Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector
Credit risk is among the foremost risk factors that banks may encounter. Banking function is rendering credits. In rendering commercial credits based on fraudulent financial statements, credit risk can occur if banks cannot ensure the repayments of credits completely or...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Isarder
2015-03-01
|
Series: | İşletme Araştırmaları Dergisi |
Subjects: | |
Online Access: | http://isarder.org/isardercom/2015vol7issue1/vol.7_issue.1_article04_full_text.pdf |
id |
doaj-345bff4aa13f440e98612e664b8991aa |
---|---|
record_format |
Article |
spelling |
doaj-345bff4aa13f440e98612e664b8991aa2020-11-25T02:45:11ZengIsarderİşletme Araştırmaları Dergisi1309-07122015-03-01716089Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking SectorMustafa Uğurlu0Şerafettin Sevim1Gazi UniversityDumlupınar UniversityCredit risk is among the foremost risk factors that banks may encounter. Banking function is rendering credits. In rendering commercial credits based on fraudulent financial statements, credit risk can occur if banks cannot ensure the repayments of credits completely or partially. This case lead to an important problem for banks. The accuracy and reliability of information provided by financial statements are of crucial importance in credit risk management. In this context, main purpose of this study is to make it possible to predict fraud risk in financial statements. By doing so, it is possible prevent credit risk that may emerge in banks. In this study, to predict and deter mine fraud risk in financial statements, artificial neural network (ANN) methodology is utilized. This research includes commercial and corporate customers of banks. Financial data of 289 firms, belonging to the year of 2007, (97 firms were assumed to have fraudulent financial statements and 192 firms were in control group) was analyzed, and an ANN model is proposal. The proposal model that was developed is highly successful in predicting fraud risk in financial statements with an accuracy ratio of 90%. http://isarder.org/isardercom/2015vol7issue1/vol.7_issue.1_article04_full_text.pdfFinancial InformationFinancial Statement FraudPrediction of Financial Statement FraudArtificial Neural Networ |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mustafa Uğurlu Şerafettin Sevim |
spellingShingle |
Mustafa Uğurlu Şerafettin Sevim Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector İşletme Araştırmaları Dergisi Financial Information Financial Statement Fraud Prediction of Financial Statement Fraud Artificial Neural Networ |
author_facet |
Mustafa Uğurlu Şerafettin Sevim |
author_sort |
Mustafa Uğurlu |
title |
Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector |
title_short |
Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector |
title_full |
Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector |
title_fullStr |
Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector |
title_full_unstemmed |
Artificial Neural Network Methodology In Fraud Risk Prediction On Financial Statements; An Emprical Study In Banking Sector |
title_sort |
artificial neural network methodology in fraud risk prediction on financial statements; an emprical study in banking sector |
publisher |
Isarder |
series |
İşletme Araştırmaları Dergisi |
issn |
1309-0712 |
publishDate |
2015-03-01 |
description |
Credit risk is among the foremost risk factors that banks may encounter. Banking
function is rendering credits. In rendering commercial credits based on fraudulent
financial statements, credit risk can occur if banks cannot ensure the repayments of
credits completely or partially. This case lead to an important problem for banks. The accuracy and reliability of information provided by financial statements are of crucial importance in credit risk management. In this context, main purpose of this study is to make it possible to predict fraud risk in financial statements. By doing so, it is possible
prevent credit risk that may emerge in banks. In this study, to predict and deter
mine fraud risk in financial statements, artificial neural network (ANN) methodology is
utilized. This research includes commercial and corporate customers of banks. Financial
data of 289 firms, belonging to the year of 2007, (97 firms were assumed to have
fraudulent financial statements and 192 firms were in control group) was analyzed, and
an ANN model is proposal. The proposal model that was developed is highly
successful in predicting fraud risk in financial statements with an accuracy ratio of 90%. |
topic |
Financial Information Financial Statement Fraud Prediction of Financial Statement Fraud Artificial Neural Networ |
url |
http://isarder.org/isardercom/2015vol7issue1/vol.7_issue.1_article04_full_text.pdf |
work_keys_str_mv |
AT mustafaugurlu artificialneuralnetworkmethodologyinfraudriskpredictiononfinancialstatementsanempricalstudyinbankingsector AT serafettinsevim artificialneuralnetworkmethodologyinfraudriskpredictiononfinancialstatementsanempricalstudyinbankingsector |
_version_ |
1724763645672423424 |