The Village Credit Institution (LPD) is a microfinance institution that plays a vital role in the rural economy in Bali. LPDs provide credit through a manual and subjective analysis process conducted by loan officers. This often hinders the objectivity and consistency of credit analysis. Modernization is a strategic step to address this issue. This study aims to analyze the digital transformation process in the LPD credit analysis system through the implementation of machine learning. Initial analysis indicates that debtors with long tenors, high delinquency rates, and high debt-to-income ratios have a higher risk of default. This pattern serves as the basis for learning a machine learning model using Logistic Regression, Decision Tree, Random Forest, and XGBoost algorithms to classify creditworthiness. The XGBoost algorithm demonstrated the best performance with an accuracy of 93% and an AUC of 0.96. Regarding credit approval, this model was able to identify high-risk potential debtors with significantly better accuracy than conventional methods. With the ability to process thousands of historical data points in a relatively short time, thereby accelerating decision-making, the application of machine learning significantly improves the efficiency and objectivity of credit analysis. This supports the realization of digital transformation in credit analysis at LPDs.