Journal entry anomaly detection model

• Author: Svjetlana Letinic,Verica Budimir,Mario Zupan
• Published date: 01 October 2020
• DOI: http://doi.org/10.1002/isaf.1485
• Date: 01 October 2020

Received: 19 February 2020Revised: 8 November 2020Acce pted: 8 November 2020

DOI: 10.1002/isaf.1485

RESEARCH ARTICLE

Journalentry anomaly detection model

Mario ZupanVerica BudimirSvjetlana Letinic

Social Department,Polytechnic in Pozega,

Pozega, Croatia

Correspondence

Mario Zupan, So cial Department, Polytec hnic

in Pozega, Pozega,Croatia.

Email: mzupan@vup.hr

Summary

Although numerous scientific papers have been written on deep learning, very few

have been written on the exploitation of such technology in the field of accounting

or bookkeeping. Our scientificstudy is oriented exactly towardthis specificfield.

As accountants, we know the problems faced in modern accounting. Although

accountants may have a plethoraof information regarding technology support,

looking for errors or fraud is a demanding and time-consuming task that depends on

manual skills and professional knowledge. Our efforts are oriented toward resolving

the problem of error-detection automation that is currently possible through new

technologies, and we are trying to develop a web application that will alleviate the

problems of journal entry anomaly detection. Our developed application accepts

data from one specific enterprise resource planning system while also representing

a general software framework for other enterprise resource planning developers.

Our web application is a prototype that uses two of the most popular deep-learning

architectures; namely, a variationalautoencoder and long short-term memory. The

application was tested on two different journals: data set D, learned on accounting

journals from 2007 to 2018 and then tested during the year 2019, and data set H,

learned on journals from 2014 to 2016 and then tested during the year 2017. Both

acco unting jou rnals were ge nerated by micro en trepreneurs.

KEYWORDS

accounting control system,anomaly detection, bookkeeping, deep learning, generalledger

1INTRODUCTION

Croatian small and medium enterprises have a wide pallet of quality

enterprise resource planning (ERP) systems developed by numerous

domestic software companies. As in everyinfo rmationsystem, human

effort, as well as interaction between modules, can cause errors.

Anomalies in accounting books occur on a daily basis, and unin-

tentional human errors, attempted fraud, and continuous legislative

changes are some of the critical causes. Very rare accounting trans-

actions recorded we will call anomalies. The debit side of the equity

account recorded by a VAT document, for instance, is not a stan-

dard procedureand needs to be red-flagged. Anomalies could happen

intentionallyor unintentionally,violating accounting rules or not.Unin-

tentional anomalies occur despitethe f act thatmost existing controls

integrated into accounting modules of modern ERP systems are cre-

ated inco mpliancewith bookkeeping rules. Because smalland medium

enterprises do not have audit obligations regulated by Croatian law ,

manualtax inspections are theo nly mechanism of their accounting and

tax control. In general, the detection oferrors, made intentionally or

not, consumes a largeportion of a bookkeeper'so rtax inspector's time,

and the correction of errors is not an easy part of their job, particu-

larly owing to the architectu rean df unctioning of acco unting software

modules. Namely, most of today's ERP systems have specializeddoc-

uments (digital forms) fo rev ery specific business event. Every digital

form is connected with one or more journal entry schemes created

by senior accountants. Junior accountants or non-accountant employ-

ees do not have to be familiar with journal schemes because they

communicate only through forms. As long as the modern accounting

modules inside ERP systems are functioning based on the princi-

ple described, a single error in only one journal entry scheme can

cause an incorrect accounting entry for a whole set of connected

digital forms.

IntellSys Acc Fin Mgmt. 2020;27:197–209. wileyonlinelibrary.com/journal/isaf© 2020 John Wiley & Sons, Ltd.197