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Generating credit scores is a data mining process. Credit scores represent the creditworthiness of an individual. Lenders, such as banks, credit insurance companies, and consumer finance companies use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt; therefore, a reliable credit score model is essential. The credit score model is used to analyze multiple parameters that are collected through various channels and to determine who qualifies for a loan, at what interest rate, and what credit limits. This project explores details of credit score models including "Weight of Evidence" and "Information Value" which determines the levels of importance of each parameter, and the mathematics of logistic regression analysis, which yields a probability of the consumer being qualified or unqualified. Additionally, this project explores potential ways to improve the performance of the credit score model.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Fang, Jintao, "The Credit Scorecard Model" (2019). Senior Projects Spring 2019. 44.
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