Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Citation - Scopus: 2Alternative Data Sources and Psychometric Scales Supported Credit Scoring Models(IEEE, 2023) Şahin, Türkay; Çakar, Tuna; Çakar, Tuna; Özvural, Özden Gebizlioğlu; Nicat, Şahin; Gebizlioǧlu Özvural, Özden; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study aims to evaluate individuals with limited access to banking services and enhance credit scoring models with alternative data sources. A psychometric-based credit scoring model was developed and tested. Despite limited data, significant potential findings were obtained. However, clarification of the distinction between credit payment intention and ability and validation of the results with more data are necessary.Conference Object Citation - Scopus: 1Modeling Consumer Creditworthiness Via Psychometric Scale and Machine Learning(IEEE, 2022) Çakar, Tuna; Çakar, Tuna; Sayar, Alperen; Sahin, Türkay; Bozkan, Tunahan; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityAlthough 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.