Open Access
American Research Journal of Computer Science and Information Technology
ISSN (Online): 2572-2921
DOI: 10.46568/arjcsit
Application of Machine Learning for Risk Prediction in Credit Institutions
Director of Cyber Data Risk & Resilience Division, China/Canada (Montreal, Quebec)
Di Feng, “Application of Machine Learning for Risk Prediction in Credit Institutions”, American Research
Journal of Computer Science and Information Technology, Vol 8, no. 1, 2025, pp. 12-17.
Abstract
The article examines modern financial systems, which are a multi-layered structure where data processing requires the
use of analytical methods. The relevance of the topic is due to the need to develop algorithms that take into account
the complexity of information and the volume of data. The main focus of the article is on the use of machine learning
algorithms for managing credit risks and assessing their adaptation to financial activities.
The article discusses approaches aimed at predicting risks in credit institutions. The emphasis is on models adapted to
the specifics of financial processes, taking into account atypical situations. This allows you to create tools that meet the
requirements of the industry.
The use of algorithms increases the accuracy of forecasting borrowers’ creditworthiness, detects fraud, and improves
loan portfolio management processes. Methods related to the identification of relationships between data characteristics
contribute to the development of more effective models. Such approaches demonstrate resilience to changing financial
market conditions.
The conclusions of the article are addressed to specialists involved in risk management, developers of analytical solutions,
and researchers in the field of economic applications of machine learning. The use of these tools helps to optimize the
internal processes of credit institutions and increase their efficiency in working with data.
The use of machine learning algorithms opens up prospects for accurate analysis of credit risks. These technologies
improve the quality of forecasting, strengthen the sustainability of organizations, and create conditions for successful
adaptation to changes in the financial environment.
