Research on big data audit based on financial sharing service model using fuzzy AHP

Abstract

With the rapid development of artificial intelligence and big data technology, the traditional audit method has been constantly impacted by big data. In the era of big data, enterprises actively explore and build a financial sharing service model, and through this model, build audit methods based on big data. In this paper, based on the financial sharing service model, we elaborate the preprocessing process of big data collection, clarity and storage, and build the simulation process framework of big data audit under the service model. Evaluation model is developed based on fuzzy analytic hierarchy process (AHP) and methodology for order estimation by similarity of solution. Finally, on the basis of the implementation process framework, the specific content of each link of big data audit is briefly given. Under the financial sharing service mode, it provides theoretical guidance and practical significance for the implementation of big data audit

Keywords

Financial sharing services artificial intelligence (AI)big data audit data processing process framework fuzzy analytic hierarchy

1 Introduction

In the era of big data, with the enormous development of various information technologies represented by cloud computing and data mining, new audit methods (hereinafter referred to as big data audit) based on the contextual of big data have gradually become the main audit methods of accounting firms and auditors [1]. As the audited object of enterprise business, auditors use big data related technology to collect, sort out, examine and process all data related to all businesses of the audited unit, and then output the final audit report, and comprehensively evaluate whether the audited unit has made sufficient arrangements for enterprise asset safety and operation efficiency by using the financial information system effectively [2]. No matter what kind of audit work, it is the auditor’s verification and evaluation of the audit object according to the clear audit standard. When auditors carry out the audit work, they need the active cooperation of the audited unit to provide complete and authentic audit data. At the same time, auditors must have an accurate understanding of the business content, organizational structure and work platform of the auditee [3]. In the traditional working environment, audit data are mainly offline data, not only the integrity of the data is insufficient, but also the traces of human modification are difficult to detect. In addition, all kinds of information are stored in different owners, the relationship between information is split, and it is unable to share and communicate in a timely and complete manner [4]. With the development of information network technology, not only the enterprises have realized electronization and networking in all aspects such as daily office work and cooperation of all departments, but also the business transactions between the unit and the outside have gradually interacted through the network platform. Some emerging enterprises have even basically realized the comprehensive data and networking [5]. When all activities of the enterprise can be down by the network and database, a large number of collection, review, tracking and comparison work of auditors can be completed by the computer system, and more importantly, the business and organizational characteristics of the audited unit, as well as the details of specific activities, can be visually displayed through visual data, and human interference is greatly reduced Weak. In the era of “Internet+” audit, audit work has the ability to become a tool for enterprises to comprehensively refine and standardize management [6].

The development of big data and cloud computing technology has a lot of influence on audit. Another researchers [7] believed that cloud accounting would have an impact on audit environment, audit objects, audit evidence, audit risk and auditors, and gave corresponding countermeasures founded on the results of impact analysis. The influence of big data and cloud computing on audit indirectly promotes the birth and development of big data audit. A novel research has given that [8] taking the audit of local tax collection and management as an example, elaborated the influence of audit based on the aspects of audit data acquisition, organization and audit data analysis. Some researchers [9] analyzed background of big data utilizing audit data analysis from these aspects: data volume, data structure and data processing method, and elaborated the audit data analysis method under the big data environment from the perspective of analytics and users. Another research [10] has shown that taking the construction of the Internet of things as the application background, described the objectives, basis, objects and three-tier audit system of big data audit. Looking at the existing research, most of the literatures are based on the traditional financial accounting mode to study the audit or big data audit in the era of big data, and few of them are based on the financial sharing service mode. Model illustrating financial service was originated in United States in early 1980 s and has been widely concerned and actively applied in China [11]. The establishment of enterprise financial sharing service center provides a good foundation for the implementation of big data audit. In view of this, this paper studies the big data audit below the service of financial service mode, analyzes the large data preprocessing process under the financial sharing service mode combined with the characteristics of the financial sharing service mode, and expounds the big data audit based on the financial sharing service mode technological process [12].

2 Characteristics of operation and management under the mode of Financial Sharing

Financial sharing mode is a management mode of accounting and reporting business developed in recent years. It is to collect the accounting business of different entities scattered in different regions into a Financial Sharing Center for unified bookkeeping and reporting. Through the division of labor within the organization of Financial Sharing Center for different entities’ financial business, but in terms of work platform, accounting and reporting standards, structure, etc., the financial system of different regions is properly adapted. Thus, on the one hand, the financial organization of regional branches will be simplified to focus more on business support; on the other hand, the isolated information island will be eliminated to ensure the real-time sharing and collection of financial information, especially in the group financial center, which can output financial reports covering all regional entities with high frequency [13]. Of course, the operation of this financial sharing center will be limited by the laws of some countries, but for the group, it is still feasible to share financial information in real time through the interconnection of the information platform within the group. Obviously, the financial sharing service mode is mainly applicable to the units with diversified businesses and many branches, such as financial institutions, chain enterprises, information and communication service enterprises, etc., while the factories and administrative institutions with single institutions, centralized businesses and low degree of informatization are not suitable. The enterprise management under the application of financial sharing service mode has the following characteristics [14]:

System platform of enterprise organization management and business operation. Finance and actual business are inseparable. Only when the business operation and organization management have a unified system platform support, can the financial sharing service mode be realized. Otherwise, when the business and finance are separated, there will be a lack of connection and cooperation between the financial management system and the diversified needs of the actual business operation, and even the financial management system will restrict the development of the business. For example, in the shared financial service mode, the business characteristics of each business module are different, and the financial management methods and support required are also different, including budget management, daily approval, etc. If the management rules of Financial Sharing Center conflict with the characteristics of business operation, there will be serious contradictions in management logic and monitoring.

Business oriented financial services. Based on the unified bookkeeping and reporting rules and structure of the Financial Sharing Center, and with the help of modular adaptive selection, entities and institutions in various regions can hand over a large number of basic financial work, such as bookkeeping, Sub Ledger and statistical reports, to the sharing center to complete, while local financial personnel need to develop business capabilities, provide greater assistance to project operation and organizational operation, and more Financial risk management and financial solutions to help business and management. Generally speaking, the accounting work is handed over to the Financial Sharing Center, and each branch strengthens the financial cost management ability. Third, the financial information sharing network is smooth, and the financial data is shared and collected in real time. The financial basic data of each region and each branch can be transmitted in real time and at all levels through the system of the sharing center, and according to the standard paradigm and structure, financial reports that meet the requirements of each region entity and the group can be generated automatically, so as to carry out the overall systematic financial internal control management of the enterprise. Based on massive micro financial data, Financial Sharing Center has the ability to implement big data audit.

3 Fuzzy analytic hierarchy process method

Analytic Hierarchy Process (AHP) is most widely utilized model for decision issues. Many certain issues can be integrated into many sub issues utilizing AHP in terms of level of hierarchy level where every phase signifies a group of criteria or components comparative to every sub issues. The model is a multi-criteria phase of evaluating on the basis of a weighted additive process, in which numerous attributes which are relevant are mentioned through their relative significance. AHP has broadly applied by professionals, mainly in application pertaining to engineering fields. Through AHP, the significance of numerous attributes is analyzed from procedure of paired association, in which the attributes which has relevance or assets to category driver. This model deploys fuzzy AHP to fuzzify hierarchical analysis by letting numbers in fuzzy for pairwise association and identifying the fuzzy hierarchical estimation.

3.1 Fuzzy number establishing

Fuzzy set are groups whose components have membership degree. Fuzzy sets have been modelled initially as an elaboration of classical notion of group. In theory of classical set, the elements membership in a group is illustrated in terms of binary values based to bivalent situation- a component either exists or does not exists in set.

A fuzzy number $\overset{⌣}{B}$ on $ℤ$ to be certain triangular fuzzy number if membership function δ ∼ B (y): $ℝ \to [0, 1]$ is equivalent to given equation below (1): $δ \sim B (y) = {\begin{matrix} \frac{y - f}{n - f}, & f ⩽ y ⩽ n \\ \frac{v - y}{v - n}, & n ⩽ y ⩽ v \\ 0, & otherwise \end{matrix}$ (1)

From Equation 1 we can conclude that f and v states the lower and upper bounds of the $\overset{⌣}{B}$ fuzzy number, and n is the value for $\overset{⌣}{B}$ . The triangular fuzzy number can be illustrated by $\overset{⌣}{B}$ = (f, n, v). the laws of operation $B_{1}^{⌣} = (f_{1}, n_{1}, v_{1})$ and $B_{2}^{⌣} = (f_{2}, n_{2}, v_{2})$ are deployed as following equations $\begin{matrix} B_{1}^{⌣} \oplus B_{2}^{⌣} = (f_{1}, n_{1}, v_{1}) \oplus (f_{2}, n_{2}, v_{2}) \\ = (f_{1}, n_{1}, v_{1} + f_{2}, n_{2}, v_{2}) \end{matrix}$ (2)

Fuzzy number multiplication ⊕ $\begin{matrix} B_{1}^{⌣} \oplus B_{2}^{⌣} = (f_{1}, n_{1}, v_{1}) \oplus (f_{2}, n_{2}, v_{2}) \\ = (f_{1} f_{2}, n_{1} n_{2}, v_{1} v_{2} for f_{1} f_{2} > 0; n_{1} n_{2} > 0; v_{1} v_{2} > 0 \end{matrix}$ (3)

Fuzzy number subtraction $\begin{matrix} B_{1}^{⌣} \oplus B_{2}^{⌣} = (f_{1}, n_{1}, v_{1}) \oplus (f_{2}, n_{2}, v_{2}) \\ = (f_{1}, v_{2}, n_{1} - n_{2}, v_{1} - v_{2}) \end{matrix}$ (4)

Fuzzy number reciprocal $\begin{matrix} {\overset{⌣}{B}}^{- 1} = (f_{1}, n_{1}, v_{1})^{- 1} = (\frac{1}{v_{1}}, \frac{1}{n_{1}}, \frac{1}{f_{1}},) \\ For f_{1} f_{2} > 0; n_{1} n_{2} > 0; v_{1} v_{2} > 0 \end{matrix}$ (5)

4 Pretreatment of audit big data based on financial sharing service mode

4.1 Big data flow in audit

The basis of big data audit is big data generated by business operation. Before preprocessing big data, it is necessary to clarify the flow direction of big data under the financial sharing service mode. Under the financial sharing service mode, the cloud accounting AIS of the Financial Sharing Service Center for unified processing of enterprise financial data is established, and the seamless connection of ERP, SCM, CRM and other systems of the subsidiary companies of the enterprise is realized, which makes the flow direction of big data in the whole audit process very various from financial accounting in traditional method. The specific flow direction is shown in Figure.

The flow direction of big data under the financial sharing service mode can effectively avoid the problems handled by audit under the outdated financial accounting mode [15]:

The workload of data collection is large. Under the traditional financial accounting mode, the data of the subsidiary companies are stored in their own accounting information systems. When auditing, the data flow from each subsidiary company to the audit data warehouse, which leads to a large amount of data collection work. In the financial sharing service mode, auditors only need to collect financial data in the Financial Sharing Service Center, and collect business data through ERP and other business systems, which greatly reduces the workload of data collection.

The workload of data cleaning is large. Below the outdated financial mode, the accounting information system of the subsidiary company of the enterprise does not realize seamless connection, which may have different data interfaces and other problems. When collecting data related to business, a lot of data cleaning work wants to be done to advance the data excellence. Under the financial sharing service mode, the accounting business can be standardized, which can be seamlessly connected with other business models like ERP, SCM and CRM under the financial sharing service style in Fig. 1, and can effectively reduce the workload of data cleaning.

Unable to use external data effectively. Under the traditional financial accounting mode, it is difficult to collect external data. Before the integration of accounting information system and other business systems of subsidiaries, it is difficult to obtain valuable information from the huge external data, and then it can not effectively use the external data. Financial sharing service mode creates conditions for enterprises to effectively use external data, and can establish an effective external data collection mechanism, greatly reducing the cost of external data collection.

Fig. 1

Big data flow of financial sharing service mode.

4.2 Big data preprocessing

On the basis of defining the flow direction towards data, big data has been preprocessed, including big data gathering, big data cleaning and data storage [16]:

Big data collection under Financial Sharing, big data collection mainly comes from three sources: the first big data source is Financial Sharing Service Center cloud accounting AIS, which includes shared financial data, business financial data and strategic financial data, not only including budget management, expense reimbursement, asset accounting and other capital management data, but also comprehensive budget, investment and financing, business analysis and other strategic management data Manage data. The second big data source is other business systems, such as ERP, SCM and CRM. This part of big data is mainly business data from business processes such as enterprise purchase management, production management and sales management. Under the financial sharing service mode, financial data can be standardized. ERP, SCM, CRM and other systems of the enterprise can be seamlessly connected with the accounting using cloud AIS of the Monetary Sharing Service Center, which makes the collection of big data easier. The third big data source is the external data of the enterprise. The external data related to the enterprise business can reflect the possible problems in the enterprise business from another dimension.

Big data cleaning is to detect, eliminate and correct errors and inconsistencies in data, so as to improve data quality. In assistance to sharing of financial models, the standardization of financial data entry can greatly reduce the workload of data cleaning, but in order to make better use of external data, data cleaning is still an essential big data preprocessing process.

After data cleaning, big data collected from big data storage should be centrally managed and stored at different levels, and an audit data warehouse should be established to facilitate auditors to find or form audit evidence during big data audit.

5 Implementation model of audit using big data using financial Sharing Service Mode

5.1 Procedure framework construction

Afterward the big data preprocessing mentioned above, auditors can implement the relevant audit work according to the audit plan to form audit doubts. On this basis, they can collect various audit evidences and finally draw audit conclusions to form audit reports. The specific audit work flow can be divided into six steps, including the establishment of audit objectives, risk identification and assessment, the formulation of audit plans, and the selection of audit procedures that can be implemented, the implementation of audit procedures, and the formation of audit conclusions and the issuance of audit reports. The complete framework flow is shown in Fig. 2.

Fig. 2

Big data audit procedure framework based on financial sharing service mode.

5.2 Big data audit implementation procedure

5.2.1 Establishment of audit objectives

As an audit method, big data audit can’t help auditors to set various audit objectives. The specific audit objectives need auditors to find situation based certain audit. Given the executor point of view of the enterprise audit, the audit of big data on monetary sharing facility mode can be used for the audit internally of the enterprise’s internal auditors, and can also be used for the external audit of the enterprise’s financial statements by the auditors of the external accounting firm. From the perspective of audit object, this kind of big data audit can not only be used to audit all businesses of the enterprise, but also audit the accounting information of cloud platform system. If we audit all businesses of an enterprise, big data audit needs to focus on fully reflecting the relevant business processes and capital flows; if we audit the system based on cloud analysis, the audit emphasis of big data audit is to verify whether the cloud platform system can play a role in the property protection, data maintenance and reasonable economic resource consumption of the enterprise [17]. In case of internal audit, the focus of big data audit will turn to whether the financial information system established by the financial sharing service mode can support the decision-making of the management; in case of external audit, the focus of big data audit is to measure expected information users can meet their decision-making needs under the financial sharing service mode.

5.2.2 Risk identification and assessment

In addition to the negative impact of importance on the problems of data in audit, the procedure and outcome of data pre analysis will also have an impact on audit risk through various ways. Under normal circumstances, if the pre-treatment process is not handled in accordance with the regulations or the results are different from the audit requirements, the audit risk enhances. In extra, the cloud credibility model is significantly related to audit risk. Too low credibility will not only significantly increase audit risk, but also may have a significant impact on the final audit conclusion [18]. In order to effectively identify and evaluate audit risk, it is necessary to implement relevant monitoring activities in the whole process of big data preprocessing. In addition, the audit project team can also consider the size of audit risk through professional third-party internal control and credibility evaluation reports on cloud platform systems and other systems. Under the premise of considering the importance of the audited unit, the final audit project team obtains the audit risk of the audited unit and evaluates it based on the relevant assessment results.

5.2.3 Development of audit plan

The specific audit plan of big data audit needs to include the scope of audit, audit time arrangement, materials and manpower required for audit, especially the time and manpower arrangement involved in big data preprocessing. For big data audit, the scope of audit is broader than that of traditional audit methods. The main data range involves from database from audit created in the process of big data analyzing, including various interior financial and non-financial data handling to audit and various external data [19].

5.2.4 Selection of audit procedures that can be implemented

Based on the big data audit under the financial sharing service mode, the selection of audit procedures is mainly divided into three steps, namely, data analysis, middle table establishment and audit doubt formation [20]:

Data constructing the analysis and audit normalization doubt mainly come from the data processing in procedure of audit, and the formation of review doubt is the premise of subsequent audit evidence acquisition. In method of analyzing data, auditors should not only focus on the association amongst platform in cloud system and system involving business like ERP and SRP, but also need to audit the internal control of business processes such as “settlement management, voucher preparation, audit and archive” in cloud platform system, with the common purpose of effective data analysis.

No matter what kind of data analysis method is used, the ultimate purpose of the establishment of the intermediate table is to help auditors find problems in different non-financial and data related businesses, so as to form audit doubts. On the basis of the formation of audit doubt, the auditor’s immediate work is to implement further audit procedures to obtain various audit evidences for audit doubt, and finally form audit conclusions and issue audit reports. Back to the problem of audit doubt, the intermediate table, as the intermediate product of forming audit doubt, is phase mentioned by auditors provided by analyzing the standard data based on the table to analyzed model of audit [21]. Considering the complexity of various business data and the similarities and differences of cross check relationship in the audit process, auditors can set up one level or multi-level intermediate table as required in the process of forming audit doubts, which will be an important reference for auditors to verify Audit doubts. The establishment of intermediate tables at all levels by auditors in accordance with the prescribed procedures and standards is a necessary guarantee for the formation of audit doubts and the acquisition of follow-up audit evidence. In the normal audit process, the methods of “cascade” and “projection” can be considered for the establishment of intermediate table.

The so-called audit doubt is the information summary of various potential problems found when the analysis of big data and association of the intermediate table [22]. After the formation of audit doubt, the main work of auditors is to further obtain various audit evidence to verify whether the audit doubt is really a false report caused by various errors or fraud, and draw the final audit conclusion according to the verification results. The main work of verifying audit doubts is to check the authenticity and integrity. Based on the big data audit of financial sharing service mode, one of the work that the auditors can reduce is the letter work in the substantive procedure [23]. The auditor can directly verify the data obtained externally through system of could and the business systems represented by ERP, without complicated audit procedures such as letter, which improves the audit performance on the basis of reducing cost and saving time Efficiency.

5.2.5 Implementation of audit procedures

Rendering to the audit procedure to be implemented in the audit plan, we need to supervise the collection, sorting and storage of all types of audit big data in the whole process [24, 25]. Based on the comprehensive platform scheme and each business system by the third-party experts, the auditors comprehensively use the audit doubts system to obtain the audit evidence as much as possible, so as to finally get the audit results. Problems found in the process of big data audit should be fed back to the enterprise management in time, and these problems need to be reviewed and evaluated at the same time.

5.2.6 Form audit conclusion and issue audit report

On the basis of forming audit doubts according to the audit plan, various audit evidences are obtained. Based on this, the audit results of the audit project team are obtained. In consideration of the auditee and its environment, the audit opinions need to be obtained in combination with the previously formulated audit objectives. All kinds of misstatements found in the audit process need to be communicated with the management of the audited unit in a timely manner. When necessary, various opinions should be put forward to the management and the management, and finally an audit report should be issued based on the relevant replies or suggestions from the management.

6 Promote the application of big data audit under the financial sharing service mode

In order to promote the application of big data audit under the financial sharing service mode, we must face the challenges and deal with the problems.

6.1 Specific data analysis methods for auditors

First, big data mining technology. Through the automatic big data mining algorithm to deal with all kinds of big data in audit data warehouse, we can find all kinds of potential, unknown or unforeseen problems of big data, and finally find all kinds of audit doubts with the help of this. In the big data audit based on the financial sharing service mode, the main functions of data mining technology are: not only can we use this to establish the financial data and financial data, financial data and the cross check relationship among business data, business data and business data forms audit doubts and finds problems in business. Second, multidimensional analysis. In addition to the use of data analysis methods, another method is often used in big data analysis. Auditors use multi-dimensional data in audit data warehouse to analyze various kinds of summary, association, clustering and classification. Using multidimensional analysis method, compared with data mining technology, it is easier to find potential commercial procedure problems in the method of analysis of data, draw audit doubts, and finally form audit evidence and audit conclusions through multidimensional data aggregation audit. Third, SQL query. In addition to big data mining technology and multidimensional analysis and data analysis process, the third data analysis method, SQL data query technology, which is basically utilized in audit containing data audit at present and in the tradition. Auditors can use SQL statements to analyze the query or fuzzy between manifold tables. In this process, they can find potential or hidden problems and form audit doubts.

6.2 Establish safety risk response plan

For the security problems caused by force majeure, in order to prevent the occurrence of accidents, audit institutions should establish a security risk emergency plan, and consider the synchronous storage of local and foreign data when storing. For the safety problems caused by human factors, we must start from the root. In view of the security problem of data modification, the audit institutions need to fully consider the professional competence and professional ethics of the auditors when arranging their work. In view of the security problem of malicious data theft, different measures should be taken in different aspects. As far as audit institutions are concerned, it is necessary to arrange personnel with high professional ethics and good professional quality to audit key information, design appropriate supervision measures for information transmission and intercommunication, and establish encryption mechanism; as far as enterprises are concerned, it is necessary to set up user identity authentication and interview authentication, strictly control data acquisition authority, and transfer data with audit institutions, Special personnel shall be arranged to be responsible for the safety and real-time monitoring of the transmission process. For the security problem of over dependence on system, auditors must keep enough professional sensitivity.

6.3 Improve the quality of auditors

Enterprises, external audit institutions and relevant government departments must strengthen the excavation and training of audit talents in their respective fields. When selecting auditors, we should expand the selection scope of auditors, not only considering the talents of audit, accounting and other majors, but also the talents of computer technology, cloud computing, network technology and other fields. We should also pay attention to the professional quality of auditors. Only learning talents can continue to grow into comprehensive audit talents. In addition, we should strengthen the training of auditors and improve their comprehensive quality.

6.4 Strengthen information communication between interested parties

For the communication between different departments within the enterprise, the enterprise management should take the lead to hold regular business and financial meetings or meetings between related business departments to exchange information. For the communication between the audit institution and the auditee, both parties shall arrange information communication specialists to be responsible for the effective communication and feedback of the information of both parties.

In contrast with the research carried for financial service based on big data from the Figs. 3 and 4 we can depict that ROC curve stating the audit and audit fraction variation for the financial servie model performance.

Fig. 3

ROC of Audit.

Fig. 4

ROC of Audit Fraction.

7 Conclusion

With the advent of the era of big data and the rapid development of various information technologies, big data audit has become more and more important. At the same time, the enterprise management mode of financial sharing service mode has been introduced by a large number of enterprises. In this context, how to use various information technologies to realize the big data audit based on financial sharing service mode has increasingly become the focus of the industry. This paper briefly expounds the flow direction of big data under the financial sharing service mode, analyzes the pretreatment process of big data, and finally expounds the audit process of big data under the financial sharing service mode, in order to provide theoretical support for the implementation of big data audit under the financial sharing service mode, promote the popularization of big data audit mode and the development of financial sharing service mode Exhibition.

References

Kumari

, Tanwar

, Tyagi

, Kumar

, Maasberg

and Choo

K.K.R.

, Multimedia big data computing and Internet of Things applications: A taxonomy and process model, Journal of Network and Computer Applications 124, 169–195.

Alguliyev,

Rasim M.

, Aliguliyev

Ramiz M.

and Sukhostat

Lyudmila V.

, Efficient algorithm for big data clustering on single machine, CAAI Transactions on Intelligence Technology 5.1 (2020), 9–14.

Appelbaum,

Deniz

, Kogan

Alexander

and Vasarhelyi

Miklos A.

, Big Data and analytics in the modern audit engagement: Research needs, Auditing: A Journal of Practice & Theory 36.4 (2017), 1–27.

Arunachalam,

Deepak

, Kumar

Niraj

and Kawalek

John Paul

, Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice, Transportation Research Part E: Logistics and Transportation Review 114 (2018), 416–436.

BalaAnand

, Karthikeyan

and Karthik

, Envisioning social media information for big data using big vision schemes in wireless environment, Wireless Pers Commun (2019). https://doi.org/10.1007/s11277-019-06590-w

Dai

, Byrnes

, Liu

, et al., Audit analytics: A field study of credit card after-sale service problem detection at a Major Bank[J], Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry (Rutgers Studies in Accounting Analytics), Emerald Publishing Limited (2019), 17–33.

Flood,

Mark D.

, Jagadish

H. V.

and Raschid

Louiqa

, Big data challenges and opportunities in financial stability monitoring. Banque de France, Financial Stability Review 20 (2016), 29–42.

Jabbour,

Charbel Jose Chiappetta

, et al., Unlocking the circular economy through new business models based on large-scale data: an integrative framework and research agenda, Technological Forecasting and Social Change 144 (2019), 546–552.

Knechel

W.R.

, Thomas

and Driskill

, Understanding financial auditing from a service perspective[J], Accounting, Organizations and Society 81 (2020), 101080.

10.

Manogaran,

Gunasekaran

, et al., “Big data security intelligence for healthcare industry 4.0.” Cybersecurity for Industry 4.0. Springer, Cham (2017), 103–126.

11.

Rehman

Mhur

, Habib

Muhammad

, et al., Big data reduction framework for value creation in sustainable enterprises, International Journal of Information Management 36.6 (2016), 917–928.

12.

Michael

and Dixon

, Audit data analytics of unregulated voluntary disclosures and auditing expectations gap[J], International Journal of Disclosure and Governance 16(4) (2019), 188–205.

13.

Bali

R.S.

and Kumar

, Secure clustering for efficient data dissemination in vehicular cyber–physical systems, Future Generation Computer Systems 56, 476–492.

14.

Ruan

, Yan

, Dong

, et al., Identifying suspicious groups of affiliated-transaction-based tax evasion in big data[J], Information Sciences 477 (2019), 508–532.

15.

Salijeni

, Samsonova-Taddei

and Turley

, Big Data and changes in audit technology: contemplating a research agenda[J], Accounting and Business Research 49(1) (2019), 95–119.

16.

Seddon,

Jonathan JJM

and Currie

Wendy L.

, A model for unpacking big data analytics in high-frequency trading, Journal of Business Research 70 (2017), 300–307.

17.

Sledgianowski,

Deb

, Gomaa

Mohamed

and Tan

Christine

, Toward integration of Big Data, technology and information systems competencies into the accounting curriculum, Journal of Accounting Education 38 (2017), 81–93.

18.

Thota,

Chandu

, et al., “Architecture for big data storage in different cloud deploymentmodels.”Handbook of Research on Big Data Storage and Visualization Techniques, IGI Global (2018), 196–226.

19.

Thota,

Chandu

, et al. “Big data security framework for distributed cloud data centers.” Cybersecurity breaches and issues surrounding online threat protection, IGI Global (2017), 288–310.

20.

Wang

and Islam

, An optimization model for poverty Alleviation fund audit mode based on BP neural network[J], Journal of Intelligent & Fuzzy Systems 37(1) (2019), 481–491.

21.

Wang

, Gao

and Ma

, Applications of Financial Shared Service Model in Chinese Corporations[C]//5th International Conference on Economics, Management, Law and Education (EMLE 2019). Atlantis Press (2019), 697–703.

22.

Wang,

Yichuan

and Hajli

Nick

, Exploring the path to big data analytics success in healthcare, Journal of Business Research 70 (2017), 287–299.

23.

Wang,

Yichuan

, et al., An integrated big data analytics-enabled transformation model: Application to health care, Information & Management 55.1 (2018), 64–79.

24.

Winter

J.S.

and Davidson

, Big data governance of personal health information and challenges to contextual integrity[J], The Information Society 35(1) (2019), 36–51.

25.

Xia

Q.I.

, et al., MeDShare: Trust-less medical data sharing among cloud service providers via blockchain, IEEE Access 5 (2017), 14757–14767.