(untitled) (Comprehensive Journal of Science, 2025)
Closing the banking system is one of the most crucial things that should be done in financial institutions especially banks. This can happen daily, monthly, or yearly. When the bank follows a proper routine every day, it helps maintain consistency in its operations, and it will also help maintain the internal and external balance of the bank. The closing is stopped and delayed when any mistake or unusual and suspicious transaction occurs. Thus, it adds stress, work, and effort. This raises the dangers of doing business. Banks must close every day to deal with the large number of transactions that happen during the day that the bank’s actual activities to directly find any mistakes, suspicions, fraud attempts or theft. Unlike the shops or stores that are mostly closed for a day in the month or a year, this is different. The research proposes a Machine Learning based approach for the early identification of anomalies leading to daily closing failure. We used synthetic data that acted like a real bank setting. The dataset used was open-source and available on Kaggle as well. They used several models such as Random Forest, which is a supervised model, and Isolation Forest, which is an unsupervised model The models were deployed and analyzed on performance through metrics pertaining to machine learning such as recall, positive accuracy, F1- Score, and ROC-AUC. The study showed the results of Random Forest model are highly accurate for classifies data. The model called Isolation Forest was also quite capable of finding the unexpected patterns. The applications of both models together could be beneficial in developing and constructing a model for an early warning system which would reduce sudden closures and enhance the role of digital transformation in the banking industry as a whole