DESIGNING A SMART MODEL FOR GRANTING BANK FACILITIES BASED ON BIG DATA
DOI:
https://doi.org/10.29121/ShodhPrabandhan.v3.i1.2026.90Keywords:
Smart Model, Granting Bank Facilities, Big Data, Machine Learning, Hierarchical AnalysisAbstract
Granting bank facilities and identifying customers for facility provision has always been considered important, as it leads to greater assurance for banks in this regard. With the advent of the digital age and big data, the large volume of data helps banks gain better insights into decisions regarding facility provision and utilize Smart approaches. Therefore, in this research, a Smart model for granting bank facilities based on big data was designed. For this purpose, customers' financial and credit histories from internal bank systems were used, and machine learning, clustering, and regression analysis models were employed for data analysis. Using K-means clustering, customer groups were categorized into three risk levels: low-risk, medium-risk, and high-risk. Then, a random forest model was used to predict credit risk, which showed 96% accuracy. Subsequently, a combination of hierarchical analysis and particle swarm optimization algorithm was utilized to optimize the facility allocation process, and the results indicated a reduction in risk and an increase in bank productivity. Finally, the Smart model for granting bank facilities can help identify better customers for facility allocation and also reduce the risk of facility provision by banks.
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