This article investigates the methodological aspects of improving credit calculation in digital banks. The study developed and tested a credit scoring model based on the gradient boosting algorithm. The results indicate that alternative data — including clients’ digital activity and transaction patterns — is more effective in identifying credit risk than traditional financial indicators. The model’s high accuracy enables optimization of credit policies, expansion of financial inclusion, and early risk detection in digital banks. The study’s methodology and findings hold both practical and theoretical significance, providing a basis for the implementation of innovative approaches in banking.
| Mualliflar | Yusupov Otayor Otabekovich |
|---|---|
| Jurnal | «Инновацион иқтисодиёт» журнали |
| Nashr sanasi | 2026-07-02 |
| Jild | 50 |
| Son | 7 |
| Til | Ingliz |
Digital bank, credit scoring, machine learning, gradient boosting, alternative data, credit risk assessment, financial inclusion, Цифровой банк, кредитный скоринг, машинное обучение, градиентный бустинг, альтернативные данные, определение кредитного риска, финансовая инклюзия, Raqamli bank, kredit skoring, mashinaviy o‘qitish, gradient boosting, alternativ ma’lumotlar, kredit riskini aniqlash, moliyaviy inklyuziya
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