Utilizing Machine Learning and Big Data Analysis for Risk Mitigation and Fraud Detection in Finance

Abstract

With the rise of online banking systems and easy transactions, there is an increase in fraud in the banking system and in the field of finance. To reduce fraud in the transactions we can apply the systems of machine learning algorithms and big data analysis. In this research paper, we discuss various methods used in the field such as Supervised learning, Unsupervised learning, and Ensemble Methods in the field of machine learning and transaction monitoring, behavior analytics, network analytics, and pattern recognition in the field of real-time monitoring. We have used a data set from Kaggle on credit card transactions and the methods of Random Forest Classification and Support Vector Machine which comes under the supervised learning method in machine learning and discussed other results and benefits achieved from it.