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Bedömning av fakturor med hjälp av maskininlärning
Örebro University, School of Science and Technology.
Örebro University, School of Science and Technology.
2017 (Swedish)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesisAlternative title
Invoice Classification using Machine Learning (English)
Abstract [sv]

Factoring innebär försäljning av fakturor till tredjepart och därmed möjlighet att få in kapital snabbt och har blivit alltmer populärt bland företag idag. Ett fakturaköp innebär en viss kreditrisk för företaget i de fall som fakturan inte blir betald och som köpare av kapital önskar man att minimera den risken. Aros Kapital erbjuder sina kunder tjänsten factoring. Under detta projekt undersöks möjligheten att använda maskininlärningsmetoder för att bedöma om en faktura är en bra eller dålig investering. Om maskininlärningen visar sig vara bättre än manuell hantering kan även bättre resultat uppnås i form av minskade kreditförluster, köp av fler fakturor och därmed ökad vinst. Fyra maskininlärningsmetoder jämfördes: beslutsträd, slumpmässig skog, Adaboost och djupa neurala nätverk. Utöver jämförelse sinsemellan har metoderna jämförts med Aros befintliga beslut och nuvarande regelmotor. Av de jämförda maskininlärningsmetoderna presterade slumpmässig skog bäst och visade sig bättre än Aros

befintliga beslut på de testade fakturorna, slumpmässig skog fick F1-poängen 0,35 och Aros 0,22 .

Abstract [en]

Today, companies can sell their invoices to a third party in order to to quickly capitalize them. This is called factoring. For the financial institute which serve as the third party, the purchase of an invoice infers a certain risk in case the invoice is not paid, a risk the financial institute would like to minimize. Aros Kapital is a financial institute that offers factoring as one of their services. This project at Aros Kapital evaluated the possibility of using machine learning to determine whether or not an invoice will be good investment for the financial institute. If the machine learning algorithm performs better than manual handling and by minimizing credit losses and buying more invoices this could lead to an increase in profit for Aros. Four machine learning algorithms have been compared: decision trees, random forest, Adaboost and deep neural network. Beyond the comparison between the four algorithms, the algorithms were also compared with Aros actual decision and Aros current rule engine solution. The  results show that random forest is the best performing algorithm and it also shows a slight improvement on performance compared to Aros actual decision, random forest got an F1- core of 0.35 and Aros 0.22.

Place, publisher, year, edition, pages
2017. , p. 29
Keyword [en]
machine learning, invoice scoring, credit scoring, random forest, artificial intelligence, deep learning
Keyword [sv]
maskininlärning, fakturabedömning, kreditbedömning, slumpmässig skog, artificiell intelligens, djupinlärning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-64835OAI: oai:DiVA.org:oru-64835DiVA, id: diva2:1180552
Subject / course
Computer Engineering
Presentation
2017-05-30, T101, Örebro universitet, Fakultetsgatan 1, 702 81, Örebro, 12:45 (Swedish)
Supervisors
Examiners
Available from: 2018-02-06 Created: 2018-02-06 Last updated: 2018-02-06Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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