Open AccessProvisional Research On Ensemble Learning Techniques For Card Fraud Detection
Author(s) -
Pooja Pant,
P. D. Srivastava,
Ashutosh Gupta
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f1003.0886s19
Subject(s) - credit card fraud , ensemble learning , credit card , computer science , falling (accident) , stability (learning theory) , financial stability , artificial intelligence , machine learning , computer security , business , financial system , psychology , world wide web , psychiatry , payment
Machine learning have revolutionized fraud detection in various domains like telecommunication and ecommerce. Global statistics shows how billions of dollars are lost because of card frauds every year and millions of people falling the victims. Fraud detection systems used for credit card fraud detection 2 decades ago are still being used because of the trust and stability they have provided for so long. With a number of academic research being done in fraud detection their effect on the financial industry has been minimum. Even with high prediction accuracy using machine learning approaches like deep learning and stack ensemble most of these research gets directly rejected by the industry. Our research objective is to highlight the reason of rejectection which are mostly ignored by the researchers and there adverse effect on the results