Work2018

A financial statement fraud detection model based on hybrid data mining methods

65 citations0/0 files storedInternational Conference on Artificial Intelligence and Big Data (ICAIBD)

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Financial Distress and Bankruptcy Prediction Imbalanced Data Classification Techniques Benford’s Law and Fraud Detection

Abstract

Financial statement fraud has been a difficult problem for both the public and government regulators, so various data mining methods have been used for financial statement fraud detection to provide decision support for stakeholders. The purpose of this study is to propose an optimized financial fraud detection model combining feature selection and machine learning classification. The study indicated that random forest outperformed the other four methods. As to two feature selection methods, Xgboost performed better. And according to our research, 2 or 5 variables are more acceptable for models in this paper.

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References

2011Predicting Material Accounting Misstatements*2017Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods 2010Making words work: Using financial text as a predictor of financial events 2015Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments 2011A business process mining application for internal transaction fraud mitigation 2015Financial Fraud Detection Model: Based on Random Forest 2016Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning 2010A support vector machine-based model for detecting top management fraud

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