Zunanje revidiranje in  podatkovna analitika

Zver, Sanja

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Title:	Zunanje revidiranje in podatkovna analitika
Authors:	ID Zver, Sanja (Author) ID Zdolšek, Daniel (Mentor) More about this mentor...
Files:	VS_Zver_Sanja_2021.pdf (1,16 MB) MD5: 61D429936DBF113C3401690974CCB878 PID: 20.500.12556/dkum/f0d2b61c-a5f9-4470-abcc-498cc53e7638
Language:	Slovenian
Work type:	Bachelor thesis/paper
Typology:	2.11 - Undergraduate Thesis
Organization:	EPF - Faculty of Business and Economics
Abstract:	Zaradi tehnologije in tehnološkega napredka ter vse večje digitalizacije poslovanja različnih podjetij sta tudi na področju revidiranja opazna napredek in spremljanje vpeljevanja novih tehnologij, ki bodo v prihodnosti spremenile oziroma prenovile poslovanje z namenom povečanja kakovosti opravljenih storitev revizijskih podjetij. Med najpomembnejše tehnologije za revizijska podjetja spadajo umetna inteligenca, veriženje blokov, internet stvari, robotska avtomatizacija procesov in podatkovna analitika, ki ji v diplomskem delu posvečamo posebno pozornost. Glavna prednost podatkovne analitike je, da omogoča pregled 100 % populacije oziroma množice podatkov revidiranega podjetja in ne le posameznih vzorcev, na katerih je do sedaj temeljilo revidiranje računovodskih izkazov revidiranih podjetij. S tem pa lahko revizijsko podjetje odpravi tveganje pri vzorčenju, ki nastane prav zaradi tega, ker revizorji pri svojem delu ne preizkušajo celotne populacije oziroma množice podatkov, temveč le reprezentativne vzorce. Potreba po podatkovni analitiki je med drugim nastala zaradi vse večjih in obsežnih podatkov, ki so na voljo revizorjem pri revidiranih podjetjih. Revizorji lahko le z uporabo orodij podatkovne analitike omogočijo in v primernem času pregledajo podatke ter s pomočjo orodij prepoznajo trende in anomalije, ki se lahko pojavijo pri posameznih uradnih trditvah poslovodstva, tem pa morajo nameniti posebno pozornost, saj obstaja tveganje za pomembno napačne navedbe v računovodskih izkazih revidiranega podjetja. V raziskovalnem delu smo naprej medsebojno primerjali zaznane koristi in slabosti glede uporabe podatkovne analitike na podlagi mnenj tujih avtorjev, omejili pa smo se na proučevanje le angleških virov. Največjo korist so tuji avtorji izrazili v možnosti analiziranja 100 % populacije podatkov, s čimer je možno v celoti odpraviti tveganje pri vzorčenju določenih revizijskih postopkov. Pri reviziji računovodskih izkazov je podatkovno analitiko mogoče uporabiti pri naslednjih revizijskih postopkih: analitičnih postopkih, preračunavanju oziroma ponovnem izračunavanju, ponovnem izvajanju postopkov in poizvedovanju oziroma izpraševanju. Nekaterih revizijskih postopkov, kot so zunanje potrditve ali fizični pregled sredstev pri revidiranem podjetju, pa ne bo nikoli mogoče 100-odstotno preveriti, saj je namen revizije le-to izvajati učinkovito in gospodarno ter v ustreznem časovnem okvirju. Pri zaznanih slabostih pa je največja težava v neobstoječih standardih. Trenutni standardi niti ne prepovedujejo niti ne predpisujejo uporabe podatkovne analitike. Revizijska podjetja se torej lahko samostojno odločijo, na kakšen način in kako bodo vpeljale uporabo podatkovne analitike pri reviziji računovodskih izkazov. Glavna skrb pa se pojavlja tudi pri varnosti in zasebnosti podatkov, s katerimi bo ravnalo revizijsko podjetje. V raziskovalnem delu ugotavljamo tudi, da je podatkovna analitika že v uporabi v velikih revizijskih podjetjih, ki samostojno razvijajo različne programske rešitve oziroma orodja, ki vključujejo podatkovno analitiko glede na svoje potrebe pri revidiranju. Medtem ko je pri manjših revizijskih podjetjih mogoče zaznati omejitve glede začetne investicije in vlaganja v orodja, preoblikovanje znanja in kompetenc zaposlenih, preoblikovanje revizijskega pristopa k revidiranju ter potencialno izgubo oziroma zmanjšanje obsega obračunanih ur zaradi uporabe avtomatiziranih tehnik podatkovne analitike.
Keywords:	revidiranje, zunanje revidiranje, revizija računovodskih izkazov, tehnologija, podatkovna analitika, prihodnost.
Place of publishing:	[Maribor
Publisher:	S. Zver
Year of publishing:	2021
PID:	20.500.12556/DKUM-79822
UDC:	657.6
COBISS.SI-ID:	81180419
Publication date in DKUM:	18.10.2021
Views:	1309
Downloads:	196
Metadata:
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:	18.08.2021

Secondary language

Language:	English
Title:	External auditing and data analytics
Abstract:	Due to new technologies and technological progress and transition on digitalization in various ways in companies, audit firms are monitoring and adjusting to new technologies that will in the future change and enable new services in auditing. Among the most important technologies for audit firms are artificial intelligence, blockchain, internet of things, robotic process automation and data analytics, to which we are focusing in our diploma thesis. The main advantage of data analytics is that it allows for auditors testing of 100 % of the population, rather than testing on sample data. In this way, the audit firm can eliminate sampling risk, which arises precisely because the auditors in their work do not test the entire population but only representative samples. The need for use of data analytics has among other things arisen due to the growing and extensive data available to auditors at audited companies. Auditors can only use data analytics tools to enable and review data in the appropriate time, and use tools to identify trends and anomalies that may occur in management's official statements, and to focus attention to these, as there is a risk of material misstatement. In the research part we compare the perceived advantages and disadvantages of the use of data analytics in auditing according to foreign authors. We limited ourselves to the study of only English sources. The most important advantage was expressed by foreign authors in the possibility of analysing 100 % of the data population, which makes it possible to eliminate sampling risk in certain audit procedures. In the audit of financial statements, data analytics can be used in the following audit procedures: analytical procedures, recalculations, re-execution of procedures and inquiries. However, some audit procedures, such as external or third-party confirmation of assets and physical inspection, will never be 100 % verifiable, as the purpose of the audit is to be performed efficiently and economically and within an appropriate time frame. The biggest disadvantage in using data analytics authors see in non-existent standards. Current standard neither prohibit nor prescribe the use of data analytics, therefore audit firms can decide themselves in what way and how they will and are using data analytics in auditing. Concern also arises within the security and privacy of the data that the audit firm will handle and the willingness of audited firms to enable full access to auditors. The research also came to the conclusion that data analytics is already being used in large audit firms, which independently develop various software solutions and platforms, which include data analytics in relation to their own needs in auditing. Meanwhile, smaller audit firms may have various barriers regarding making initial investments in tools, transformation of employee skills, transformation of the current audit approach they use, and in potential loss due to reduced billable hours generated by the current audit approach.
Keywords:	auditing, external auditing, financial statement audit, technology, data analytics, future.

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