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Jo’rayev Bekzod Bahodir ugli
10.5281/zenodo.22814465
Annotatsiya
This paper examines how data-driven risk management — machine-learning credit scoring, predictive analytics, early warning systems and real-time monitoring — affects the profitability and operating efficiency of commercial banks. Rather than treating digital technology as a cost-cutting device, the paper models it as a change in the bank’s information set: sharper discrimination between good and bad borrowers alters approval, pricing, monitoring and provisioning decisions at the same time, and the financial effect is the sum of these channels. A framework linking statistical model quality to risk-adjusted financial outcomes is developed, five hypotheses are formulated, and a two-level research design combining loan-level model comparison with bank-level panel analysis is proposed. The discussion draws on international fintech experience and on the conditions of an emerging banking market with thin credit histories, where the marginal value of alternative data is likely to be highest.
Kalit so'zlar:
credit scoring, machine learning, predictive analytics, early warning systems, risk-adjusted profitability,
cost-to-income ratio, model risk, fintech, digital banking.
Foydalanilgan adabiyotlar
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