A hybrid model based on machine learning and genetic algorithm for detecting fraud in financial statements

نویسندگان

چکیده

Financial statement fraud has increasingly become a serious problem for business, government, and investors. In fact, this threatens the reliability of capital markets, corporate heads, and even the audit profession. Auditors in particular face their apparent inability to detect large-scale fraud, and there are various ways to identify this problem. In order to identify this problem, the majority of the proposed methods are based on existing algorithms and have only attempted to identify human or simple data mining methods that have high overhead and are also costly. The data mining methods presented so far have had high computational overhead or low accuracy. The purpose of this study is to present a model in which an improved ID3 decision tree with a support vector machine is used as a hybrid approach and also to improve the performance and accuracy, genetic algorithm and multilayer perceptron neural networks are applied. More efficient feature selection has been used to reduce computational overhead. The tree proposed in the proposed method has the lowest depth possible and therefore has high velocity and low computational overhead. For this purpose, the financial statements of 151 listed companies in Tehran Stock Exchange during 2014-2015 were surveyed and 125 financial ratios were extracted using ANOVA test, 23 fraud related ratios were selected as model input data. The proposed model has a high accuracy of about 80% of prediction accuracy compared to similar models.

برای دانلود باید عضویت طلایی داشته باشید

برای دسترسی به متن کامل این مقاله و 10 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

The hybrid approach based on genetic algorithm and neural network to predict financial fraud in banks

Audit has become an essential topic in the world because there is much evidence about deliberate manipulations in the reports. One of the concerns in the field of audit is discovery and search of the financial statements and the high volume of bulk data. This study tried to suggest the appropriate method to detect these frauds due to the data which has been available and a proposed algorithm. R...

متن کامل

The hybrid approach based on genetic algorithm and neural network to predict financial fraud in banks

Audit has become an essential topic in the world because there is much evidence about deliberate manipulations in the reports. One of the concerns in the field of audit is discovery and search of the financial statements and the high volume of bulk data. This study tried to suggest the appropriate method to detect these frauds due to the data which has been available and a proposed algorithm. R...

متن کامل

Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm

both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, ...

متن کامل

Detecting Corporate Financial Fraud using Beneish M-Score Model

Detecting financial fraud is an important issue and ignoring this issue may cause financial and non-financial losses to individuals and organizations. The aim of this study is to test the ability of Beneish M-Score Model for detecting financial fraud among companies listed on Tehran stock exchange. The research sample consists of 137 companies listed on Tehran Stock Exchange for a period of 11 ...

متن کامل

MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud

This appendix reports the results for the baseline and yearly/quarterly context-based classifiers when using the 1:10 regulator cost setting. Since the AUC values are computed across different cost settings (and are therefore the same for the investor and regulator situations), we report only the legitimate/fraud recall rates. Overall AUC values as well as results for the investor cost setting ...

متن کامل

A Hybrid Algorithm based on Deep Learning and Restricted Boltzmann Machine for Car Semantic Segmentation from Unmanned Aerial Vehicles (UAVs)-based Thermal Infrared Images

Nowadays, ground vehicle monitoring (GVM) is one of the areas of application in the intelligent traffic control system using image processing methods. In this context, the use of unmanned aerial vehicles based on thermal infrared (UAV-TIR) images is one of the optimal options for GVM due to the suitable spatial resolution, cost-effective and low volume of images. The methods that have been prop...

متن کامل

ذخیره در منابع من

ذخیره در منابع من ذخیره شده در منابع من

{@ msg_add @}

  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی راحت تر خواهید کرد

دانلود متن کامل

برای دسترسی به متن کامل این مقاله و 10 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید


عنوان ژورنال:

دوره 14  شماره 2

صفحات  183- 201

تاریخ انتشار 2021-07-01

با دنبال کردن یک ژورنال هنگامی که شماره جدید این ژورنال منتشر می شود به شما از طریق ایمیل اطلاع داده می شود.

میزبانی شده توسط پلتفرم ابری doprax.com

copyright © 2015-2021