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Predictive analysis at Krononfogden: Classifying first-time debtors with an uplift model
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The use of predictive analysis is becoming more commonplace with each passing day,

which lends increased credence to the fact that even governmental institutions should

adopt it. Kronofogden is in the middle of a digitization process and is therefore in a

unique position to implement predictive analysis into the core of their operations.

This project aims to study if methods from predictive analysis can predict how many

debts will be received for a first-time debtor, through the use of uplift modeling. The

difference between uplift modeling and conventional modeling is that it aims to

measure the difference in behavior after a treatment, in this case guidance from

Kronofogden. Another aim of the project is to examine whether the scarce literature

about uplift modeling have it right about how the conventional two-model approach

fails to perform well in practical situations.

The project shows similar results as Kronofogden’s internal evaluations. Three

models were compared: random forests, gradient-boosted models and neural

networks, the last performing the best. Positive uplift could be found for 1-5% of the

debtors, meaning the current cutoff level of 15% is too high. The models have several

potential sources of error, however: modeling choices, that the data might not be

informative enough or that the actual expected uplift for new data is equal to zero.

Place, publisher, year, edition, pages
2016. , 70 p.
UPTEC STS, ISSN 1650-8319 ; 16036
Keyword [en]
predictive analysis, machine learning, Kronofogden, uplift, uplift modeling
National Category
Engineering and Technology
URN: urn:nbn:se:uu:diva-303322OAI: oai:DiVA.org:uu-303322DiVA: diva2:971471
Educational program
Systems in Technology and Society Programme
Available from: 2016-09-16 Created: 2016-09-16 Last updated: 2016-09-16Bibliographically approved

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