions. Future research is suggested to integrate financial and non-financial indicators and expand studies to digital companies, SMEs, and creative economy sectors.