KORXONALARNING BANKROTLIK EHTIMOLINI SUN’IY INTELLEKT YORDAMIDA PROGNOZLASH

Ismoil Zaynutdinov

GREEN ECONOMY AND DEVELOPMENT, 2025, vol. 3, issue 11

Abstract: Ushbu maqolada korxonalarning bankrotlik ehtimolini aniqlash va erta ogohlantirish tizimini shakllantirishdasun’iy intellekt (AI) hamda mashinaviy o‘qitish (ML) algoritmlarining qo‘llanishi chuqur tahlil qilinadi. An’anaviy yondashuvlar— Altman Z-score, Ohlson logit modeli va Beaver uslublarining cheklovlari ko‘rsatilib, zamonaviy AI modellarining ustunjihatlari asoslab berilgan. Tadqiqot metodologiyasi doirasida Random Forest, XGBoost, Logistic Regression va LSTMneyron tarmoqlari tanlanib, ularning samaradorligi Accuracy, Recall, Precision, F1-score va ROC-AUC mezonlari orqalibaholangan. Empirik natijalar XGBoost va LSTM modellarining bankrotlik ehtimolini prognozlashda eng yuqori aniqlikkaega ekanini ko‘rsatdi. Tadqiqot yakunida AI asosida erta ogohlantirish tizimini yaratishning ilmiy-amaliy imkoniyatlariishlab chiqildi hamda korxonalar moliyaviy barqarorligini oshirish uchun amaliy tavsiyalar ilgari surildi.

Keywords: sun’iy intellekt; bankrotlik ehtimoli; prognozlash; Machine Learning; LSTM; XGBoost; moliyaviy barqarorlik; erta ogohlantirish tizimi (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:teu:ged000:v:3:y:2025:i:11:id:7823

DOI: 10.5281/zenodo.17596186

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