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Optimizing Fraudulent Firm Prediction Using Ensemble Machine Learning: A Case Study of an External Audit

Hooda, Nishtha ; Bawa, Seema ; Rana, Prashant Singh

Applied artificial intelligence, 2020-01, Vol.34 (1), p.20-30 [Periódico revisado por pares]

Philadelphia: Taylor & Francis

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  • Título:
    Optimizing Fraudulent Firm Prediction Using Ensemble Machine Learning: A Case Study of an External Audit
  • Autor: Hooda, Nishtha ; Bawa, Seema ; Rana, Prashant Singh
  • Assuntos: Algorithms ; Applications programs ; Artificial intelligence ; Case studies ; Classifiers ; Decision making ; Machine learning ; Multiple criterion ; Programming languages
  • É parte de: Applied artificial intelligence, 2020-01, Vol.34 (1), p.20-30
  • Descrição: This paper is a case study of utilizing machine learning for developing a decision-making system for auditors before initializing the audit fieldwork of public firms. Annual data of 777 firms from 14 different sectors are collected and a MCTOPE (Multi criteria ToPsis based Ensemble) framework is implemented to build an ensemble classifier. MCTOPE framework optimizes the performance of classification during ensemble building using the TOPSIS multi-criteria decision-making algorithm. Ensemble machine learning is used for optimizing the prediction performance of suspicious firm predictor in the previous work available at https://www.tandfonline.com/doi/full/10.1080/08839514.2018.1451032 . After achieving an accuracy of 94.6% and AUC (area under the curve) value of 0.98, this ensemble classifier is employed in a web application developed for auditors using Python and R script for the prediction of suspicious firm before planning an external audit. The performance of an ensemble classifier is validated using K-fold cross validation technique and is found to be better than the state-of-the-art classifiers.
  • Editor: Philadelphia: Taylor & Francis
  • Idioma: Inglês
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