Example-dependent cost-sensitive credit cards fraud detection using SMOTE and Bayes minimum risk

Almhaithawi, Doaa; Jafar, Assef; Aljnidi, Mohamad

doi:10.1007/s42452-020-03375-w

Cited by 26 publications

(18 citation statements)

References 19 publications

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“…Other scholars have assessed the likelihood of vulnerability to different types of financial crime by applying computational and statistical methods [ 12 , 59 ]. The two most common techniques are neural networks [ 49 , 60 ] and binary logistic regression, respectively [ 41 , 50 , 61 , 62 ]. Neural networks could effectively be employed to identify patterns in large unstructured datasets [ 12 , 58 , 59 ].…”

Section: Methodsmentioning

confidence: 99%

Predicting Fraud Victimization Using Classical Machine Learning

Lokanan

Liu

2021

Entropy

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Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

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Section: Methodsmentioning

confidence: 99%

Predicting Fraud Victimization Using Classical Machine Learning

Lokanan

Liu

2021

Entropy

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“…Besides, using over-sampling methods leads to the production of duplicate data that doesn't provide information (the data and information are different, and the subject is discussed under the "Entropy"). Some researchers use synthetic minority oversampling (SMOTE) as a solution, which avoids the drawbacks of under and over sampling [5], [17], [22]. However, the SMOTE method causes an increase in the false-positive rate, which is not acceptable in banking for customer orientation.…”

Section: B Data Pre-processingmentioning

confidence: 99%

“…However, the SMOTE method causes an increase in the false-positive rate, which is not acceptable in banking for customer orientation. To solve this problem, in this study, we use class weight tuning hyperparameter to solve the mentioned disadvantages [5], [17], [22]. However, the SMOTE method causes an increase in the false-positive rate, which is not acceptable in banking for customer orientation.…”

Section: B Data Pre-processingmentioning

confidence: 99%

Fraud Detection in Banking Data by Machine Learning Techniques

2023

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As technology advanced and e-commerce services expanded, credit cards became one of the most popular payment methods, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in fraud requires high banking transaction costs. As a result, detecting fraudulent activities has become a fascinating topic. In this study, we consider the use of class weighttuning hyperparameters to control the weight of fraudulent and legitimate transactions. We use Bayesian optimization in particular to optimize the hyperparameters while preserving practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as CatBoost and XGBoost to improve the performance of the LightGBM method by accounting for the voting mechanism. Finally, in order to improve performance even further, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one. We perform some experiments on real-world data to test the proposed methods. To better cover unbalanced datasets, we use recall-precision metrics in addition to the standard ROC-AUC. CatBoost, LightGBM, and XGBoost are evaluated separately using a 5-fold cross-validation method. Furthermore, the majority voting ensemble learning method is used to assess the performance of the combined algorithms. LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79, according to the results. By using deep learning and the Bayesian optimization method to tune the hyperparameters, we also meet the ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a significant improvement over the cutting-edge methods we compared it to.

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“…The oversampling approach can be used to sample imbalanced data by generating new positive samples or replicating some positive samples, such as random oversampling and the synthetic minority oversampling technique (SMOTE; Chawla et al, 2002). Almhaithawi et al (2020) applied four common classifiers with SMOTE for fraud detection and concluded that SMOTE could improve the performance of most classifiers. The undersampling approach can be used to sample imbalanced data by eliminating some negative samples or generating new negative samples to replace the original negative samples, such as BalanceCascade (Liu et al, 2009) and cluster centroids (Lin et al, 2017).…”

Section: A Learning From Imbalanced Datamentioning

confidence: 99%

A Novel Multi-Stage Ensemble Model With a Hybrid Genetic Algorithm for Credit Scoring on Imbalanced Data

Jin

Zhang

et al. 2021

IEEE Access

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Credit scoring models are the cornerstone of the modern financial industry. After years of development, artificial intelligence and machine learning have led to the transformation of traditional credit scoring models based on statistics. In this study, a novel multi-stage ensemble model with a hybrid genetic algorithm is proposed to achieve accurate and stable credit prediction. To alleviate the adverse effects of imbalanced data in credit scoring models, the Instance Hardness Threshold method is extended using a majority voting strategy to deal with data imbalance. To eliminate redundant and irrelevant features in the dataset and select well-performing base classifiers, a new hybrid genetic algorithm is proposed to obtain the optimal feature subset and base classifier subset. To aggregate the predictive power of the base classifiers, a stacking approach is adopted to integrate the optimal base classifiers into the ensemble model. The proposed model is tested on three standard imbalanced credit scoring datasets, compared with similar state-of-the-art approaches, and evaluated using four well-known evaluation indicators. The experimental results prove the effectiveness of the proposed model and demonstrate its superiority.

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Example-dependent cost-sensitive credit cards fraud detection using SMOTE and Bayes minimum risk

Cited by 26 publications

References 19 publications

Predicting Fraud Victimization Using Classical Machine Learning

Predicting Fraud Victimization Using Classical Machine Learning

Fraud Detection in Banking Data by Machine Learning Techniques

A Novel Multi-Stage Ensemble Model With a Hybrid Genetic Algorithm for Credit Scoring on Imbalanced Data

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