Modeling Consumer Creditworthiness via Psychometric Scale and Machine Learning
Citation
Sahin, T., Cakar, T., Bozkan, T., Ertugrul, S., & Sayar, A. (2022). Modeling Consumer Creditworthiness via Psychometric Scale and Machine Learning. 2022 7th International Conference on Computer Science and Engineering (UBMK). https://doi.org/10.1109/ubmk55850.2022.9919596Abstract
Although the predictive power of economic metrics to detect the creditworthiness of the customers is high, there is a rising interest in the integration of cognitive, psychological, behavioral, alternative, and demographic data into credit risk systems and processing the data through modern methods. The primary motivation for the rising interest is increased customer classification accuracy. In this research, customer creditworthiness was modeled through data consisting of personality, money attitudes, impulsivity, self-esteem, self-control, and material values and processed through artificial intelligence. The obtained findings have been evaluated as a reference point for the following research. © 2022 IEEE.
