KORXONALARNING BANKROTLIK EHTIMOLINI SUN’IY INTELLEKT YORDAMIDA PROGNOZLASH
DOI:
https://doi.org/10.5281/zenodo.17596186Ключевые слова:
sun’iy intellekt, bankrotlik ehtimoli, prognozlash, Machine Learning, LSTM, XGBoost, moliyaviy barqarorlik, erta ogohlantirish tizimiАннотация
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.
Библиографические ссылки
1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of
Finance, 23(4), 589–609.
2. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research,
18(1), 109–131.
3. Zhang, L., & Li, W. (2022). AI-based bankruptcy prediction models: Evidence from emerging economies. Journal of
Applied Finance and Banking, 12(3), 75–89.
4. Samariddinovich, Z. I. (2023). Ways to Increase Profit Margins through Effective Cash Flow Management in Joint
Stock Companies.
5. Samariddinovich, Z. I. (2021). Analysis of the Effectiveness of Receivables Management. American Journal of
Economics and Business Management, 4(7), 86-92.
6. Зайнутдинов, И. (2021). Молиявий активларни бошқаришнинг йўллари. Экономика и инновационные технологии,
(4), 68-72.
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