USING MACHINE LEARNING IN BANKS TO FORECAST LOAN DEFAULTS: PART 2

Berdiyorov Bekzod Shoymardonovich, Berdiyorov Jahongir Shaymardon o’g’li

Актуар молия ва бухгалтерия ҳисоби · 2026-yil

Annotatsiya

For most traditional banks, lending activity remains the major source of revenue. However, there is always a possibility that some borrowers default on their loans and this situation creates credit risk for banks. To manage this risk, banks utilize different qualitative and quantitative methods to predict loan defaults. Currently, traditional algorithms such as logistic regression and decision trees are among the most popular models used to predict loan defaults. Their simple implementation and high accuracy make them preferred choice among credit analysts. However, rapid technological progress and increased computing capabilities of computers are creating opportunities to apply more advanced machine learning algorithms for managing credit risk. For instance, deep neural networks and recently developed Extreme Gradient Boosting algorithms (XGB) have been shown to exhibit high accuracy in wide range of classification tasks and have potential to exhibit high accuracy in loan default prediction.

Maqola ma’lumotlari
MualliflarBerdiyorov Bekzod Shoymardonovich, Berdiyorov Jahongir Shaymardon o’g’li
JurnalАктуар молия ва бухгалтерия ҳисоби
Nashr sanasi2026-03-18
Jild6
Son03
Betlar1-12
TilIngliz
DOI10.55439/afa/vol6_iss03/1331

DOI: 10.55439/afa/vol6_iss03/1331 · Maqolaning asl sahifasi

Kalit so‘zlar

banks, lending, loan defaults, machine learning, deep neural networks, Extreme Gradient Boosting algorithms.

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