KORXONALARNING BANKROTLIK EHTIMOLINI SUN’IY INTELLEKT YORDAMIDA PROGNOZLASH
DOI:
https://doi.org/10.5281/zenodo.17596186Keywords:
sun’iy intellekt, bankrotlik ehtimoli, prognozlash, Machine Learning, LSTM, XGBoost, moliyaviy barqarorlik, erta ogohlantirish tizimiAbstract
Ushbu maqolada korxonalarning bankrotlik ehtimolini aniqlash va erta ogohlantirish tizimini shakllantirishda
sun’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 ustun
jihatlari asoslab berilgan. Tadqiqot metodologiyasi doirasida Random Forest, XGBoost, Logistic Regression va LSTM
neyron tarmoqlari tanlanib, ularning samaradorligi Accuracy, Recall, Precision, F1-score va ROC-AUC mezonlari orqali
baholangan. Empirik natijalar XGBoost va LSTM modellarining bankrotlik ehtimolini prognozlashda eng yuqori aniqlikka
ega ekanini ko‘rsatdi. Tadqiqot yakunida AI asosida erta ogohlantirish tizimini yaratishning ilmiy-amaliy imkoniyatlari
ishlab chiqildi hamda korxonalar moliyaviy barqarorligini oshirish uchun amaliy tavsiyalar ilgari surildi.
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