Deep learning detecting fraud in credit card transactions

Roy, Abhishek; Sun, Jingyi; Mahoney, Robert; Alonzi, L. P.; Adams, Stephen; Beling, Peter A.

doi:10.1109/sieds.2018.8374722

Cited by 196 publications

(90 citation statements)

References 18 publications

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“…For example, American Express relies heavily on data and DL to help detect fraud in near-real time, therefore saving millions in losses [84]. In [85], it is examined the behavior of borrowers to predict the mortgage risk, in [86] the fraud in credit card transactions is detected, and in [87] an algorithm for trading is proposed. The black-box characteristics (lack of interpretability) that these solutions present when taking decisions should be taken into account, since they can cause millionaire losses and subsequent demands.…”

Section: Applications In Technological Financial and Other Successfmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

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Section: Applications In Technological Financial and Other Successfmentioning

confidence: 99%

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

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“…Within the past decade, however, we see a rise of application of deep learning models due to their ability to handle big datasets as well as to train realtime in a streaming manner. This is while retaining their stateof-the-art performance in various tasks like real time object recognition [27] and fraud detection [28].…”

Section: Unsupervised Deep Learning Modelsmentioning

confidence: 98%

GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

Nguyen

Lim

Divakaran³

et al. 2019

2019 IEEE Conference on Communications and Network Security (CNS)

110

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This paper looks into the problem of detecting network anomalies by analyzing NetFlow records. While many previous works have used statistical models and machine learning techniques in a supervised way, such solutions have the limitations that they require large amount of labeled data for training and are unlikely to detect zero-day attacks. Existing anomaly detection solutions also do not provide an easy way to explain or identify attacks in the anomalous traffic. To address these limitations, we develop and present GEE, a framework for detecting and explaining anomalies in network traffic. GEE comprises of two components: (i) Variational Autoencoder (VAE) -an unsupervised deep-learning technique for detecting anomalies, and (ii) a gradient-based fingerprinting technique for explaining anomalies. Evaluation of GEE on the recent UGR dataset demonstrates that our approach is effective in detecting different anomalies as well as identifying fingerprints that are good representations of these various attacks.

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“…Roy et al extend the baseline features and add the following new features to their model [20]: Frequency of transactions per month, filling the missing data dummy variables, maximum, mean authorization amounts in the 8-month period, new variables to indicate when a transaction is made at a predefined location such as restaurants, gas stations etc., a new variable demonstrating whether a transaction amount in a given retailer is greater than 10% of the standard deviation of the mean of legitimate transactions for that retailer [20].…”

Section: Feature Engineering Challengementioning

confidence: 99%

Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods

Yazıcı¹

2020

Computer Science &Amp; Information Technology

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Credit card fraud is an ongoing problem for almost all industries in the world, and it raises millions of dollars to the global economy each year. Therefore, there is a number of research either completed or proceeding in order to detect these kinds of frauds in the industry. These researches generally use rule-based or novel artificial intelligence approaches to find eligible solutions. The ultimate goal of this paper is to summarize state-of-the-art approaches to fraud detection using artificial intelligence and machine learning techniques. While summarizing, we will categorize the common problems such as imbalanced dataset, real time working scenarios, and feature engineering challenges that almost all research works encounter, and identify general approaches to solve them. The imbalanced dataset problem occurs because the number of legitimate transactions is much higher than the fraudulent ones whereas applying the right feature engineering is substantial as the features obtained from the industries are limited, and applying feature engineering methods and reforming the dataset is crucial. Also, adapting the detection system to real time scenarios is a challenge since the number of credit card transactions in a limited time period is very high. In addition, we will discuss how evaluation metrics and machine learning methods differentiate among each research.

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Deep learning detecting fraud in credit card transactions

Cited by 196 publications

References 18 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection

Approaches to Fraud Detection on Credit Card Transactions using Artificial Intelligence Methods

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