2022
DOI: 10.4018/jgim.308806
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Risk Measurement of the Financial Credit Industry Driven by Data

Abstract: The risk measurement of financial credit industry is an important research issue in the field of financial risk assessment. The design of financial credit risk measurement algorithm can help investors avoid greater risks and obtain higher returns, so as to promote the benign development of financial credit industry. Based on the combined deep learning algorithm, this paper studies the risk measurement of financial and credit industry, and proposes a fusion algorithm of deep auto-encoder (DAE) and Long Short-Te… Show more

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Cited by 12 publications
(4 citation statements)
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“…Since the numerical units of all features involved in 20) . Compared with the traditional Auto Encoder (AE), DAE uses Gaussian noise method to increase the pollution of data, which aims to improve the ability of AE to reconstruct the original data and reduce the replication of the model to the original input.…”
Section: Normalization and Anti-normalizationmentioning
confidence: 99%
“…Since the numerical units of all features involved in 20) . Compared with the traditional Auto Encoder (AE), DAE uses Gaussian noise method to increase the pollution of data, which aims to improve the ability of AE to reconstruct the original data and reduce the replication of the model to the original input.…”
Section: Normalization and Anti-normalizationmentioning
confidence: 99%
“…Qiu (2021) devised an artificial intelligence accounting information web system by integrating various subsystems and then assessed the feasibility of the system's theoretical and technological foundations. Li et al (2022) proposed a data-driven approach based on deep learning algorithms and introduced finance-related content, and Parada et al (2018) proposed an anomaly detection method based on information management in the Internet of Things (IoT) environment that can provide ideas for related research. Zhao and Zhou (2022) proposed a deep learning digital economy scale measurement method based on a big data cloud platform as well as related applications.…”
Section: Related Workmentioning
confidence: 99%
“…Financial frauds are difficult to detect manually since the instances of corporate frauds are always concealed (Zakolyukina, 2018; Amiram et al, 2020), particularly in the case of that fraud methods are getting diversified and complicated as corporate business expands and innovates continuously (Li et al, 2022; Yang & Wu, 2022). Fortunately, machine learning develops rapidly in recent years, providing efficient approaches to exploring the relationship between financial risks and the growing financial data (Du & Shu, 2022; Li et al, 2022; Wu et al, 2022). Therefore, many scholars are devoted to developing novel fraud detection models using machine learning, such as Logistic Regression, Naive Bayes, Support Vector Machine, Neural Network, Random Forest, Ensemble Method and many more (Song et al, 2014; Cao et al, 2015; Vasarhelyi et al, 2015; Brown et al, 2020; Ding et al, 2020; Bertomeu et al, 2021; Chen & Zhai, 2023; Xu et al, 2023; Achakzai & Peng, 2023; Li et al, 2023; Pan et al, 2023; Riskiyadi, 2023; Rahman & Zhu, 2023; Zhou et al, 2023).…”
Section: Introductionmentioning
confidence: 99%