An intelligent payment card fraud detection system

Seera, Manjeevan; Lim, Chee Peng; Kumar, Ajay; Dhamotharan, Lalitha; Tan, Kim Hua

doi:10.1007/s10479-021-04149-2

Cited by 45 publications

(21 citation statements)

References 41 publications

(76 reference statements)

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“…In [15][16][17], the GA was used for feature selection and aggregation in an intelligent payment card fraud detection system. To test the efficacy of their suggested strategy, the authors used a variety of machine learning methods.…”

Section: Methodsmentioning

confidence: 99%

Fraud detection under the unbalanced class based on gradient boosting

Alothman

Talib

Mohammed

2022

EEJET

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Credit fraud modeling is an important topic covered by researchers. Overdue risk management is a critical business link in providing credit loan services. It directly impacts the rate of return and the bad debt percentage of lending organizations in this sector. Credit financial services have benefited the general public as a result of the development of the mobile Internet, and overdue risk control has evolved from the manual judgment that relied on rules in the past to a credit model built using a large amount of customer data to predict the likelihood of customers becoming delinquent. When creating a credit rating model, the emerging nature of the credit samples makes the minority class sample score very few; that is, when a large number of actual samples are obtained, this causes machine learning models to be biased towards the majority class when training. Traditional data balancing methods can reduce the bias of models to the majority category when the data is relatively unbalanced rather than excessive. Gradient boosting algorithms (XGBoost and CatBoost) are proposed in this paper to model highly unbalanced data to detect credit fraud. To find hyperparameters and determine the accuracy of the minority class as an optimization function of the model, Bayesian optimization is used to increase the model's accuracy for the minority class. The paper was tested with real European credit card fraud data. The results were compared to traditional machine learning (decision trees and logistic regression) and the performance of the bagging algorithm (random forest). For comparison, the traditional data balancing method (Oversample) is used

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Section: Methodsmentioning

confidence: 99%

Fraud detection under the unbalanced class based on gradient boosting

Alothman

Talib

Mohammed

2022

EEJET

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“…Seera et al [24] implemented 13 machine learning and statistical techniques to detect credit card fraud by utilizing actual public records of transactions. They performed a statistical hypothesis test to determine whether the features acquired through the genetic algorithm performed well compared with basic features for fraud detection.…”

Section: Related Workmentioning

confidence: 99%

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

et al. 2022

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Online sales and purchases are increasing daily, and they generally involve credit card transactions. This not only provides convenience to the end-user but also increases the frequency of online credit card fraud. In the recent years, in some countries, this fraud increase has led to an exponential increase in credit card fraud detection, which has become increasingly important to address this security issue. Recent studies have proposed machine learning (ML)-based solutions for detecting fraudulent credit card transactions, but their detection scores still need improvement due to the imbalance of classes in any given dataset. Few approaches have achieved exceptional results on different datasets. In this study, the Kaggle dataset was used to develop a deep learning (DL)-based approach to solve the text data problem. A novel text2IMG conversion technique is proposed that generates small images. The images are fed into a CNN architecture with class weights using the inverse frequency method to resolve the class imbalance issue. DL and ML approaches were applied to verify the robustness and validity of the proposed system. An accuracy of 99.87% was achieved by Coarse-KNN using deep features of the proposed CNN.

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“…To consider the time-series characteristics of input features, the long short-term memory algorithm has been used to develop both credit card delinquency prediction and fraud detection models [8,9]. In addition, various machine learning algorithms, such as decision tree, support vector machine, random forest, and genetic algorithm, are widely used in fraud detection modeling [31][32][33].…”

Section: Modeling Algorithms For Credit Scoringmentioning

confidence: 99%

Two-stage credit scoring using Bayesian approach

Kyeong¹,

Shin²

2022

J Big Data

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Commercial banks are required to explain the credit evaluation results to their customers. Therefore, banks attempt to improve the performance of their credit scoring models while ensuring the interpretability of the results. However, there is a tradeoff between the logistic regression model and machine learning-based techniques regarding interpretability and model performance because machine learning-based models are a black box. To deal with the tradeoff, in this study, we present a two-stage logistic regression method based on the Bayesian approach. In the first stage, we generate the derivative variables by linearly combining the original features with their explanatory powers based on the Bayesian inference. The second stage involves developing a credit scoring model through logistic regression using these derivative variables. Through this process, the explanatory power of a large number of original features can be utilized for default prediction, and the use of logistic regression maintains the model's interpretability. In the empirical analysis, the independent sample t-test reveals that our proposed approach significantly improves the model’s performance compared to that based on the conventional single-stage approach, i.e., the baseline model. The Kolmogorov–Smirnov statistics show a 3.42 percentage points (%p) increase, and the area under the receiver operating characteristic shows a 2.61%p increase. Given that our two-stage modeling approach has the advantages of interpretability and enhanced performance of the credit scoring model, our proposed method is essential for those in charge of banking who must explain credit evaluation results and find ways to improve the performance of credit scoring models.

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An intelligent payment card fraud detection system

Cited by 45 publications

References 41 publications

Fraud detection under the unbalanced class based on gradient boosting

Fraud detection under the unbalanced class based on gradient boosting

A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach

Two-stage credit scoring using Bayesian approach

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