Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Developing a selection of credit scoring models based on customer data
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Utveckling av ett urval av kreditvärdighetsmodeller baserat på kunddata (Swedish)
Abstract [en]

Consumer credits are becoming increasingly popular and widespread in Sweden, with many actors trying to establish themselves on the market. In this thesis, we develop a selection of quantitative models for credit scoring, based on logistic regression and decision trees. These models may be used to reduce the number of credits approved to customers who are likely to default, and are mainly intended for e.g. newly started credit institutes who lack a statistically rigorous credit approval process, relying instead on qualitative, subjective judgements.

Abstract [sv]

Konsumentkrediter blir allt vanligare och populärare i Sverige, med många aktörer som försöker etablera sig på marknaden. I denna avhandling utvecklar vi ett urval av kvantitativa modeller för bedömning av kreditvärdighet, baserade på logistisk regression och beslutsträd. Dessa modeller kan användas för att reducera antalet krediter som ställs ut till kunder med bristande betalningsförmåga, och riktas huvudsakligen till ex. nystartade kreditinstitut som saknar en statistiskt rigorös kreditbedömningsprocess, utan istället förlitar sig på kvalitativa och subjektiva bedömningar.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:373
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-262824OAI: oai:DiVA.org:kth-262824DiVA, id: diva2:1362800
External cooperation
Divide AB
Subject / course
Financial Mathematics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2019-10-30 Created: 2019-10-21 Last updated: 2019-10-30Bibliographically approved

Open Access in DiVA

fulltext(802 kB)18 downloads
File information
File name FULLTEXT01.pdfFile size 802 kBChecksum SHA-512
39cc0d4f875c2904295c167ad0ffc5517406ce7be1d351f9d6a99a39fc46670b4c20947ea81163512585f5efed617d3d1be1d1753c89866b03e71abcef2e54d0
Type fulltextMimetype application/pdf

By organisation
Mathematical Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 18 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 58 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf