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The Applications of Machine Learning in Accounting and Auditing Research

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Encyclopedia of Finance

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Abstract

The term “machine learning” has become a buzzword in the past few years. In accounting and auditing area, while this technology has been used in major accounting firms such as Big 4 s, its research is still evolving. Increased use of machine learning and other artificial intelligence techniques will allow accountants to focus on providing better decision support instead of on data gathering and manual analyses. This entry introduces machine learning as compared to traditional statistical modeling, discusses its current applications in accounting and auditing research, and provides directions for future research.

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Notes

  1. 1.

    The data source is https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data. In this application case, explanatory variables including room types, geographical availability, and the number of reviews per month are treated as the independent variables to predict prices.

  2. 2.

    The data source is http://yann.lecun.com/exdb/mnist/

  3. 3.

    One of the examples in this domain is in https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python. The task of this data set is to segment customers based on their behavioral related attributes by applying the K-means clustering algorithms.

  4. 4.

    A bootstrap replicate is accessed by randomly sampling the training set with replacement. This operation will generate a new training set with size equal to that of the original one.

  5. 5.

    The statistical problem means that the training set fails to provide adequate information to select one single learner within the circumstance that multiple unique learners can achieve the same accuracy on the training set.

  6. 6.

    Searching for the best hypothesis (e.g., neutral network) that fits in the training data may be computationally intractable.

  7. 7.

    The approximations to the real target function, which are generated from single learners, may not be ideal.

  8. 8.

    “Complexity” means that the decision tree model generates a plethora of rules, resulting the overfitting issues.

  9. 9.

    For details of the cross-validation method, check the website: https://scikit-learn.org/stable/modules/cross_validation.html

  10. 10.

    More details are presented on the website: https://www.tensorflow.org/

  11. 11.

    Shapley, Lloyd S. (August 21, 1951). “Notes on the n-Person Game -- II: The Value of an n-Person Game” (PDF). Santa Monica, Calif.: RAND Corporation.

  12. 12.

    Read “Cooperative game theory assumes that groups of players, called coalitions, are the primary units of decision-making, and may enforce cooperative behavior.” (Choudhary 2019). https://www.analyticsvidhya.com/blog/2019/11/shapley-value-machine-learning-interpretability-game-theory/

  13. 13.

    Local surrogate models are interpretable models that are used to explain individual predictions of black box machine learning models.

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Authors and Affiliations

  1. Department of Accounting and Information Systems, Rutgers University, Newark, NJ, USA

    Hanxin Hu

  2. Department of Accounting and Information Systems, The College of New Jersey, Ewing, NJ, USA

    Ting Sun

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  1. Hanxin Hu
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  2. Ting Sun
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Correspondence to Ting Sun .

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Editors and Affiliations

  1. Rutgers School of Business, Rutgers, The State University of New Jer, Piscataway, NJ, USA

    Cheng-Few Lee

  2. Phoenix, AZ, USA

    Alice C. Lee

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Hu, H., Sun, T. (2021). The Applications of Machine Learning in Accounting and Auditing Research. In: Lee, CF., Lee, A.C. (eds) Encyclopedia of Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-73443-5_91-1

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