Digital Credit Risk Assessment through Predictive Analytics and Machine Learning Models
Keywords:
Digital credit risk assessment, predictive analytics, machine learning, credit scoring, loan default prediction, financial technology, creditworthiness, risk management, logistic regression, random forest, gradient boosting, explainable artificial intelligenceAbstract
Digital credit risk assessment has become an essential component of modern financial services as banks, fintech companies, and digital lending platforms increasingly rely on data-driven techniques to evaluate borrower creditworthiness. Traditional credit assessment methods often depend on predefined scoring rules, historical repayment records, and conventional statistical models, which may not adequately capture complex relationships among borrower characteristics, financial behavior, and changing economic conditions. This study proposes a predictive analytics framework that integrates machine learning models to improve the accuracy, efficiency, and reliability of digital credit risk assessment. The proposed approach involves collecting relevant financial and demographic data, preprocessing datasets, engineering predictive features, training classification models, and evaluating their performance using established statistical metrics. Logistic regression, decision trees, random forests, gradient boosting, and support vector machines are considered candidate models for predicting loan default risk. Model performance is assessed using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and calibration measures. Particular attention is given to class imbalance, model interpretability, data privacy, and fairness in lending decisions. Comparative evaluation is expected to identify models that balance predictive performance with transparency and operational efficiency. The proposed framework aims to support more consistent credit decisions, improve early identification of potentially delinquent borrowers, and strengthen portfolio risk management. The study also emphasizes that machine learning predictions should complement responsible lending policies, human oversight, and regulatory compliance rather than replace them entirely
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