Design of financial big data audit model based on artificial neural network

Zhang, Zhiping; Wang, Zhaolin

doi:10.1007/s13198-021-01258-w

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…For big data technology, deep learning (DL) has been growing rapidly in recent years and is being used in a variety of fields. Zhang et al [24] established a financial audit model based on artificial neural network (ANN) technology to solve the problem of complexity of contemporary economic activities caused by the increase of financial data. Ye et al [25] constructed a data clustering-based network security state distribution model in a complex network environment, and adopted a big data mining method to data mine network security information to achieve security posture assessment and precise evaluation.…”

Section: Related Workmentioning

confidence: 99%

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Deng,

He,

Zhou

et al. 2023

Phys. Scr.

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Due to the large span of flight delay, numerous influencing factors and large number of flights, the collected flight delay data is characterized by large-scale and imbalance, which is considered as one of the largest challenges in balanced flight delay data classification by using conventional classification algorithms. To solve this problem, an adaptive multi-class classification approach of imbalanced flight delay data using synthetic minority over-sampling technique (SMOTE)-based convolutional neural network with sparrow search algorithm, namely SSA-LTCNN is proposed in this paper. Firstly, the SMOTE is used to reduce the imbalance between the classes of the flight delay data. Then, the qualitative analysis method is employed to determine the key parameters of the CNN, which are optimized by using SSA to establish an optimized CNN model. Finally, the optimized CNN model with more effective feature representation ability is employed to extract discriminative features from the balanced flight delay data automatically. The obtained features in the last fully connected layer of the optimized CNN model are used for flight delay classification. The MIT-BIH Arrhythmia Data is selected to verify the effectiveness of the proposed SSA-LTCNN method and the confusion matrix, classification accuracy, precision, recall and F1 score are also used to evaluate the performance of the proposed SSA-LTCNN method, which can get better performance than RF, DT, LR, CNN and QRSCNN. The actual flight delay data is processed to verify the effectiveness of the proposed classification approach, which can achieve higher accuracy than KNN, NB, UCNN, CNN, SSA-LCNN and SSA-TCNN in different time periods. The experimental results show that the SSA-LTCNN can fast and effectively realize the classification of MIT-BIH Arrhythmia Data and flight delay data.

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Section: Related Workmentioning

confidence: 99%

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Deng,

He,

Zhou

et al. 2023

Phys. Scr.

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“…Internal auditors must adapt to AI's new innovation in order to help firms without losing sight of their goals. [48,81,82] 16.…”

Section: Internal Auditmentioning

confidence: 99%

Transforming the Capabilities of Artificial Intelligence in GCC Financial Sector: A Systematic Literature Review

Khan

Malik

Alomari

et al. 2022

Wireless Communications and Mobile Computing

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Identity and access management is a business process framework that makes it easier to maintain genuine user identities and regulate access to sensitive assets. The word “access control” refers to an organization’s policy for authorizing access procedures, the mechanisms that implement and enforce the policy, and the model that the policy and procedures are built on. Adopting new technology may give rise to specific cyber threats that decrease or degrade business operations. The paper has designed to discuss the artificial intelligence-based access control system as a necessary component of governing and safeguarding the financial sector’s information assets in the Gulf Cooperation Council (GCC) region. Due to the dynamic and complicated nature of security rules for access control, organizations that employ web-enabled remote access in conjunction with applications access deployed over several networks face various obstacles, including increased operational complexity and monitoring concerns. Organizations spend a vast budget on securing their business. As the industry trend has shifted to intelligent internet-based companies on the same side, the cyber threat has become a challenge for the researcher to find the solution. A systematic research is conducted to fill the gaps in the existing literature by picking the most relevant research papers (126) from the four most reputable online repositories based on the four research questions specified. These research topics aim to evaluate the current situation from many perspectives and provide new avenues for future study to be studied soon to maintain high security and authenticity inside financial sectors of the GCC’s countries.

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“…As a multilayer feed-forward neural network, BP neural networks are trained to achieve complex nonlinear mapping functions by mimicking the function and structure of the brain's nervous system based on the mechanism of forward propagation of learning signals and backward propagation of errors. BP neural networks are used in a wide range of fields such as medicine [37], economics [38], and, in recent years, in the field of auditing [39,40].…”

Section: Data Mining Algorithmmentioning

confidence: 99%

An Audit Risk Model Based on Improved BP Neural Network Data Mining Algorithm

Niu¹,

Zhao²,

Jia³

et al. 2022

Advances in Multimedia

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Professional auditors provide audit services to businesses and they are key participants in enterprise development. Effective identification of audit risks can help auditors plan their audit work rationally and issue correct audit opinions. In the era of big data and the Internet, enterprises generate a large amount of data in their daily operations. For auditors, it is a great challenge to use data mining algorithms, machine learning, artificial intelligence, and other emerging technologies to identify high-quality audit data from the vast amount of data of audited enterprises. At the same time, some companies may falsify and modify their financial statements for their own benefit, which further increases the difficulty for auditors in conducting audits. Traditional auditing methods are costly and consuming and cannot meet standard auditing requirements. Therefore, this study applies computer data mining algorithms to construct an audit risk model that provides a reference for auditors to conduct big data analysis and mines valuable data, thereby improving the efficiency and accuracy of the audit process.

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Design of financial big data audit model based on artificial neural network

Cited by 7 publications

References 33 publications

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

A sparrow search algorithm-optimized convolutional neural network for imbalanced data classification using synthetic minority over-sampling technique

Transforming the Capabilities of Artificial Intelligence in GCC Financial Sector: A Systematic Literature Review

An Audit Risk Model Based on Improved BP Neural Network Data Mining Algorithm

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