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

ABSTRACT

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.

Acknowledgments

The authors wish to thank the auditors of audit office for their assistance, time, and continued support. The authors are grateful for their helpful feedbacks and comments on the early version of this work.

Additional information

Funding

This research work is supported by Ministry of Electronics and Information Technology (MEITY), Govt. of India (Grant No. DoRSP/1633).