O‘ZBEKISTONDA ALTERNATIV MA’LUMOTLAR ASOSIDA KREDIT RISKINI BAHOLASHNING ZAMONAVIY MODELLARI
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https://doi.org/10.5281/zenodo.20258235##semicolon##
alternativ ma’lumotlar, kredit riski, kredit scoring, sun’iy intellekt, Machine Learning, Logistic Regression, XGBoost, FinTech, raqamli bank xizmatlari, kredit portfeli, risk-menejment, moliyaviy inkluzivlik, KATM, Open Banking##article.abstract##
Mazkur maqolada Oʻzbekistonda alternativ ma’lumotlar asosida kredit riskini baholashning zamonaviy
modellari va ularning bank-moliya tizimidagi amaliy ahamiyati tahlil qilingan. Tadqiqotda an’anaviy kredit scoring
tizimlarining cheklovlari, ayniqsa, moliyaviy tarixi yetarli bo‘lmagan jismoniy shaxslar hamda kichik biznes subyektlarini
kreditlashdagi muammolari ilmiy jihatdan asoslab berilgan. Shuningdek, mobil aloqa operatorlari ma’lumotlari, kommunal
to‘lovlar tarixi, elektron tijorat tranzaksiyalari, raqamli to‘lov platformalari va mijozlarning xulq-atvoriga oid ma’lumotlardan
foydalanish orqali kredit riskini aniqlash imkoniyatlari o‘rganilgan. Tadqiqot davomida Logistic Regression, Random
Forest, Gradient Boosting, XGBoost va sun’iy neyron tarmoqlar kabi zamonaviy Machine Learning modellarining kredit
riskini prognozlashdagi samaradorligi tahlil qilingan. Oʻzbekiston Respublikasi Markaziy banki, KATM va tijorat banklari
faoliyatidagi raqamlashtirish jarayonlari hamda FinTech infratuzilmasining rivojlanishi alternativ scoring tizimlarini joriy
etish uchun muhim institutsional asos bo‘lib xizmat qilayotgani aniqlangan. Tadqiqot natijasida Oʻzbekiston bank sektorida
alternativ ma’lumotlarga asoslangan adaptiv kredit scoring modelini joriy etish kredit portfeli sifatini oshirish, problemali
kreditlar ulushini kamaytirish va moliyaviy inkluzivlikni kengaytirishga xizmat qilishi asoslab berilgan
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