Improving Credit Scorecard Modeling Through Applying Text Analysis

In the credit card scoring and loans management, the prediction of the applicant's future behavior is an important decision support tool and a key factor in reducing the risk of Loan Default. A lot of data mining and classification approaches have been developed for the credit scoring purpose. For the best of our knowledge, building a credit scorecard by analyzing the textual data in the application form has not been explored so far. This paper proposes a comprehensive credit scorecard model technique that improves credit scorecard modeling though employing textual data analysis. This study uses a sample of loan application forms of a financial institution providing loan services in Yemen, which represents a real-world situation of the credit scoring and loan management. The sample contains a set of Arabic textual data attributes defining the applicants. The credit scoring model based on the text mining pre-processing and logistic regression techniques is proposed and evaluated through a comparison with a group of credit scorecard modeling techniques that use only the numeric attributes in the application form. The results show that adding the textual attributes analysis achieves higher classification effectiveness and outperforms the other traditional numerical data analysis techniques. Keywords—Credit Scoring; Textual Data Analysis; Logistic Regression; Loan Default. circumstances. Group of the existing credit scoring models will be discussed in section II. The data sources used in making a credit scoring decisions includes both the applicant form details and the information collected by a credit reference agency like the public registries, internal records in the bank from previous experiences, trans- actions, and any other activities initiated by the applicant in the bank (13). Beside all the work that has been done in this area, there is a drawback point related to the structure of the data used to build the statistical model. This limitation was a result of using only that data that could be represented as numeric values and neglecting the textual data regardless of its importance in purpose of simplifying the analysis calculations and the shape of the output of the model. This limitation results in decreasing the degree of efficiency of those models by neglecting some of the available valuable data that could be used to extract some features to increase the percentage of accuracy and correctness of the classification model. In this study, we aim to improve the existing credit scorecard modeling by employing the textual data analysis. The textual analysis results in extracting a group of textual features inserted to the credit scorecard to increase its accu- racy depending on statistical aspects. The resulting scorecard from the proposed methodology is compared with a group of scorecards generated from using only the numerical data analysis. Logistic Regression, Decision Tree, SVM, and Neural Network techniques are used in the comparison using a sample of 180 loan application forms collected from a financial institution providing loan services in Yemen. The results of the comparison using Error Rate, Recall, Precision, and F1- score show the improvement in the credit scorecard accuracy when employing the textual data analysis.