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Machine learning and spending patterns: A study on the possibility of identifying riskily spending behaviour
KTH, School of Computer Science and Communication (CSC).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Maskininlärning och utgiftsmönster (Swedish)
Abstract [en]

The aim of this study is to research the possibility of using customer transactional data to identify spending patterns among individuals, that in turn can be used to assess creditworthiness. Two different approaches to unsupervised clustering are used and compared in the study, one being K-means and the other an hierarchical approach. The features used in both clustering techniques are extracted from customer transactional data collected from the customers banks. Internal cluster validity indices and credit scores, calculated by credit institutes, are used to evaluate the results of the clustering techniques. Based on the experiments in this report, we believe that the approach exhibit interesting results and that further research with evaluation on a larger dataset is desired. Proposed future work is to append additional features to the models and study the effect on the resulting clusters.

Abstract [sv]

Målet med detta arbete är att studera möjligheten att använda data om individers kontotransaktioner för att identifiera utgiftsmönster hos individer, som i sin tur kan användas för att utvärdera kreditvärdighet. Två olika tillvägagångssätt som använder oövervakad klustring (eng. unsupervised clustering) används och utvärderas i rapporten, den ena är K-means och den andra är en hierarkisk teknik. De attribut (eng. features) som används i de båda klustrings teknikerna utvinns från data som innehåller kontotransaktioner och som erhålls från banker. Interna kluster värde index (eng. cluster validity indices) och individers riskprognoser, som beräknats av ett kreditinstitut, används för att utvärdera resultaten från klustrings teknikerna. Vi menar att resultaten som presenteras i denna rapport visar att målet till viss del uppnåtts, men att mer data och forskning krävs. Vidare forskning som föreslås är att lägga till fler attribut (eng. features) till modellerna och utvärdera effekten på de resulterande klusterna.

Place, publisher, year, edition, pages
2018. , p. 82
Series
TRITA-EECS-EX ; 2018:25
Keywords [en]
Machine learning, Unsupervised learning, clustering, credit assessment, spending patterns
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-222016OAI: oai:DiVA.org:kth-222016DiVA, id: diva2:1178589
Educational program
Master of Science - Machine Learning
Supervisors
Examiners
Available from: 2018-01-30 Created: 2018-01-30 Last updated: 2018-01-30Bibliographically approved

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