Abstract
Closing the banking system is one of the most crucial things that should be done in financialinstitutions especially banks. This can happen daily, monthly, or yearly. When the bank follows aproper routine every day, it helps maintain consistency in its operations, and it will also helpmaintain the internal and external balance of the bank. The closing is stopped and delayed when anymistake or unusual and suspicious transaction occurs. Thus, it adds stress, work, and effort. Thisraises the dangers of doing business. Banks must close every day to deal with the large number oftransactions that happen during the day that the bank’s actual activities to directly find anymistakes, suspicions, fraud attempts or theft. Unlike the shops or stores that are mostly closed for aday in the month or a year, this is different. The research proposes a Machine Learning basedapproach for the early identification of anomalies leading to daily closing failure. We used syntheticdata that acted like a real bank setting. The dataset used was open-source and available on Kaggleas well. They used several models such as Random Forest, which is a supervised model, andIsolation Forest, which is an unsupervised model The models were deployed and analyzed onperformance 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 accuratefor classifies data. The model called Isolation Forest was also quite capable of finding theunexpected patterns. The applications of both models together could be beneficial in developing andconstructing a model for an early warning system which would reduce sudden closures and enhancethe role of digital transformation in the banking industry as a whole