Credit Card Fraud Prediction and Classification using Deep Neural Network and Ensemble Learning

Khan, Fairoz Nower; Khan, Amit Hasan; Israt, Lamiah

doi:10.1109/tensymp50017.2020.9231001

Cited by 6 publications

(1 citation statement)

References 3 publications

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“…For financial banks and institutes, credit card default prediction, credit approval and bankruptcy prediction are significant tasks and challenges. The frauds of credit card can be divided into many activities, such as lost card, card holder not present and counterfeit card [1]. Hence, these tasks of monitoring credit card data and transactions are essential.…”

Section: Introductionmentioning

confidence: 99%

Identifying a Default of Credit Card Clients by using a LSTM Method: A Case Study

Wu¹,

Liu²

2022

Artificial Intelligence Trends

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Detecting fraudulent transactions is critical and challenging for financial banks and institutes. This study used a deep learning technique, which is a long short-term memory (LSTM) method, for identifying a default of credit card clients (an imbalanced dataset). To evaluate the performance of optimizers for the LSTM approach, this study employed three optimizers based on gradient methods, such as adaptive moment estimation (Adam), stochastic gradient descent with momentum (Sgdm) and root mean square propagation (Rmsprop). This study used 10-fold cross-validation. Moreover, this study compared the best numerical results of the LSTM method with those of supervised machine learning classifiers, which are back-propagation neural network (BPNN) with a gradient descent algorithm (GDA) and a scaled conjugate gradient algorithm (SCGA). Numerical results indicate that the LSTM-Adam and the BPNN-SCGA classifiers have identical performance, and that selecting an appropriate classification threshold value is important for an imbalanced dataset. Based on the numerical results, the LSTM-Adam classifier can be considered for dealing with credit scoring problems, which are binary classification problems.

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Section: Introductionmentioning

confidence: 99%

Identifying a Default of Credit Card Clients by using a LSTM Method: A Case Study

Wu¹,

Liu²

2022

Artificial Intelligence Trends

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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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Ensemble Approach with Hyperparameter Tuning for Credit Worthiness Prediction

Sugave

2022

2022 IEEE 3rd Global Conference for Advancement in Technology (GCAT)

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Credit Card Fraud Prediction and Classification using Deep Neural Network and Ensemble Learning

Cited by 6 publications

References 3 publications

Identifying a Default of Credit Card Clients by using a LSTM Method: A Case Study

Identifying a Default of Credit Card Clients by using a LSTM Method: A Case Study

Fraud Detection in Banking Data by Machine Learning Techniques

Ensemble Approach with Hyperparameter Tuning for Credit Worthiness Prediction

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