Enhancing Credit Score Analysis: A Novel Approach with Random Forest and Kernel SVM
| International Journal of Electronics and Communication Engineering |
| © 2023 by SSRG - IJECE Journal |
| Volume 10 Issue 11 |
| Year of Publication : 2023 |
| Authors : Annie Chacko, D. John Aravindhar, A. Antonidoss |
10.14445/23488549/IJECE-V10I11P105
How to Cite?
Annie Chacko, D. John Aravindhar, A. Antonidoss, "Enhancing Credit Score Analysis: A Novel Approach with Random Forest and Kernel SVM," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 11, pp. 45-54, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I11P105
Abstract:
Credit score analysis systematically evaluates an individual or entity’s financial history and behaviour to determine their creditworthiness. Traditional methods for credit score analysis have several challenges, such as privacy concerns, lack of flexibility, vulnerability to identity theft, limited data, and real-time analysis. To overcome these complexities, this paper proposes a novel method combining the advantages of Random Forest and kernel Support Vector Machine (SVM). The proposed method has three phases: data preprocessing, feature extraction, and classification. In the preprocessing phase, the proposed method eliminates the noise and errors from the raw data based on obtaining quality input for the analysis. In this study, Random Forest is utilized to extract the most significant features based on the domain and credit data analysis also, kernel SVM is employed for classification by analyzing the components and their impact on credit scoring. Also, the study conducted experiments on the German Credit Risk dataset. The performance evaluation of the proposed method involves analyzing its effectiveness based on evaluation metrics and comparing its performance with existing methods. The experimental results depict that the proposed method obtained better outcomes and achieved high efficiency for credit score analysis.
Keywords:
Credit score analysis, Random Forest, Kernel Support Vector Machine, German Credit Risk dataset, Feature extraction, Classification.
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