Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Lucas, Yvan; Portier, Pierre-Édouard; Laporte, Léa; He-Guelton, Liyun; Caelen, Olivier; Granitzer, Michael; Calabretto, Sylvie

doi:10.1016/j.future.2019.08.029

Cited by 98 publications

(50 citation statements)

References 26 publications

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“…At the same time, insufficient attention is paid to the accompanying efficiency factors, namely the costs of various resources (temporal, human, financial, computational). It is particularly important to reduce the time spent on the development and maintenance of systems for identifying fraudulent transactions, a factor that only [13] pays attention to.…”

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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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%

Automatic machine learning algorithms for fraud detection in digital payment systems

Kolodiziev

Mints

Sidelov

et al. 2020

EEJET

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“…As it could be guessed, credit card transactions are not independent events that are isolated; instead, they are a sequence of transactions [10]. Lucas et al take this property into account and create Hidden Markov Model (HMM) to map a current transaction to its previous transactions, extract derived features, and use those features to come up with a Random Forest classifier for fraud detection [10]. The features created by HMM quantify how similar a sequence is to a past sequence of a cardholder or terminal [10].…”

Section: Akila Et Al Present An Ensemble Model Named Riskmentioning

confidence: 99%

“…Lucas et al take this property into account and create Hidden Markov Model (HMM) to map a current transaction to its previous transactions, extract derived features, and use those features to come up with a Random Forest classifier for fraud detection [10]. The features created by HMM quantify how similar a sequence is to a past sequence of a cardholder or terminal [10]. They evaluate the final model using Precision-Recall AUC metric and showed that feature engineering with HMM presents an acceptable rise in the PR-AUC score.…”

Section: Akila Et Al Present An Ensemble Model Named Riskmentioning

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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“…Feature engineering strategies perform the exam same process. A feature engineering based model for detecting frauds in credit card transactions was proposed by Lucas et al [8]. This work aims at using HMM for the prediction process.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Level Credit Card Fraud Detection

Sobanadevi*,

Ravi

2019

IJRTE

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Fraud detection in credit card transactions is one of the major requirements of the current business scenario due to the huge amount of losses associated with the domain. This work presents a multi-level model that can provide highly effective fraud detection in credit card transactions. The model is based on the amount for which the transaction is committed. The proposed MLFD model identifies the nature of the transaction and depending on the significance level of the transaction, the appropriate learning model is selected. Experiments were performed with the standard benchmark data and comparisons were performed with existing model in literature. Results shows that the proposed model exhibits high performance compared to the existing model.

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Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Cited by 98 publications

References 26 publications

Automatic machine learning algorithms for fraud detection in digital payment systems

Automatic machine learning algorithms for fraud detection in digital payment systems

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

Multi-Level Credit Card Fraud Detection

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