Work2019

Financial Fraudulent Statements Detection through a Deep Dense Artificial Neural Network

Georgios S. Temponeras · Stamatios-Aggelos N. Alexandropoulos · Sotiris B. Kotsiantis · Michael N. Vrahatis

20 citations0/0 files storedInternational Conference on Information, Intelligence, Systems & Applications (IISA)

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Financial Distress and Bankruptcy Prediction Auditing, Earnings Management, Governance Benford’s Law and Fraud Detection

Abstract

A very important issue in the financial field is to identify and reliably predict Fraudulent Financial Statements (FFS). For this purpose, several Machine Learning models have been developed that identify the issues that are directly related to FFS. In this paper, we present a new predictive model for fraudulent detection through a deep dense artificial neural network. Specifically, a new forecasting model was tested experimentally using data from Greek companies. The obtained results showed that the proposed scheme is robust and promising.

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References

2010Detection of financial statement fraud and feature selection using data mining techniques 2018Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature 2017Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods 2002Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques 2015Detecting the financial statement fraud: The analysis of the differences between data mining techniques and experts’ judgments 2016Detecting financial misstatements with fraud intention using multi-class cost-sensitive learning 2017Detecting financial restatements using data mining techniques

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