Browsing by Author "Moberg, Johanna"

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    Artificial Intelligence within Financial Services -In Relation to Data Privacy Regulation
    (2018-08-01) Moberg, Johanna; Olevall, Alexis; University of Gothenburg/Graduate School; Göteborgs universitet/Graduate School
    Background: The data that is processed about individuals is increasing rapidly, which is one contributing factor to the increased usefulness of Artificial Intelligence (AI) within today’s businesses. However, this extensive processing of personal information has become heavily debated, and is an area that the General Data Protection Regulation (GDPR) aims to regulate. At the same time, it has been argued that the formulation of the GDPR is infeasible with AI technology. One industry where an extensive amount of data about customers is processed, including automated processing based on AI technology, is financial services. Purpose and Research Question: The purpose of this research is to examine what impact the GDPR has on AI applications within financial services, and thereby the research question stated is: What is the potential impact of the GDPR on Artificial Intelligence applications within the financial services industry? Methodology: To fulfil the purpose of this research, a qualitative research strategy was applied, including semi-structured interviews with experts within the different fields of examination: law, AI technology and financial services. The findings were analysed through performing a thematic analysis, where coding was conducted in two steps. Findings: AI has many useful applications within financial services, which currently mainly are of the basic form of AI, so-called rule-based systems. However, the more complicated machine learning systems are used in some areas. Based on these findings, the impact of the GDPR on AI applications is assessed by examining different characteristics of the regulation. The GDPR initially imposes both an administrative and compliance burden on organisations within this industry, and is particularly severe when machine learning is used. These burdens foremost stem from the general restriction of processing personal data and the data erasure requirement. However, in the long term, these burdens instead contribute to a positive impact on machine learning. The timeframe until enforcement contributes to a somewhat negative impact in the short term, which is also true for the uncertainty around interpretations of the GDPR requirements. Yet, the GDPR provides flexibility in how to become compliant, which is favourable for AI applications. Finally, GDPR compliance can increase company value, and thereby incentivise investments into AI models of higher transparency. Conclusion: The impact of the GDPR is quite insignificant for the basic forms of AI applications, which are currently most common within financial services. However, for the more complicated applications that are used, the GDPR is found to have a more severe negative impact in the short term, while it instead has a positive impact in the long term. Contribution: This research makes a theoretical contribution to the field of research about the feasibility of the GDPR with technology, by examining how this regulation will impact one specific technology, that is, Artificial Intelligence. This study also makes a practical contribution by reducing the ambiguities for companies about how the GDPR will impact AI applications.
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    Upplysningskvalitet vid nedskrivningsprövning av goodwill och dess påverkan på svenska börsbolags räntekostnad
    (2016-06-16) Grönberg, Karin; Moberg, Johanna; University of Gothenburg/Department of Business Administration; Göteborgs universitet/Företagsekonomiska institutionen
    Bakgrund och problem: Behandlingen av goodwill inom redovisning har länge varit ett omdiskuterat ämne. Företag som redovisar enligt IFRS skall årligen testa goodwill för nedskrivningsbehov. Eftersom IFRS är ett principbaserat regelverk finns möjlighet till subjektiva inslag och vinstmanipulering för företagsledningen. För att minska osäkerheten och informationsasymmetrin för goodwill skall uppgifter lämnas om gjord nedskrivningsprövningen, där värdet av goodwill värderas. Tidigare forskning har påvisat att upplysningskvaliteten utifrån de obligatoriska upplysningarna i IAS 36 har varit bristfällig samt att goodwill utgör en betydande del av svenska företags balansräkningar. Syfte: Uppsatsen syftar till att undersöka om upplysningskvaliteten vid nedskrivningsprövning av goodwill får en påverkan på svenska börsbolags räntekostnad. Metod: En kvantitativ metod med deduktiv ansats har använts. Genom en innehållsanalys har upplysningskvaliteten vid nedskrivningsprövning av goodwill i årsredovisningar utifrån IAS 36 punkt 134 undersökts. Sambandet mellan upplysningskvalitet och räntekostnad har sedan undersökts mellan 2011 och 2013 för företag noterade på Stockholmsbörsen. Sekundärdata används utifrån databasen Retriever och statistiska tester genomfördes genom en regressionsmodell. Resultat och slutsatser: Resultatet av studien påvisar att det finns ett samband mellan upplysningskvalitet av goodwill vid nedskrivningsprövning och räntekostnad. Ökad upplysningskvalitet samvarierar med en lägre räntekostnad. Förslag till fortsatt forskning: Ett förslag till fortsatt studie är att genomföra en kvalitativ studie med kreditgivare för att undersöka i hur stor grad de använder sig av information enligt IAS 36 obligatoriska upplysningskrav.
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    Värdelevansen av icke-reglerade resultatmått - En studie som belyser värdelevansen av EBIT i nordiska noterade bolag
    (2018-06-12) Moberg, Johanna; Sandberg, Nicolas; University of Gothenburg/Department of Business Administration; Göteborgs universitet/Företagsekonomiska institutionen