• B Lakshmiprasanna
• T Kesava Rao
• M.E.Palanivel

Fraud has come a trillion-bone assiduity moment. Certain fiscal institutions have devoted brigades of sphere experts and data scientists that are assigned with detecting fraudulent exertion. To find frauds, data scientists constantly employ intricate statistical models. But there are a lot of downsides to this strategy. Since fraud discovery isn't real- time, fraudulent exertion are constantly discovered only after the factual fraud has taken place. These methodologies are prone to mortal crimes. In addition, it requires precious, largely professed sphere expert brigades and data scientists. nonetheless, the delicacy of homemade fraud discovery methodologies is low and due to that, it's veritably delicate to handle large volumes of data. More frequently, it requires time- consuming examinations into the other deals related to the fraudulent exertion in order to identify fraudulent exertion patterns.

Random Forest Classifier, Decision Tree Classifier, KMeans, Gaussian NB, SVC and KNeighbors Classifier.

"A PREDICTIVE ANALYTICS WITH ML FOR FRAUD DETECTION OF REAL TIME FINANCIAL DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.g230-g236, July-2024, Available :http://www.jetir.org/papers/JETIR2407628.pdf

"A PREDICTIVE ANALYTICS WITH ML FOR FRAUD DETECTION OF REAL TIME FINANCIAL DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppg230-g236, July-2024, Available at : http://www.jetir.org/papers/JETIR2407628.pdf