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[{"key": "dc.contributor.advisor", "value": "Riekkinen, Janne", "language": "", "element": "contributor", "qualifier": "advisor", "schema": "dc"}, {"key": "dc.contributor.author", "value": "Kokko, Santtu", "language": "", "element": "contributor", "qualifier": "author", "schema": "dc"}, {"key": "dc.date.accessioned", "value": "2024-06-19T08:25:50Z", "language": null, "element": "date", "qualifier": "accessioned", "schema": "dc"}, {"key": "dc.date.available", "value": "2024-06-19T08:25:50Z", "language": null, "element": "date", "qualifier": "available", "schema": "dc"}, {"key": "dc.date.issued", "value": "2024", "language": "", "element": "date", "qualifier": "issued", "schema": "dc"}, {"key": "dc.identifier.uri", "value": "https://jyx.jyu.fi/handle/123456789/96012", "language": null, "element": "identifier", "qualifier": "uri", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Tilinp\u00e4\u00e4t\u00f6stietoja koskevat petokset ovat yksi talouspetosten merkitt\u00e4vimmist\u00e4 ja huomattavimmista petosmuodoista. Tilinp\u00e4\u00e4t\u00f6spetoksia voidaan pit\u00e4\u00e4 eritt\u00e4in merkitt\u00e4v\u00e4n\u00e4 taloudellisen petoksen tyyppin\u00e4, koska niiden aiheuttamat taloudelliset tappiot ovat muihin taloudellisiin petoksiin verrattuna hyvin suuret, ja tilinp\u00e4\u00e4t\u00f6spetokset aiheuttavat paljon negatiivisia vaikutuksia monille eri sidosryhmille. Tutkimukset ovat my\u00f6s osoittaneet, ett\u00e4 perinteisesti ihmisen tekem\u00e4 tilinp\u00e4\u00e4t\u00f6sten tarkastaminen on ep\u00e4tarkkaa ja aikaa viev\u00e4\u00e4 sek\u00e4 vain suhteellisen pieni osuus petoksista onnistutaan havaitsemaan. Lis\u00e4\u00e4ntyneen petosten m\u00e4\u00e4r\u00e4n takia monet eri tahot ovat korostaneet tarvetta tehokkaaseen tilinp\u00e4\u00e4t\u00f6spetosten havaitsemiseen. Tilinp\u00e4\u00e4t\u00f6spetosten havaitsemiseksi onkin kehitetty erilaisia \u00e4lykk\u00e4it\u00e4 ja algoritmeihin pohjautuvia menetelmi\u00e4, joiden avulla pyrit\u00e4\u00e4n tehostamaan tilinp\u00e4\u00e4t\u00f6spetosten havaitsemista. T\u00e4ss\u00e4 kandidaatintutkielmassa tutkittiin, miten erilaiset algoritmeihin pohjautuvat menetelm\u00e4t, kuten koneoppimisen, syv\u00e4oppimisen ja datanlouhinnan menetelm\u00e4t, suoriutuvat tilinp\u00e4\u00e4t\u00f6spetosten havaitsemisessa ja kuinka menetelmien avulla yritykset voidaan luokitella tilinp\u00e4\u00e4t\u00f6ksien perusteella petollisiksi tai ei-petollisiksi. Tutkielman toteutettiin kuvailevana kirjallisuuskatsauksena. Tutkielmassa selvisi, ett\u00e4 tilinp\u00e4\u00e4t\u00f6spetosten havaitsemiseen k\u00e4ytetyill\u00e4 petosindikaattoreilla on huomattava merkitys siihen, kuinka tarkasti yrityksi\u00e4 voidaan luokitella oikein petollisiksi tilinp\u00e4\u00e4t\u00f6ksien perusteella. Huomioitavaa esimerkiksi oli, ett\u00e4 taloudellisten tunnuslukujen k\u00e4yt\u00f6n lis\u00e4ksi oikeinluokittelun tarkkuutta lis\u00e4\u00e4 my\u00f6s ei-taloudelliset tunnusluvut, kuten johdon kommentit liiketoiminnan suoriutumisesta. Yksitt\u00e4isist\u00e4 petoksentunnistusmenetelmist\u00e4 tarkimmaksi osoittautuivat keinotekoisiin neuroverkkoihin perustuvat syv\u00e4oppimisen menetelm\u00e4t, jotka saavuttivat useissa tutkimuksissa l\u00e4hes 95 % oikeinluokittelutarkkuuden tilinp\u00e4\u00e4t\u00f6spetoksissa.", "language": "fi", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.abstract", "value": "Financial statement fraud is one of the most significant and noteworthy forms of financial fraud. They can be considered highly significant due to their substantial costs compared to other types of financial fraud, and they cause numerous negative effects on various stakeholders. Additionally, studies have shown that traditionally human-based financial statement audits are inaccurate and time-consuming, with only a relatively small portion of frauds being successfully detected. As a result of the increased level of fraud, a number of stakeholders have highlighted the need for effective detection of financial statement fraud. Consequently, a range of intelligent and algorithm-based methods have been developed to improve the detection of financial statement fraud. This bachelor's thesis investigated how different algorithm-based methods, such as machine learning, deep learning and data mining methods, perform in detecting financial statement frauds and how these methods can be used to classify companies as fraudulent or non-fraudulent based on their financial statements. The thesis was conducted as a descriptive literature review. The study found that the fraud indicators used for detecting financial statement frauds significantly influence the accuracy of fraud detection. For example, it was noted that in addition to the use of financial indicators, the accuracy of the classification is also enhanced by non-financial indicators, such as management comments on business performance. Among the individual fraud detection methods, deep learning methods based on artificial neural networks proved to be the most accurate, achieving almost 95% accuracy in financial statement fraud classification in several studies.", "language": "en", "element": "description", "qualifier": "abstract", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Submitted by Miia Hakanen (mihakane@jyu.fi) on 2024-06-19T08:25:50Z\nNo. of bitstreams: 0", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.description.provenance", "value": "Made available in DSpace on 2024-06-19T08:25:50Z (GMT). No. of bitstreams: 0\n Previous issue date: 2024", "language": "en", "element": "description", "qualifier": "provenance", "schema": "dc"}, {"key": "dc.format.extent", "value": "38", "language": "", "element": "format", "qualifier": "extent", "schema": "dc"}, {"key": "dc.language.iso", "value": "fin", "language": null, "element": "language", "qualifier": "iso", "schema": "dc"}, {"key": "dc.rights", "value": "In Copyright", "language": "en", "element": "rights", "qualifier": null, "schema": "dc"}, {"key": "dc.subject.other", "value": "tilinp\u00e4\u00e4t\u00f6spetos", "language": "", "element": "subject", "qualifier": "other", "schema": "dc"}, {"key": "dc.title", "value": "Tilinp\u00e4\u00e4t\u00f6spetosten havaitseminen algoritmipohjaisten menetelmien avulla", "language": "", "element": "title", "qualifier": null, "schema": "dc"}, {"key": "dc.type", "value": "bachelor thesis", "language": null, "element": "type", "qualifier": null, "schema": "dc"}, {"key": "dc.identifier.urn", "value": "URN:NBN:fi:jyu-202406194778", "language": "", "element": "identifier", "qualifier": "urn", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Bachelor's thesis", "language": "en", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.type.ontasot", "value": "Kandidaatinty\u00f6", "language": "fi", "element": "type", "qualifier": "ontasot", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Informaatioteknologian tiedekunta", "language": "fi", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.faculty", "value": "Faculty of Information Technology", "language": "en", "element": "contributor", "qualifier": "faculty", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Informaatioteknologia", "language": "fi", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.department", "value": "Information Technology", "language": "en", "element": "contributor", "qualifier": "department", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "Jyv\u00e4skyl\u00e4n yliopisto", "language": "fi", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.contributor.organization", "value": "University of Jyv\u00e4skyl\u00e4", "language": "en", "element": "contributor", "qualifier": "organization", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Tietoj\u00e4rjestelm\u00e4tiede", "language": "fi", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "dc.subject.discipline", "value": "Information Systems Science", "language": "en", "element": "subject", "qualifier": "discipline", "schema": "dc"}, {"key": "yvv.contractresearch.funding", "value": "0", "language": "", "element": "contractresearch", "qualifier": "funding", "schema": "yvv"}, {"key": "dc.type.coar", "value": "http://purl.org/coar/resource_type/c_7a1f", "language": null, "element": "type", "qualifier": "coar", "schema": "dc"}, {"key": "dc.rights.accesslevel", "value": "openAccess", "language": null, "element": "rights", "qualifier": "accesslevel", "schema": "dc"}, {"key": "dc.type.publication", "value": "bachelorThesis", "language": null, "element": "type", "qualifier": "publication", "schema": "dc"}, {"key": "dc.subject.oppiainekoodi", "value": "601", "language": "", "element": "subject", "qualifier": "oppiainekoodi", "schema": "dc"}, {"key": "dc.subject.yso", "value": "tiedonlouhinta", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "syv\u00e4oppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "tilinp\u00e4\u00e4t\u00f6s", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "algoritmit", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.subject.yso", "value": "koneoppiminen", "language": null, "element": "subject", "qualifier": "yso", "schema": "dc"}, {"key": "dc.rights.url", "value": "https://rightsstatements.org/page/InC/1.0/", "language": null, "element": "rights", "qualifier": "url", "schema": "dc"}]
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