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SMOTE vs. ADASYN: An Analysis of Data Balancing Techniques to Enhance Machine Learning-Based Bank Loans Standard Hazard Forecasts
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Author(s): Sabyasachi Pramanik (Haldia Institute of Technology, India), Soma Bag (Asadtala Nivedita Kanya Vidya Math, India), Atanu Roy (The Neotia University, India), Ramkrishna Ghosh (Haldia Institute of Technology, India)and Pranati Rakshit (JIS College of Engineering, India)
Copyright: 2025
Pages: 18
Source title:
Reshaping the Economy With AI
Source Author(s)/Editor(s): Fernando Ortiz-Rodriguez (Tamaulipas Autonomous University, Mexico), Yuridia Mendoza (Tamaulipas Autonomous University, Mexico), Vicente Villanueva (Tamaulipas Autonomous University, Mexico)and Shwetambari Chiwhane (Symbiosis International University, India)
DOI: 10.4018/979-8-3693-8714-6.ch004
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Abstract
Significant technical progress has led to an expansion in human requirements. As a result, the banking sector has seen a rise in the quantity of loan approval requests. When choosing a candidate for loan approval, a number of factors are taken into account to determine the loan's status. Evaluating loan applications and reducing the risks associated with potential borrower defaults provide significant problems for banks. The need for banks to carefully evaluate each borrower's loan eligibility makes this procedure very onerous. The dataset balancing will be done first. Given the importance of the job, the current research used two oversampling techniques—SMOTE and ADASYN—for comparative analysis and balanced the datasets as a first step. The goal of the study was to use algorithms like support vector machines (SVM) and logistic regression to analyse loan approval data and determine the best balancing approach.
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