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

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

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This paper is a case study of utilizing machine learning for developing a decision-making system for auditors before initializing the audit Console Bracket Assembly 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 FOLDING RAZOR 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.