A graph-based, semi-supervised, credit card fraud detection system

Lebichot, Bertrand; Braun, Fabian; Caelen, Olivier; Saerens, Marco

doi:10.1007/978-3-319-50901-3_57

Cited by 33 publications

(36 citation statements)

References 19 publications

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“…For example, work [9] examines the use of a combination of Unsupervised and Supervised training methods. Article [10] suggested that a graph-based semi-supervised system should be used to solve the problem. This gradually expands the methodology for building fraud-detection systems; it should be noted that recently there has been more and more research on the use of resource-intensive technologies such as deep learning and artificial neural networks [11,12].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…A large part of the studies analyzed [3,4,[7][8][9][10][11] refer, in one way or another, to a global open data analysis project in digital banking payment systems called "Fraud Detection with Machine Learning." The project has been carried out since 2013 at a web platform by researchers from Europe, the United States, and other countries of the world [14].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Automatic machine learning algorithms for fraud detection in digital payment systems

Kolodiziev

Mints

Sidelov

et al. 2020

EEJET

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Data on global financial statistics demonstrate that total losses from fraudulent transactions around the world are constantly growing. The issue of payment fraud will be exacerbated by the digitalization of economic relations, in particular the introduction by banks of the concept of "Bank-as-a-Service", which will increase the burden on payment services. The aim of this study is to synthesize effective models for detecting fraud in digital payment systems using automated machine learning and Big Data analysis algorithms. Approaches to expanding the information base to detect fraudulent transactions have been proposed and systematized. The choice of performance metrics for building and comparing models has been substantiated. The use of automatic machine learning algorithms has been proposed to resolve the issue, which makes it possible in a short time to go through a large number of variants of models, their ensembles, and input data sets. As a result, our experiments allowed us to obtain the quality of classification based on the AUC metric at the level of 0.977-0.982. This exceeds the effectiveness of the classifiers developed by traditional methods, even as the time spent on the synthesis of the models is much less and measured in hours. The models' ensemble has made it possible to detect up to 85.7 % of fraudulent transactions in the sample. The accuracy of fraud detection is also high (79-85 %). The results of our study confirm the effectiveness of using automatic machine learning algorithms to synthesize fraud detection models in digital payment systems. In this case, efficiency is manifested not only by the resulting classifiers' quality but also by the reduction in the cost of their development, as well as by the high potential of interpretability. Implementing the study results could enable financial institutions to reduce the financial and temporal costs of developing and updating active systems against payment fraud, as well as improve the effectiveness of monitoring financial transactions

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Automatic machine learning algorithms for fraud detection in digital payment systems

Kolodiziev

Mints

Sidelov

et al. 2020

EEJET

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“…Lebichot et al [42] proposed several improvements based on an existing Fraud Detection Systems APATE. APATE uses a collective inference algorithm to spread fraudulent influence through a network by using a limited set of confirmed fraudulent transactions.…”

Section: Graph Based Semi-supervised Learningmentioning

confidence: 99%

A state of the art survey of data mining-based fraud detection and credit scoring

Zhou¹,

Cheng²,

Zhu³

et al. 2018

MATEC Web Conf.

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Abstract.Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.

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“…This work focuses on automatically detecting fraudulent e-commerce transactions using network-related features and free energy distance [20]. Our work is based on a recent paper [21] which introduced several improvements to an existing collective inference algorithm called APATE [36]. More precisely, this algorithm starts from a defined number of known frauds and propagates the fraudulent influence through a graph to obtain a risk score, quantifying the fraudulent behavior for each transaction, cardholder, and merchant [36].…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Fraud Detection with the Free Energy Distance

Courtain

Lebichot

Kivimäki

et al. 2019

Complex Networks and Their Applications VIII

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This paper investigates a real-world application of the free energy distance between nodes of a graph [14, 20] by proposing an improved extension of the existing Fraud Detection System named APATE [36]. It relies on a new way of computing the free energy distance based on paths of increasing length, and scaling on large, sparse, graphs. This new approach is assessed on a real-world large-scale e-commerce payment transactions dataset obtained from a major Belgian credit card issuer. Our results show that the free-energy based approach reduces the computation time by one half while maintaining state-of-the art performance in term of Precision@100 on fraudulent card prediction.

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A graph-based, semi-supervised, credit card fraud detection system

Cited by 33 publications

References 19 publications

Automatic machine learning algorithms for fraud detection in digital payment systems

Automatic machine learning algorithms for fraud detection in digital payment systems

A state of the art survey of data mining-based fraud detection and credit scoring

Graph-Based Fraud Detection with the Free Energy Distance

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