Abstract
The increase in the financial data has led to the complexity in contemporary economic activities making traditional audit work challenging. The problem of financial frauds and economic losses has routed the path for designing of reliable financial big data audit models. To overcome this issue and assist the auditors to examine the elementary state of the audit object accurately, this article establishes an artificial neural network technology based financial audit model to perform large number of verification calculations. This article analyzes the effect of loan enterprises operating conditions based on the characteristics of financial audit to establish a financial audit model by using artificial neural network technology. The outcomes obtained reveals that accurate model performance is achieved with maximum error rate of the sample is 4.8%. The obtained outcomes demonstrates that the classification training accuracy of the model is 94.29% and testing accuracy of 90% is achieved. The evaluation value of the loan enterprise calculated by the model in this paper is essentially consistent with the assessment value of the actual loan enterprise, and the classification of the enterprise operation status is also accurate. This model can be utilized by auditors for the estimation of financial audit conditions and it can solve the overall analysis problem of financial audit data under the condition of massive amount of data.
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References
Abbasi A, Albrecht C, Vance A, Hansen J (2012) Metafraud: a meta-learning framework for detecting financial fraud. MIS Q 36:1293–1327
Ahmad T, Chen H (2020) A review on machine learning forecasting growth trends and their real-time applications in different energy systems. Sustain Cities Soc 54:102010
Alameer Z, Abd Elaziz M, Ewees AA, Ye H, Jianhua Z (2019) Forecasting copper prices using hybrid adaptive neuro-fuzzy inference system and genetic algorithms. Nat Resour Res 28(4):1385–1401
Chen WS, Du YK (2009) Using neural networks and data mining techniques for the financial distress prediction model. Expert Syst Appl 36(2):4075–4086
Chow DS, Humphrey C, Moll J (2007) Developing whole of government accounting in the UK: Grand claims, practical complexities and a suggested future research agenda. Financial Account Manag 23(1):27–54
Chun SH, Kim SH (2004) Data mining for financial prediction and trading: application to single and multiple markets. Expert Syst Appl 26(2):131–139
Chun SH, Park YJ (2006) A new hybrid data mining technique using a regression case based reasoning: Application to financial forecasting. Expert Syst Appl 31(2):329–336
Deng, Y. . (2020). The Financial Data Prediction System Based on the Recurrent Neural Network. Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019).
Dhiman, G., Kumar, V. V., Kaur, A., & Sharma, A. (2021). DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images. Interdisciplinary Sciences: Computational Life Sciences, 1–13.
Farhath ZA, Arputhamary B, Arockiam L (2016) A survey on ARIMA forecasting using time series model. Int J Comput Sci Mobile Comput 5:104–109
Fernández-Gámez MA, García-Lagos F, Sánchez-Serrano JR (2016) Integrating corporate governance and financial variables for the identification of qualified audit opinions with neural networks. Neural Comput Appl 27(5):1427–1444
Feroz EH, Kwon TM, Pastena VS, Park K (2000) The efficacy of red flags in predicting the SEC’s targets: an artificial neural networks approach. Intell Syst Acc Fin Manag 9(3):145–157
Fischer JA, Pohl P, Ratz D (2020) A machine learning approach to univariate time series forecasting of quarterly earnings. Rev Quant Financ Acc 55(4):1163–1179
Gaganis C, Pasiouras F, Doumpos M (2007) Probabilistic neural networks for the identification of qualified audit opinions. Expert Syst Appl 32(1):114–124
Geng R, Bose I, Chen X (2015) Prediction of financial distress: An empirical study of listed Chinese companies using data mining. Eur J Oper Res 241(1):236–247
Hao Y (2020) Combination of neural network model for enterprise accounting information quality assessment. Int J Comput Sci Math 12(3):297
Jiang S, Yang C, Guo J, Ding Z (2018) ARIMA forecasting of China’s coal consumption, price and investment by 2030. Energy Sources Part B 13(3):190–195
Jun, S. Z. , & Yuan, Y. W. . (2019). Bp neural network via granular computing for prediction of financial trends. Journal of Chinese Computer Systems.
Kannan S, Dhiman G, Natarajan Y, Sharma A, Mohanty SN, Soni M, Gheisari M (2021) Ubiquitous Vehicular Ad-Hoc Network Computing Using Deep Neural Network with IoT-Based Bat Agents for Traffic Management. Electronics 10(7):785
Khandelwal M, Armaghani DJ, Faradonbeh RS, Yellishetty M, Abd Majid MZ, Monjezi M (2017) Classification and regression tree technique in estimating peak particle velocity caused by blasting. Eng Comput 33(1):45–53
Kim MJ, Han I (2003) The discovery of experts’ decision rules from qualitative bankruptcy data using genetic algorithms. Expert Syst Appl 25(4):637–646
Koyuncugil AS, Ozgulbas N (2012) Financial early warning system model and data mining application for risk detection. Expert Syst Appl 39(6):6238–6253
Lam M (2004) Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decis Support Syst 37(4):567–581
Li H, Sun J, Wu J (2010) Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods. Expert Syst Appl 37(8):5895–5904
Lin, F. Y., & McClean, S. (2001). A data mining approach to the prediction of corporate failure. In Applications and Innovations in Intelligent Systems VIII (pp. 93–106). Springer, London.
Liu, D. . (2019). Research and application of community prediction of time series of network of artificial neural network nonlinear perspective. ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS III: 3rd International Conference on Advances in Materials, Machinery, Electronics (AMME 2019).
Man, Y. , Ding, L. , & Xiaoguo, Z. . (2018). Nonlinear System Identification Method Based on Improved Deep Belief Network. 2018 Chinese Automation Congress (CAC).
Pourheydari O, Nezamabadi-pour H, Aazami Z (2012) Identifying qualified audit opinions by artificial neural networks. Afr J Bus Manage 6(44):11077–11087
Sánchez-Serrano JR, Alaminos D, García-Lagos F, Callejón-Gil AM (2020) Predicting Audit Opinion in Consolidated Financial Statements with Artificial Neural Networks. Mathematics 8(8):1288
Sotti F (2018) The value relevance of consolidated and separate financial statements: Are non-controlling interests relevant? Afr J Bus Manage 12(11):329–337
Wenlong, C. , Jianwu, R. , & Min, L. . (2019). Survey of neural network model compression methods. Application Research of Computers.
Wu J, Zhang L, Yin F (2019) Research on the prediction of popular opinion trend of web news based on bp neural network and lstm. IOP Conference Series: Mater Sci Eng 646:012003
Yin, F. M., Xu, H. H., Gao, H. H., & Bian, M. J. (2019). Research on Weibo public opinion prediction using improved genetic algorithm based BP neural networks
Yuvaraj N, Srihari K, Dhiman G, Somasundaram K, Sharma A, Rajeskannan S, Masud M (2021) Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking. Math Problems Eng 2021:1–12
Zhang X, Liang X, Zhiyuli A, Zhang S, Xu R, Wu B (2019) At-lstm: an attention-based lstm model for financial time series prediction. IOP Conference Series Mater Sci Eng 569:052037
Zhao J, Li XL, Ma HF (2020) The application of bp neural network algorithm in rapid design of the vertical boom of bridge inspection vehicle. J Phys conference Series 1570(1):012005
Zhou W, Kapoor G (2011) Detecting evolutionary financial statement fraud. Decis Support Syst 50(3):570–575
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Zhang, Z., Wang, Z. Design of financial big data audit model based on artificial neural network. Int J Syst Assur Eng Manag (2021). https://doi.org/10.1007/s13198-021-01258-w
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DOI: https://doi.org/10.1007/s13198-021-01258-w