Яшил иқтисодиёт ва тараққиёт 4-том 7-сан (2026) · 26-32-беттер

COMPARATIVE ANALYSIS OF DIGITAL FINANCIAL TECHNOLOGIES FOR CREDIT RISK PREDICTION AND MANAGEMENT IN COMMERCIAL BANKS BASED ON ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYTICS

Kholdorov, Sardor

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Аннотация

This article examines modern digital approaches to credit risk prediction and management incommercial banks based on artificial intelligence and big data analytics. The effectiveness of machine learningmodels such as XGBoost, Random Forest, and neural networks is analyzed in comparison with traditionalstatistical models, particularly logistic regression. Based on real statistical data, it is demonstrated that theaccuracy of the models increased by 25%, while the default rate decreased by 20–30%.

artificial intelligence, big data, credit risk, machine learning, XGBoost, digital financial technologies, predictive modeling.

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Цитата алуу

APA 7 
Kholdorov, Sardor (2026). COMPARATIVE ANALYSIS OF DIGITAL FINANCIAL TECHNOLOGIES FOR CREDIT RISK PREDICTION AND MANAGEMENT IN COMMERCIAL BANKS BASED ON ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYTICS. Яшил иқтисодиёт ва тараққиёт, 4(7), 26-32.
GOST R 7.0.5 
Kholdorov, Sardor COMPARATIVE ANALYSIS OF DIGITAL FINANCIAL TECHNOLOGIES FOR CREDIT RISK PREDICTION AND MANAGEMENT IN COMMERCIAL BANKS BASED ON ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYTICS // Яшил иқтисодиёт ва тараққиёт. 2026. Т. 4. № 7. С. 26-32.
BibTeX 
@article{sardor2026,
  author  = {Kholdorov, Sardor},
  title   = {COMPARATIVE ANALYSIS OF DIGITAL FINANCIAL TECHNOLOGIES FOR CREDIT RISK PREDICTION AND MANAGEMENT IN COMMERCIAL BANKS BASED ON ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYTICS},
  journal = {Яшил иқтисодиёт ва тараққиёт},
  year    = {2026},
  volume  = {4},
  number  = {7},
  pages   = {26-32}
}
RIS 
TY  - JOUR
AU  - Kholdorov, Sardor
TI  - COMPARATIVE ANALYSIS OF DIGITAL FINANCIAL TECHNOLOGIES FOR CREDIT RISK PREDICTION AND MANAGEMENT IN COMMERCIAL BANKS BASED ON ARTIFICIAL INTELLIGENCE AND BIG DATA ANALYTICS
JO  - Яшил иқтисодиёт ва тараққиёт
PY  - 2026
VL  - 4
IS  - 7
SP  - 26
EP  - 32
ER  -