| Autor: |
Maulida, Risma, Rusgiyono, Agus, Widiharih, Tatik |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2738 Issue 1, p1-8, 8p |
| Abstrakt: |
Survival analysis provides us with tools to model the time of death or failure of organisms. The concept of survival analysis can be successfully used in many different situations. This paper presents parametric survival analysis models, particularly the accelerated failure time model with Weibull distributed survival time. Next, we investigate the application of this model in analyzing credit scoring using client credit data, such as credit payment status, loan amount, interest and loan type, and client background. Credit scoring is a tool to predict the financial risk of loan clients and assist banks in analyzing new loans to be given to clients, which helps strengthen the credit risk management system. This methodology is applied to a personal loan dataset provided by credit bank in Indonesia. Accelerated Failure Time (AFT) is an alternative to parametric survival. The explanatory variable acts as an acceleration factor to accelerate or slow down the survival process compared to the baseline survival function. Consequently, it was found that the AFT model measures the direct effect of the explanatory variable on survival time, not hazard. The model meets the criteria of suitable or ideal so that the results can select clients who will receive credit. The Mean Absolute Percentage Error (MAPE) criterion compares the actual results with the predicted results to obtain the best model. This method also analyzes the factors that contribute to AFT using information from clients' credit applications as well as information provided in their credit reports. The prediction results are then assessed for accuracy with MAPE is 18.035%, this value means 81.965% of the model's good criteria to predict the client's default time. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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