_____________________________________________________________________________________________________ *Corresponding author: E-mail: 1954samir@gmail.com; Asian Journal of Research in Computer Science 11(3): 9-22, 2021; Article no.AJRCOS.72569 ISSN: 2581-8260 Emerging Approach for Detection of Financial Frauds Using Machine Learning Upasana Mukherjee 1 , Vandana Thakkar 1 , Shawni Dutta 1 , Utsab Mukherjee 1 and Samir Kumar Bandyopadhyay 2* 1 Department of Computer Science, The Bhawanipur Education Society College, India. 2 The Bhawanipur Education Society College, India. Authors’ contributions This work was carried out in collaboration among all authors. All authors read and approved the final manuscript. Article Information DOI: 10.9734/AJRCOS/2021/v11i330263 Editor(s): (1) Dr. Hasibun Naher, BRAC University, Bangladesh. Reviewers: (1) Shamitha S Kotekani, Visvesvaraya Technological University (VTU), India. (2) Maad M. Mijwil, Baghdad College of Economics Sciences University, Iraq. Complete Peer review History: https://www.sdiarticle4.com/review-history/72569 Received 15 June 2021 Accepted 21 August 2021 Published 27 August 2021 ABSTRACT The growth of regularly generated data from many financial activities has significant implications for every corner of financial modelling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many more can be modelled by implementing machine learning methods. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively. Original Research Article