Open AccessPredictive Analytics with Machine Learning for Fraud Detection
Author(s) -
Arjun Abhishek
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.39046
Subject(s) - commit , popularity , adaboost , random forest , credit card fraud , credit card , computer science , logistic regression , set (abstract data type) , analytics , business , machine learning , artificial intelligence , data mining , database , world wide web , payment , psychology , support vector machine , social psychology , programming language
Abstract: The popularity of online shopping is growing day by day. In financial year 2021, over 40 billion digital transactions worth more than a quadrillion Indian rupees were recorded across the country. As the number of credit card users rise world- wide, the opportunities for attackers to steal credit card details and subsequently, commit fraud are also increasing. Since humans tend to exhibit specific behavioristic profiles, every cardholder can be represented by a set of patterns containing information about the typical purchase category, the time since the last purchase, the amount of money spent etc. So these frauds can be detected through various algorithms mainly random forest and logistic regression. To enhance the boost and build model with much more efficiency adaboost is also added. Keywords: Fraud detection, behavioristic profile, random forest, logistic regression, adaboost