Genetic Algorithm-Based Feature Selection for Optimizing Credit Risk Algorithms

From the early start of the economy, credit risk has been study case for both researchers and financial experts. Financial credit scoring has been the most crucial process in the finance industry as it helps banks and financial institutions to make better decisions. The challenge arises in selecting the features that will make my decision better. This study proposes a feature selection approach using a genetic algorithm based on the information gain to improve classification performance. The genetic algorithm chooses feature subsets trough Logistic Regression. The validation is applied to the Home Credit Default Risk dataset. The experimental results demonstrates that the approach effectively selects features that improves the classification accuracy. The work suggests that evolutionary algorithms combined with mathematical measures can enhance credit risk prediction models and leads to better decision making in financial institutions.