Effectiveness of Machine Learning Algorithms in Credit Risk Detection

Credit risk detection is one of the most significant tasks in the banking and financial sector because inaccurate assessment of borrowers may lead to substantial financial losses and instability in financial institutions. Traditional statistical approaches such as logistic regression have been widely used in credit scoring systems for decades. However, the growth of digital banking, large-scale financial datasets, and computational technologies has encouraged the adoption of machine learning algorithms for more accurate prediction of default risk. This article analyzes the effectiveness of major machine learning algorithms in credit risk detection, including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, Artificial Neural Networks, and XGBoost. The study is based on recent scientific literature, comparative experimental findings, and empirical evidence from financial datasets. Research findings demonstrate that ensemble learning methods such as Random Forest and XGBoost generally outperform traditional statistical models in predictive accuracy, Area Under Curve (AUC), recall, and precision metrics. At the same time, issues related to interpretability, fairness, regulatory compliance, and imbalanced datasets remain important challenges in practical implementation. The paper also discusses the significance of explainable artificial intelligence (XAI) methods in improving transparency in machine learning-based credit scoring systems. The study concludes that machine learning algorithms significantly improve credit risk detection performance when combined with appropriate preprocessing techniques, feature engineering, and interpretability frameworks.