Detection of Bank Fraud Using Machine Learning Techniques
Gomatham Kavya Kalyani, Anand Kumar Mishra, Diya Harish, Amit Kumar Tyagi, S. A. Sajidha, Shashank Shekhar Pandey · 2023
In this digitally advanced age, the number and types of frauds happening around has also increased exponentially. The one place which people trust their money and other precious belongings to be with is the bank. These days, bank fraud, too, has been rising to an extent that it is high time that we understand the need for its early detection and prediction. Bank fraud refers to the use of illegal means to obtain the money, property, or other belongings of another individual or institution by some individuals who pose themselves as a bank or another financial institution. Most often, bank fraud is considered to be a criminal offense, but sometimes it also applies to actions that employ a scheme, and hence it is categorized as a white-collar crime, too. This project aims to detect fraudulent transactions from the banksim dataset. The utilization of machine learning (ML) in the finance industry can enhance the efficiency of bank transactions. This study showcases the ability of various regression models to predict insurance costs. A comparison of the results of the various models will be done—for example, random forest and various other regression and classification algorithms. The issues and frauds regarding the transactions associated with the banking field have been of utmost concern as these are quite high in spite of keeping the best of security systems. This project aims to determine the personal factors of an individual account that lead to bank frauds using various ML algorithms and take decisions on where and how much to invest or deposit.