Proceedings ArticleDOI
Credit card fraud detection using machine learning techniques: A comparative analysis
John O. Awoyemi,Adebayo Olusola Adetunmbi,Samuel A. Oluwadare +2 more
- pp 1-9
TLDR
Investigation of the performance of naïve bayes, k-nearest neighbor and logistic regression on highly skewed credit card fraud data shows that k-NEarest neighbour performs better than naive bayes and logistics regression techniques.Abstract:
Financial fraud is an ever growing menace with far consequences in the financial industry. Data mining had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data mining problem, becomes challenging due to two major reasons — first, the profiles of normal and fraudulent behaviours change constantly and secondly, credit card fraud data sets are highly skewed. The performance of fraud detection in credit card transactions is greatly affected by the sampling approach on dataset, selection of variables and detection technique(s) used. This paper investigates the performance of naive bayes, k-nearest neighbor and logistic regression on highly skewed credit card fraud data. Dataset of credit card transactions is sourced from European cardholders containing 284,807 transactions. A hybrid technique of under-sampling and oversampling is carried out on the skewed data. The three techniques are applied on the raw and preprocessed data. The work is implemented in Python. The performance of the techniques is evaluated based on accuracy, sensitivity, specificity, precision, Matthews correlation coefficient and balanced classification rate. The results shows of optimal accuracy for naive bayes, k-nearest neighbor and logistic regression classifiers are 97.92%, 97.69% and 54.86% respectively. The comparative results show that k-nearest neighbour performs better than naive bayes and logistic regression techniques.read more
Citations
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Journal ArticleDOI
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.
TL;DR: Fast AnoGAN (f‐AnoGAN), a generative adversarial network (GAN) based unsupervised learning approach capable of identifying anomalous images and image segments, that can serve as imaging biomarker candidates is presented.
Journal ArticleDOI
An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine
TL;DR: The proposed intelligent approach for detecting fraud in credit card transactions using an optimized light gradient boosting machine (OLightGBM) outperformed the other approaches and achieved the highest performance in terms of accuracy.
Journal ArticleDOI
Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019
TL;DR: This review provides a good reference source in guiding the detection of financial fraud for both academic and practical industries with useful information on the most significant data mining techniques used and shows the list of countries that are exposed to financial fraud.
Journal ArticleDOI
Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection
TL;DR: In this paper, the authors used three machine learning algorithms namely: logistic regression, Naive Bayes and K-nearest neighbor to predict fraudulent transactions in credit card data, and the performance of these algorithms is measured based on accuracy, sensitivity, specificity, precision, F-measure and area under curve.
Journal ArticleDOI
Champion-challenger analysis for credit card fraud detection: Hybrid ensemble and deep learning
Eunji Kim,Jehyuk Lee,Hunsik Shin,Hoseong Yang,Sungzoon Cho,Seung-kwan Nam,Youngmi Song,Jeong-a Yoon,Jong Il Kim +8 more
TL;DR: An in-depth comparison between the hybrid ensemble and deep learning method to determine whether or not to adopt the latter in a partner’s system that currently operates with the Hybrid ensemble model is conducted.
References
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Proceedings Article
On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes
Andrew Y. Ng,Michael I. Jordan +1 more
TL;DR: It is shown, contrary to a widely-held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is increased, one in which each algorithm does better.
Journal ArticleDOI
Data mining for credit card fraud: A comparative study
TL;DR: Two advanced data mining approaches, support vector machines and random forests, together with the well-known logistic regression, are evaluated as part of an attempt to better detect (and thus control and prosecute) credit card fraud.
Journal ArticleDOI
Minority report in fraud detection: classification of skewed data
TL;DR: This paper compares the new fraud detection method (meta-learning approach) against C4.5 trained using undersampling, oversamplings, and SMOTEing without partitioning, and shows that, given a fixed decision threshold and cost matrix, the partitioning and multiple algorithms approach achieves marginally higher cost savings than varying the entire training data set with different class distributions.
Proceedings ArticleDOI
Survey of fraud detection techniques
TL;DR: The goal of this paper is to provide a comprehensive review of different techniques to detect frauds and present a survey of current techniques used in credit card fraud detection, telecommunication Fraud detection, and computer intrusion detection.
Unsupervised Profiling Methods for Fraud Detection
Richard J. Bolton,David J. Hand +1 more
TL;DR: This paper is concerned with detecting behavioural fraud through the analysis of longitudinal data, and discusses two methods for unsupervised fraud detection in credit data and applies them to some real data sets.