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Research Paper | Computer Science | India | Volume 11 Issue 9, September 2022 | Rating: 4.9 / 10
Fraudulent Transactions Detection using Machine Learning
Asher George Jacob
Abstract: Information Technology has revolutionized and influenced each and every sector of the nation with banking sector not being an exception. India has moved from a manual, scale restricted space to an environment which has opened vistas of systems which are automated and computerized. Various customer-oriented products like ATM services, mobile banking, digital wallet online payment has made the life of the customers convenient. Lot of innovations have been made in the recent years across the banking sector through enablers like artificial intelligence, payment provisions, biometrics etc. Phenomenal improvements have been witnessed in the banking space with an increase in competition. However, making use of technology and sophisticated products pose a lot of discommodes. There are numerous challenges that need to be addressed. Credit frauds in the banking sector are seen as a common practice as numerous scams and scandals have been uncovered in the past few years. This research aims at identifying how frauds and scams in banks occur when customer makes payment through online mode. An attempt has been made in this research to detect such kinds of frauds using a machine learning algorithm. The random forest algorithm has been used for classification and detection of fraudulent transactions.
Keywords: Artificial Intelligence, Scams, Scandals, Fraudulent Transaction, Machine Learning, Random Forest Algorithm
Edition: Volume 11 Issue 9, September 2022,
Pages: 463 - 469