An Enhanced Credit Risk Classification Framework Using Hybrid Genetic Algorithm and Machine Learning Models on the German Credit Dataset

The evaluation of credit risk remains an important task in the financial sector, requiring high precision and interpretability in order to neutralize default risks. This work presents a comprehensive and hybrid classification model to forecast credit risk on the German credit dataset. The model proposed is a combination of traditional machine learning classifiers, i.e., the Decision Tree (C4.5) and Support Vector Machine with an evolutionary Hybrid Genetic Algorithm as a feature selector, thus enhancing classification performance. Besides single models, we utilize ensemble learning approaches, i.e., bagging, with and without feature selection, to investigate performance on varying iterations of bagging. Experiments are performed on both quantitative and qualitative forms of the dataset, split into five train-test ratios (60:40, 70:30, 75:25, 80:20, 85:15), in order to examine the stability and generalization ability of the models. The Hybrid Genetic Algorithm-based proposed classifiers minimize misclassification errors more effectively, especially when the training set is small, while, concurrently, obtaining low Type I and Type II error rates. Ensemble models with feature selection (Feature Selection-Hybrid Bagging) also outperformed the performance of conventional bagging models in all partitions of the data consistently. The evaluation metrics like Accuracy, Precision, Recall, F1-score, Area Under the Receiver Operating Characteristic Curve, and Log Loss confirm the efficacy of this hybrid approach. The results confirm that the combination of genetic algorithms for feature optimization and conventional classifiers leads to a strong and interpretable solution to credit risk classification problems. The hybrid model is a reliable decision-support system for financial institutions aiming to automate and improve credit scoring procedures.