Abstract: Banks have been the most important institutions of money lending and deposits. Primary functions include accepting deposits, offering loans, credit, overdraft, providing liquidity and discounting of bills. Secondary functions include providing safe custody of valuables, loans on valuables, corporate and consumer finances. Though the structure of banks has remained the same, the functionalities have been boosted. Automated tools, bots and computers have modernized the banking system. The dataset accumulated over a period of time is so huge that, automation tools and computer programs are the need of the day. In this paper we have tried to enhance the present bank credit-debit system by the use of Artificial Intelligence. Machine learning is a subset of AI and directly trains the machine by feeding the historic and runtime data collected during transactions. The machine which is trained is now capable of taking decisions, thereby making predictions. This would characterize the dataset as stored and predicted outcomes. Every business enthusiast would have keen interest to carefully study the performance of a financial institute for his/her benefit. In this assignment we have used both classification and regression algorithms to create a ML model of prediction. Linear regression model is designed from scratch using formula method. Classification algorithms like Support Vector Machine (SVM), Random Forest Classifier and KNN algorithms are effectively applied to fit to the dataset. Comparisons must be made during implementation to understand the pattern of predicted data. Regression algorithms like linear regression (developed from scratch) will be a boost to the accuracy of the assignment (categorical data excluded).
Keywords: accepting deposits, offering loans, credit, overdraft, providing liquidity and discounting of bills, Automated tools, bots and computers, Machine learning, Support Vector Machine (SVM), Random Forest Classifier and KNN algorithms, linear regression (developed from scratch), historic and runtime data collected during transactions, AI.
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DOI:
10.17148/IJARCCE.2020.9331
