Purpose

Fraud transaction detection has become a significant factor in the communication technologies and electronic commerce systems, as it affects the usage of electronic payment. Even though, various fraud detection methods are developed, enhancing the performance of electronic payment by detecting the fraudsters results in a great challenge in the bank transaction.

Design/methodology/approach

This paper aims to design the fraud detection mechanism using the proposed Harris water optimization-based deep recurrent neural network (HWO-based deep RNN). The proposed fraud detection strategy includes three different phases, namely, pre-processing, feature selection and fraud detection. Initially, the input transactional data is subjected to the pre-processing phase, where the data is pre-processed using the Box-Cox transformation to remove the redundant and noise values from data. The pre-processed data is passed to the feature selection phase, where the essential and the suitable features are selected using the wrapper model. The selected feature makes the classifier to perform better detection performance. Finally, the selected features are fed to the detection phase, where the deep recurrent neural network classifier is used to achieve the fraud detection process such that the training process of the classifier is done by the proposed Harris water optimization algorithm, which is the integration of water wave optimization and Harris hawks optimization.

Findings

Moreover, the proposed HWO-based deep RNN obtained better performance in terms of the metrics, such as accuracy, sensitivity and specificity with the values of 0.9192, 0.7642 and 0.9943.

Originality/value

An effective fraud detection method named HWO-based deep RNN is designed to detect the frauds in the bank transaction. The optimal features selected using the wrapper model enable the classifier to find fraudulent activities more efficiently. However, the accurate detection result is evaluated through the optimization model based on the fitness measure such that the function with the minimal error value is declared as the best solution, as it yields better detection results.

中文翻译：

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基于包装模型和Harris水优化的深度循环神经网络银行交易欺诈检测

目的

欺诈交易检测已成为通信技术和电子商务系统中的重要因素，因为它影响电子支付的使用。尽管开发了各种欺诈检测方法，但通过检测欺诈者来提高电子支付的性能给银行交易带来了巨大挑战。

设计/方法/方法

本文旨在使用所提出的基于 Harris 水优化的深度循环神经网络（HWO-based deep RNN）设计欺诈检测机制。所提出的欺诈检测策略包括三个不同的阶段，即预处理、特征选择和欺诈检测。最初，输入事务数据经过预处理阶段，其中使用 Box-Cox 变换对数据进行预处理，以去除数据中的冗余和噪声值。预处理后的数据被传递到特征选择阶段，在那里使用包装模型选择基本和合适的特征。选择的特征使分类器具有更好的检测性能。最后，选定的特征被馈送到检测阶段，

发现

此外，所提出的基于 HWO 的深度 RNN 在精度、灵敏度和特异性等指标方面获得了更好的性能，其值为 0.9192、0.7642 和 0.9943。

原创性/价值

一种名为基于 HWO 的深度 RNN 的有效欺诈检测方法旨在检测银行交易中的欺诈行为。使用包装模型选择的最佳特征使分类器能够更有效地发现欺诈活动。然而，准确的检测结果是通过基于适应度度量的优化模型来评估的，因此将具有最小误差值的函数声明为最佳解决方案，因为它会产生更好的检测结果。