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Title:Orodja za detekcijo prevar v računovodskih izkazih
Authors:ID Očko, Živa (Author)
ID Kolar, Iztok (Mentor) More about this mentor... New window
Files:.pdf MAG_Ocko_Ziva_2021.pdf (2,20 MB)
MD5: 2E62502D6B0DA990F5AFE392D6D2401D
PID: 20.500.12556/dkum/4b34764c-6cfc-4a85-89b2-c82754babc6f
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:EPF - Faculty of Business and Economics
Abstract:Magistrska naloga se nanaša na področje forenzičnega računovodstva, in sicer na orodja za detekcijo prevar v računovodskih izkazih. Pri tem se nanašamo na raziskave, ki so bile že v preteklosti narejene na to temo, hkrati pa tudi na ugotovitve, ki smo jih pridobili z izvedbo lastne raziskave. Magistrsko delo je sestavljeno iz 5 delov. Najprej predstavimo namen, cilje in eno samo hipotezo magistrske naloge, ki jo skušamo potrditi ali ovreči s pomočjo lastne raziskave. V naslednjem poglavju opredelimo pojem računovodskih izkazov in prevare. Predstavimo različne vrste prevar, indikatorje prevar, na katere moramo biti pozorni in najbolj znano teorijo prevar, ki jo je prvi razložil Donald Ray Creesey. Največji poudarek v tem delu je na teoriji o računovodskih izkazih, povezanih s prevarami. Naslednje poglavje se nanaša na detekcijo prevar v računovodskih izkazih, kjer so povzete predhodno izvedene raziskave. Predstavljen je matematični model Beneish na primeru grških podjetij, Benfordov zakon, uporabljen v Južni Afriki. Predstavljeno pa je tudi orodje diamant, ki so ga uporabili na primeru indonezijskih podjetij, pa tudi različne tehnike rudarjenja podatkov, kot so na primer nevronske mreže in odločitvena drevesa. Tretji del magistrske naloge je najbolj obsežen, saj obsega izvedbo lastne raziskave na vzorcu 40 velikih in srednje velikih slovenskih podjetij. Podjetja so s pomočjo matematičnega modela Beneish razvrščena v dve skupini – skupino A, ki bi naj imela resnične in poštene računovodske izkaze, ter skupino B, ki bi naj manipulirala s svojimi računovodskimi izkazi. S pomočjo različnih statističnih testov smo preverili, ali obstajajo statistično pomembne razlike med skupino A in B. Pri izvedbi lastne raziskave pa nismo uporabili samo Beneish modela, ampak tudi analizo finančnih kazalnikov. Raziskava je bila izvedena po vzoru tuje raziskave, izvedene v Litvi. Analiza raziskave je pokazala, da je kar 44 od 51 analiziranih finančnih kazalnikov občutljivih na prevare v računovodskih izkazih. Glavna ugotovitev raziskave je, da je analiza finančnih kazalnikov dovolj zanesljivo orodje za detekcijo prevar v računovodskih izkazih. Na koncu magistrske naloge pa sledi še sklep ugotovitev.
Keywords:prevare, detekcija prevar, orodja za detekcijo prevar, Beneish model, analiza finančnih kazalnikov.
Place of publishing:Maribor
Year of publishing:2021
PID:20.500.12556/DKUM-79625 New window
UDC:657.6:343.721
COBISS.SI-ID:81584387  New window
Publication date in DKUM:20.10.2021
Views:1324
Downloads:118
Metadata:XML DC-XML DC-RDF
Categories:EPF
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:03.08.2021

Secondary language

Language:English
Title:Financial statement fraud detection tools
Abstract:The master's thesis deals with the field of forensic accounting, or, more specifically, the tools for detecting frauds in financial statements. The thesis is based on the researches already conducted on the topic and on the findings obtained in the course of the implementation of our own research. The master’s thesis is composed of 5 parts. Initially, we introduce the purpose, objectives and a single hypothesis of the master’s thesis that we attempt to confirm or disprove by means of our own research. In the following chapter we define the concept of financial statements and frauds. We introduce various types of frauds, fraud indicators that we must take note of and the best known theory of frauds, first explained by Donald Ray Creesey. The emphasis in this part is on the theory of fraud-related financial statements. The next chapter deals with the detection of frauds in financial statements, which recaps the previously implemented researches. We have presented the Beneish mathematical model on the example of Greek companies and the Benford’s law applied in South Africa. We have also presented the tool diamond, which was used on the example of Indonesian companies, as well as various data mining techniques, such as neural networks and decision trees. The third part of the master’s thesis is the most extensive, being dedicated to the execution of our own research on a sample of 40 large and medium-large Slovenian companies. Using the Beneish mathematical model, companies are classified to two groups – group A, which supposedly has true and fair financial statements, and group B, which supposedly manipulated with their financial statements. Using different statistical tests, we have examined whether there are statistically relevant differences between groups A and B. But in the course of the implementation of our research, we have not applied only the Beneish model, but also the analysis of financial indicators. The research was modelled on a research carried out in Lithuania. The research analysis has shown that as much as 44 out of 51 analysed financial indicators are sensitive to frauds in financial statements. The main finding of the research is that the analysis of financial indicators is a tool reliable enough for detection of frauds in financial statements. The master’s thesis ends with the conclusion.
Keywords:frauds, fraud detection, fraud detection tools, Beneish model, analysis of financial indicators.


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