Optimizing Fraudulent Firm Prediction Using Ensemble Machine Learning: A Case Study of an External Audit
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 imp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-01-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2019.1680182 |
| Summary: | 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. |
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| ISSN: | 0883-9514 1087-6545 |