Artificial bee colony for logic mining in credit scoring
- Siti Zulaikha Mohd. Jamaludin, Nur Syazwani Sa’ari, Mohd. Shareduwan Mohd. Kasihmuddin, Muhammad Fadhil Marsani, Nur Ezlin Zamri, Siti Aishah Azhar, Yueling, Guo, Mohd. Asyraf Mansor
- Creator: Siti Zulaikha Mohd. Jamaludin , Nur Syazwani Sa’ari , Mohd. Shareduwan Mohd. Kasihmuddin , Muhammad Fadhil Marsani , Nur Ezlin Zamri , Siti Aishah Azhar , Yueling, Guo , Mohd. Asyraf Mansor
- Date: 2022-12
- Language: ENG
- Type: Journal Article
- Identifier: vital:156177 , ISSN 1823-626X , valet-20240227-150521
- Full Text: false
- Description: 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.
- Creator: Siti Zulaikha Mohd. Jamaludin , Nur Syazwani Sa’ari , Mohd. Shareduwan Mohd. Kasihmuddin , Muhammad Fadhil Marsani , Nur Ezlin Zamri , Siti Aishah Azhar , Yueling, Guo , Mohd. Asyraf Mansor
- Date: 2022-12
- Language: ENG
- Type: Journal Article
- Identifier: vital:156177 , ISSN 1823-626X , valet-20240227-150521
- Full Text: false
- Description: 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.
Non-stationary in extreme share return: World indices application
- Creator: Muhammad Fadhil Marsani
- Date: 2020
- Subjects: Extreme Share Returns
- Language: ENG
- Type: Indexed Paper
- Identifier: vital:141399 , valet-20210621-095011
- Full Text: false
- Creator: Muhammad Fadhil Marsani
- Date: 2020
- Subjects: Extreme Share Returns
- Language: ENG
- Type: Indexed Paper
- Identifier: vital:141399 , valet-20210621-095011
- Full Text: false
Examine generalized lambda distribution fitting performance : an application to extreme share return in Malaysia
- Muhammad Fadhil Marsani, Ani Shabri, Nur Amalina Mat Jan
- Creator: Muhammad Fadhil Marsani , Ani Shabri , Nur Amalina Mat Jan
- Date: 2017
- Subjects: Science
- Language: ENG
- Type: Indexed Paper
- Identifier: vital:119911 , valet-20181007-152428
- Full Text: false
- Description: cited 0
- Creator: Muhammad Fadhil Marsani , Ani Shabri , Nur Amalina Mat Jan
- Date: 2017
- Subjects: Science
- Language: ENG
- Type: Indexed Paper
- Identifier: vital:119911 , valet-20181007-152428
- Full Text: false
- Description: cited 0
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