Factors influencing fraudulent transactions from big data perspective

Abstract

This article focuses on fraudulent behaviour and patterns as well as ways of detecting such patterns by using Big Data. The study analyses scientific articles to examine types of financial fraud and their detection techniques as well as develops a model that is based on factors characterizing fraudulent credit card transactions made across USA. Regression analysis, correlation and descriptive statistics analysis is applied. Statistically significant results are found indicating a causal relationship between fraudulent transactions and transactions made in Alaska, during the month of October and on a Thursday. Although, the impact of these relationships is relatively small. Expanding the dataset with more numerical variables that could be used for identifying fraudulent transactions is advised for future research as to better the overall fit of the model.