dc.contributor.advisor | Terziyan, Vagan | |
dc.contributor.author | Rahaman, Md | |
dc.date.accessioned | 2017-05-31T15:01:17Z | |
dc.date.available | 2017-05-31T15:01:17Z | |
dc.date.issued | 2017 | |
dc.identifier.other | oai:jykdok.linneanet.fi:1702898 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/54224 | |
dc.description.abstract | Collecting debt bills from different types of organizations and people is always a
challenging task. And for that as a debt collector company you need to have a very smart way
to collect money. There has been not been too many research done on analysis of debtor
behavior to get some generalized information in Finland. As a result, most organizations have
to have their own analytics team to set proper business strategy for any debt. We get lots of
classified data from different sources regarding debtor’s basic information, detail debt
information, payment information and many other information. From thousands of variables,
we find out the important variables and build a model. Later data is analyzed with these
models and each case is given a rating value, by which business decisions can be made. And
time to time the cash flow and other business factor is monitored to evaluate the performance
of that model. To conclude, proper and accurate analysis is important before taking any
business decision. | en |
dc.format.extent | 1 verkkoaineisto (59 sivua) | |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.rights | Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. | fi |
dc.rights | This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. | en |
dc.subject.other | data mining | |
dc.subject.other | debtor | |
dc.subject.other | case | |
dc.subject.other | prediction and classification methods. | |
dc.title | Data mining methodology and application in debt collection industries | |
dc.identifier.urn | URN:NBN:fi:jyu-201705312603 | |
dc.type.ontasot | Pro gradu -tutkielma | fi |
dc.type.ontasot | Master’s thesis | en |
dc.contributor.tiedekunta | Informaatioteknologian tiedekunta | fi |
dc.contributor.tiedekunta | Faculty of Information Technology | en |
dc.contributor.laitos | Information Technology | en |
dc.contributor.laitos | Informaatioteknologia | fi |
dc.contributor.yliopisto | University of Jyväskylä | en |
dc.contributor.yliopisto | Jyväskylän yliopisto | fi |
dc.contributor.oppiaine | Tietotekniikka | fi |
dc.contributor.oppiaine | Mathematical Information Technology | en |
dc.date.updated | 2017-05-31T15:01:17Z | |
dc.rights.accesslevel | restrictedAccess | fi |
dc.type.publication | masterThesis | |
dc.contributor.oppiainekoodi | 602 | |
dc.subject.yso | tiedonlouhinta | |
dc.subject.yso | velat | |
dc.subject.yso | velallinen | |
dc.subject.yso | analyysi | |
dc.format.content | fulltext | |
dc.rights.accessrights | Aineistoon pääsyä on rajoitettu tekijänoikeussyistä. Aineisto on luettavissa Jyväskylän yliopiston kirjaston arkistotyöasemalta. Ks. https://kirjasto.jyu.fi/fi/tyoskentelytilat/laitteet-ja-tilat. | fi |
dc.rights.accessrights | This material has a restricted access due to copyright reasons. It can be read at the workstation at Jyväskylä University Library reserved for the use of archival materials: https://kirjasto.jyu.fi/en/workspaces/facilities. | en |
dc.type.okm | G2 | |