An Autoencoder Based Model for Detecting Fraudulent Credit Card Transaction

Misra, Sumit; Thakur, Soumyadeep; Ghosh, Manosij; Saha, Sanjoy Kumar

doi:10.1016/j.procs.2020.03.219

Cited by 55 publications

(21 citation statements)

References 16 publications

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“…Processing real user credit data is costly due to its high dimensionality and extreme data imbalance ( Misra et al, 2020 ). The usual feature selection and feature extraction methods are computationally expensive to run on large datasets ( Ghosh et al, 2018 ) and statistical filtering based methods ignore the complex connections between multiple features.…”

Section: Methodsmentioning

confidence: 99%

A novel method for credit scoring based on feature transformation and ensemble model

Feng

Lin

et al. 2021

PeerJ Computer Science

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Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance problem. For the classification process, this paper designs a heterogeneous ensemble model by weighting the factorization machine (FM) and deep neural networks (DNN), which can efficiently extract low-order intersections and high-order intersections. Comprehensive experiments were conducted on two standard datasets and the results demonstrate that the proposed approach outperforms existing credit scoring models in accuracy.

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Section: Methodsmentioning

confidence: 99%

A novel method for credit scoring based on feature transformation and ensemble model

Feng

Lin

et al. 2021

PeerJ Computer Science

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“…The algorithm observes relationships in the data and then sequentially applies mathematical functions among the data. Other feature transformation methods in the field of financial fraud detection are for unsupervised learning algorithms including deep learning [6,22,23,24], and they show a high level of effects for unsupervised learning models.…”

Section: Related Workmentioning

confidence: 99%

A New Framework of Feature Engineering for Machine Learning in Financial Fraud Detection

Ikeda¹,

Ouazzane²,

Yu³

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Financial fraud activities have soared despite the advancement of fraud detection models empowered by machine learning (ML). To address this issue, we propose a new framework of feature engineering for ML models. The framework consists of feature creation that combines feature aggregation and feature transformation, and feature selection that accommodates a variety of ML algorithms. To illustrate the effectiveness of the framework, we conduct an experiment using an actual financial transaction dataset and show that the framework significantly improves the performance of ML fraud detection models. Specifically, all the ML models complemented by a feature set generated from our framework surpass the same models without such a feature set by nearly 40% on the F1-measure and 20% on the Area Under the Curve (AUC) value.

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“…Misra et al propose a two-stage model for credit card fraud detection. First, an autoencoder is used to reduce the dimensions so that the transaction attributes are transformed into a lower dimension feature vector [11]. Then, the final feature vector is sent to a supervised classifier as an input.…”

Section: Akila Et Al Present An Ensemble Model Named Riskmentioning

confidence: 99%

“…Subsequently, the output from the encoder is used as an input to a number of classifiers: Multi-Layer perceptron, knearest neighbors, logistic regression. F1 score is used to evaluate the final classifier [11]. It outperforms similar methods in terms of F1.…”

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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An Autoencoder Based Model for Detecting Fraudulent Credit Card Transaction

Cited by 55 publications

References 16 publications

A novel method for credit scoring based on feature transformation and ensemble model

A novel method for credit scoring based on feature transformation and ensemble model

A New Framework of Feature Engineering for Machine Learning in Financial Fraud Detection

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

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