Assessing customer creditworthiness is a key element of credit risk management in banking. Its quality determines a credit institution's financial stability, the level of overdue debt, and the effectiveness of its loan portfolio. In the context of the digital transformation of the banking sector, the use of information systems focused on automated credit assessment is becoming a strategic priority. Machine learning and statistical analysis algorithms, which determine the accuracy, speed, and reliability of credit decisions, occupy a central place in such systems. Traditional methods based on expert judgment and simplified scorecards are increasingly giving way to intelligent information systems integrating machine learning, big data analysis, and automated decision-making processes. This article presents a systematic review of approaches to credit assessment and an analysis of automated data processing tools for credit assessment – from classical statistical methods to advanced solutions based on deep learning and explainable artificial intelligence.
solvency, banking methods, scoring, credit risk, fundamental analysis, information system, machine learning, digital transformation, borrower assessment algorithms
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