Machine Learning-driven Group Ranking in Data Envelopment Analysis: Applications in the Banking Sector
محورهای موضوعی : Operation Research
Mohammad Sajjad Shahbazifar
1
,
Reza Kazemi Matin
2
,
Mohsen Khounsiavash
3
,
Fereshteh Koushki
4
1 - Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin,
2 - Department of Mathematics, Karaj Branch, Islamic Azad University, Karaj, Iran
3 - Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
4 - Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
کلید واژه: Group Efficiency, Banking Groups, Machine Learning, Neural Network, Data Envelopment Analysis, Ranking,
چکیده مقاله :
This paper explores the intersection of Group Ranking in Data Envelopment Analysis (DEA) and the potent capabilities of Machine Learning (ML) within the insurance sector, aiming to redefine group efficiency assessment. While DEA has been a cornerstone for evaluating Decision-Making Units (DMUs), the traditional models fall short in the nuanced insurance sector. To address these limitations, ML is integrated into DEA, enabling more effective DMU ranking. The study includes an empirical application within the banking industry, emphasizing the methodology's relevance and potential to transform the insurance landscape.
This paper explores the intersection of Group Ranking in Data Envelopment Analysis (DEA) and the potent capabilities of Machine Learning (ML) within the insurance sector, aiming to redefine group efficiency assessment. While DEA has been a cornerstone for evaluating Decision-Making Units (DMUs), the traditional models fall short in the nuanced insurance sector. To address these limitations, ML is integrated into DEA, enabling more effective DMU ranking. The study includes an empirical application within the banking industry, emphasizing the methodology's relevance and potential to transform the insurance landscape.
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