Madaminov Bekzod Allayarovich

BANK RISK-MENEJMENTI TIZIMIDA SUNʼIY INTELLEKT

TEXNOLOGIYALARIDAN FOYDALANISH

Maʼmun universiteti

Magistratura boʻlimi boshligʻi

Fizika-matematika fanlari boʻyicha falsafa doktori

PhD, v.b. professor

ORCID: 0009-0002-2806-691X

Annotatsiya

Ushbu maqolada tijorat banklarining moliyaviy samaradorligi va kapital

rentabelligiga taʼsir etuvchi asosiy omillar ekonometrik yondashuv asosida tahlil

qilinadi hamda bank risk-menejmenti tizimini takomillashtirishda sunʼiy intellekt

texnologiyalarini qoʻllash istiqbollari asoslab beriladi. Tadqiqotda natijaviy

koʻrsatkich sifatida ROE olinib, unga raqamli bank operatsiyalari hajmi, mamlakat

YaIM, raqamli xizmatlardan foydalanuvchilar soni va muammoli kreditlar darajasining

taʼsiri koʻp omilli logarifmik regressiya modeli orqali baholandi. Empirik natijalar

raqamlashtirish jarayonlari va makroiqtisodiy oʻsish bank rentabelligiga ijobiy,

muammoli kreditlar esa salbiy taʼsir koʻrsatishini tasdiqladi. Model diagnostikasi

qoldiqlarning normal taqsimlanganini koʻrsatib, baholashlarning statistik

ishonchliligini taʼminladi. Shuningdek, kredit risklarini kamaytirish, defolt ehtimolini

erta aniqlash va operatsion samaradorlikni oshirishda mashinaviy oʻqitish, neyron

tarmoqlar va “Big Data” tahliliga asoslangan sunʼiy intellekt usullarining afzalliklari

yoritildi. Tadqiqot natijalari banklarda innovatsion risk-menejment mexanizmlarini

joriy etish moliyaviy barqarorlik va raqobatbardoshlikni kuchaytirishini koʻrsatadi.

Kalit soʻzlar: moliyaviy barqarorlik, tijorat banki, banklarida risklarni

boshqarish, risk menejment, sunʼiy intellekt usullari.

Abstract

This article analyzes the main factors affecting the financial efficiency and return

on capital of commercial banks based on an econometric approach and substantiates

the prospects for the use of artificial intelligence technologies in improving the bankʼs

risk management system. The study took ROE as the outcome indicator, and the impact

of the volume of digital banking operations, the countryʼs GDP, the number of users

of digital services, and the level of problem loans on it was estimated using a

multifactor logarithmic regression model. The empirical results confirmed that

digitalization processes and macroeconomic growth have a positive effect on bank

profitability, while problem loans have a negative effect. Model diagnostics showed a

normal distribution of balances, ensuring statistical reliability of the assessments. It

also highlighted the advantages of artificial intelligence methods based on machine

learning, neural networks, and "big data" analysis in reducing credit risks, early

detection of the probability of default, and increasing operational efficiency. The

results of the study show that the introduction of innovative risk management

mechanisms in banks enhances financial stability and competitiveness.

Keywords: financial stability, commercial banking, risk management in banks,

risk management, artificial intelligence methods.

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