Credit Card Fraud Detection in the Nigerian Financial Sector: A Comparison of Unsupervised TensorFlow-Based Anomaly Detection Techniques, Autoencoders and PCA Algorithm

Abstract

Credit card fraud is a major cause of national concern in the Nigerian financial sector, affecting hundreds of transactions per second and impacting international ecommerce negatively. Despite the rapid spread and adoption of online marketing, millions of Nigerians are prevented from transacting in several countries with local credit cards due to bans and policies directed at restricting credit card fraud. Presently, a myriad of technologies exist to detect fraudulent transactions, a few of which are adopted by Nigerian financial institutions to proactively manage the situation. Fraud detection allows institutions to restrict offenders from networks and with a centralized banking identity management system, such as the Bank Verification Number used by the Central Bank of Nigeria, offenders who may have stolen other identities can be backtraced and their bank accounts frozen. This paper aims to compare the effectiveness of two fraud detection technologies that are projected to work fully independent of human intervention to possibly predict and detect fraudulent credit card transactions. Autoencoders as an unsupervised tensorflow based anomaly detection technique generally offers greater performance in dimensionality reduction than the Principal Component Analysis, and this theory was tested out on Nigerian credit card transaction data. Results demonstrate that autoencoders are better suited to analyzing complex and extensive datasets and offer more reliable results with minimal mislabeling than the PCA algorithm.