Autoencoders with Reconstruction Error and Dimensionality Reduction for Credit Card Fraud Detection

Rosley, Najmi; Tong, Gee-Kok; Ng, Keng-Hoong; Kalid, Suraya Nurain; Khor, Kok-Chin

doi:10.2991/978-94-6463-094-7_40

Cited by 2 publications

(3 citation statements)

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“…The hyperparameter tuning of AE was implemented by the Bayesian search function [2]. The optimized hyperparameters were the number of nodes in the hidden layers, the activation function, the Epochs, and the batch size.…”

Section: Related Workmentioning

confidence: 99%

“…Firstly, the AE network is an unsupervised learning algorithm that utilizes backpropagation by setting the inputs and outputs identically [21,25]. The main goal of the AE is to approximate the distribution of the input value as accurately as possible [2]. AE can detect fraud effectively for the following reasons: its ability to extract meaningful features by decoding and encoding the input data automatically during the training process.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…With the increasing incidences of fraudulent activity, detecting credit card fraud is important to guarantee financial security. As a result, there is a need to design a fraud detection model that can efficiently recognize and capture detailed patterns while correctly and reliably identifying abnormalities in credit transactions [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Credit Card Fraud Detection Using Improved Deep Learning Models

Sulaiman,

Nadher,

Hameed

2024

CMC

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Fraud of credit cards is a major issue for financial organizations and individuals. As fraudulent actions become more complex, a demand for better fraud detection systems is rising. Deep learning approaches have shown promise in several fields, including detecting credit card fraud. However, the efficacy of these models is heavily dependent on the careful selection of appropriate hyperparameters. This paper introduces models that integrate deep learning models with hyperparameter tuning techniques to learn the patterns and relationships within credit card transaction data, thereby improving fraud detection. Three deep learning models: AutoEncoder (AE), Convolution Neural Network (CNN), and Long Short-Term Memory (LSTM) are proposed to investigate how hyperparameter adjustment impacts the efficacy of deep learning models used to identify credit card fraud. The experiments conducted on a European credit card fraud dataset using different hyperparameters and three deep learning models demonstrate that the proposed models achieve a tradeoff between detection rate and precision, leading these models to be effective in accurately predicting credit card fraud. The results demonstrate that LSTM significantly outperformed AE and CNN in terms of accuracy (99.2%), detection rate (93.3%), and area under the curve (96.3%). These proposed models have surpassed those of existing studies and are expected to make a significant contribution to the field of credit card fraud detection.

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Section: Related Workmentioning

confidence: 99%