In financial risk, credit risk management is one of the most important issues in financial decisi... more In financial risk, credit risk management is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. Deep learning is a powerful classification tool which is currently an active research area and successfully solves classification problems in many domains. Deep Learning provides training stability, generalization, and scalability with big data. Deep Learning is quickly becoming the algorithm of choice for the highest predictive accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and improve the accuracy of classifiers. In this study, we constructed a credit scoring model based on deep learning and feature selection to evaluate the applicant's credit score from the applicant's input features. Two public datasets, Australia and German credit ones, have been used to test our method. The experimental results of the real world data showed that the proposed method results in a higher prediction rate than a baseline method for some certain datasets and also shows comparable and sometimes better performance than the feature selection methods widely used in credit scoring.
— Recent finance and debt crises have made credit risk management one of the most important issue... more — Recent finance and debt crises have made credit risk management one of the most important issues in financial research. Credit scoring is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. In this paper, we propose an effective credit scoring model based on feature selection approaches. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. Using the standard k-nearest-neighbors (kNN) rule as the classification algorithm, the feature selection methods are evaluated in classification tasks. Two well-known and readily available such as: Australia and German dataset has been used to test the algorithm. The results obtained by feature selection approaches shown have been superior to state-of-the-art classification algorithms in credit scoring.
In financial risk, credit risk management is one of the most important issues in financial decisi... more In financial risk, credit risk management is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. Deep learning is a powerful classification tool which is currently an active research area and successfully solves classification problems in many domains. Deep Learning provides training stability, generalization, and scalability with big data. Deep Learning is quickly becoming the algorithm of choice for the highest predictive accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and improve the accuracy of classifiers. In this study, we constructed a credit scoring model based on deep learning and feature selection to evaluate the applicant's credit score from the applicant's input features. Two public datasets, Australia and German credit ones, have been used to test our method. The experimental results of the real world data showed that the proposed method results in a higher prediction rate than a baseline method for some certain datasets and also shows comparable and sometimes better performance than the feature selection methods widely used in credit scoring.
— Recent finance and debt crises have made credit risk management one of the most important issue... more — Recent finance and debt crises have made credit risk management one of the most important issues in financial research. Credit scoring is one of the most important issues in financial decision-making. Reliable credit scoring models are crucial for financial agencies to evaluate credit applications and have been widely studied in the field of machine learning and statistics. In this paper, we propose an effective credit scoring model based on feature selection approaches. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. Using the standard k-nearest-neighbors (kNN) rule as the classification algorithm, the feature selection methods are evaluated in classification tasks. Two well-known and readily available such as: Australia and German dataset has been used to test the algorithm. The results obtained by feature selection approaches shown have been superior to state-of-the-art classification algorithms in credit scoring.
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Papers by Sang V Ha