تقييم المقاربات الكلاسيكية مقابل مقاربات لغة التعلّم الآلي (الذكاء الاصطناعي) في قياس شدة الضائقة المالية في المصارف أدلة (تقارير مالية) من مصرف الجمهورية ليب
DOI:
https://doi.org/10.65405/.v10i37.453Keywords:
Financial distress; Altman's standard score; Isolation Forest (machine learning); Anomaly detection; Jumhouria Bank; Libyan banking sector.Abstract
Economic resilience requires the soundness of financial health in the banking sector, as is the case with fragile economies like Libya. ALJUMHOURIA BANK, the country's largest state financier, has demonstrated chronic problems over the past decade. However, traditional models for distress prediction, such as Altman's Z-score, focus on a firm's chronic vulnerability but cannot identify severity when all cases fall in the distress zone. This paper discusses an analysis using a dual approach: Altman's Z-score and anomaly detection, Isolation Forest, to assess Jumhouria Bank's health from 2018 to 2022. Results indicate the occurrence of chronic stress in all years, with excess liquidity transiently disguising structural solvency and profitability weakness. Anomaly detection using the Isolation Forest estimated 2018 to be an anomalous year as a function of higher liquidity, while 2020 continued to present a distress scenario. In this regard, liquidity and profitability take precedence as the primal drivers of stress impelled by feature importance, supported by high non-performing loan ratios. The significant methodological contributions the research provides involve applying traditional and artificial intelligence approaches and offers considerable practical relevance for policy makers and regulators interested in enhancing early warning systems in fragile banking systems.
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