• Balamurugan K
• Kishanth S
• Nivetha S
• Sakthivel S

Financial fraud is becoming a bigger problem for businesses and organizations, causing significant daily losses and harming society and the economy. The traditional manual methods to detect fraud are not practical – they take too much time and money. Even with current studies, the ways we try to stop fraud now are not working well. This study introduces a new way to detect fraud in bank payments. It uses the Random Forest Classifier algorithm in machine learning. The new system, tested with the Banksim dataset, works much better than the current methods. It shows high accuracy in both training and testing, reaching 99%. This is a step forward in using smart solutions to fight financial fraud. The performance of the proposed work can be identified using metrics like accuracy, precision and recall.

Financial Fraud Detection; Machine Learning; Random Forest Classifier; Banksim Dataset; Accuracy Metrics.

"Fraudulent Detection in Banking System using Random forest classifier Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.a751-a757, May-2024, Available :http://www.jetir.org/papers/JETIR2405086.pdf

"Fraudulent Detection in Banking System using Random forest classifier Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppa751-a757, May-2024, Available at : http://www.jetir.org/papers/JETIR2405086.pdf