PENERAPAN MANAJEMEN RISIKO PADA DATA SIMULASI PERBANKAN MENGGUNAKAN METODE LOGISTIC REGRESSION
Keywords:
Credit Risk, Logistic Regression, Banking, Classification, Simulation Data.Abstract
Credit risk is one of the most significant risks affecting banking financial stability. Debtors' inability to meet payment obligations can increase non-performing loans and impact the health of bank assets. Therefore, an analytical approach capable of predicting potential defaults more accurately and measurably is needed. This study aims to apply a logistic regression model to classify credit risk using banking simulation data. The model was constructed by considering customer characteristic variables suspected of influencing payment smoothness status. The analysis results indicate that logistic regression is able to identify significant variables in determining risk status and quantitatively provide the probability of default. Furthermore, the model demonstrates a good level of accuracy in distinguishing at-risk and non-risk customers. These findings suggest that logistic regression can be an effective decision support tool in the creditworthiness assessment process and strengthen banking risk management.
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