Artificial Bee Colony for Logic Mining in Credit Scoring
Malaysian Journal of Fundamental and Applied Sciences
; 18(6):654-673, 2022.
Article
in English
| Scopus | ID: covidwho-2203537
ABSTRACT
During the SARS-CoV-2 (Covid-19) pandemic, credit applications skyrocketed unimaginably. Thus, creditors or financial entities were burdened with information overload to ensure they provided the proper credit to the right person. The existing methods employed by financial entities were prone to overfitting and did not provide any information regarding the behavior of the creditor. However, the outcome did not consider the attribute of the creditor that led to the default outcome. In this paper, a swarm intelligence-based algorithm named Artificial Bee Colony has been implemented to optimize the learning phase of the Hopfield Neural Network with 2 Satisfiability-based Reverse Analysis Methods. The proposed hybrid model will be used to extract logical information in the credit data with more than 80% accuracy compared to the existing method. The effectiveness of the proposed hybrid model was evaluated and showed superior results compared to other models. © 2022 Malaysian Journal of Fundamental and Applied Sciences.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
Malaysian Journal of Fundamental and Applied Sciences
Year:
2022
Document Type:
Article
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