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Applied Economics Letters Volume 30, 2023 - Issue 4
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Research Article

An optimal weighted machine learning model for detecting financial fraud

Minghuan ShouBusiness School, University of Nottingham Ningbo China, Ningbo, ChinaCorrespondence[email protected]
,
Xueqi BaoBusiness School, University of Nottingham Ningbo China, Ningbo, China
&
Jie YuBusiness School, University of Nottingham Ningbo China, Ningbo, China
Pages 410-415 | Published online: 06 Oct 2021

References

  • Abbasi, A., C. Albrecht, A. Vance, and J. Hansen. 2012. “MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud.” MIS Quarterly 36 (4): 1293–1327. doi:10.2307/41703508.
  • Bhattacharya, S., D. M. Xu, and K. Kumar. 2011. “An ANN-based Auditor Decision Support System Using Benford’s Law.” Decision Support Systems 50: 576–584. doi:10.1016/j.dss.2010.08.011.
  • Hajek, P., and R. Henriques. 2017. “Mining Corporate Annual Reports for Intelligent Detection of Financial Statement Fraud – A Comparative Study of Machine Learning Methods.” Knowledge-Based Systems 128: 139–152. doi:10.1016/j.knosys.2017.05.001.
  • Jan, C. L. 2018. “An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan.” Sustainability 10: 513. doi:10.3390/su10020513.
  • Perols, J. 2011. “Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms.” American Accounting Association 30 (2): 19–50.
  • Pierdzioch, C., M. Risse, and S. Rohloff. 2016. “A Boosting Approach to Forecasting Gold and Silver Returns: Economic and Statistical Forecast Evaluation.” Applied Economics Letters 23 (5): 347–352. doi:10.1080/13504851.2015.1073835.
  • Sadgali, I., N. Sael, and F. Benabbou. 2019. “Performance of machine learning techniques in the detection of financial frauds.„ Procedia Computer Science, 148: 45–54. doi:10.1016/j.procs.2019.01.007
  • Song, X. P., Z. H. Hu, J. G. Du, and Z. H. Sheng. 2014. “Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China.” Journal of Forecasting 33: 611–626. doi:10.1002/for.2294.
  • Wang, X., X. Xie, Y. H. Chen, and B. R. Zhao. 2021. “A Machine Learning Approach to Forecasting Trade Returns.” Applied Economics Letters 28:1-6. doi:10.1080/13504851.2021.1918624

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