Data mining methodology and application in debt collection industries

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 generalize...

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Bibliografiset tiedot
Päätekijä: Rahaman, Md
Muut tekijät: Informaatioteknologian tiedekunta, Faculty of Information Technology, Information Technology, Informaatioteknologia, University of Jyväskylä, Jyväskylän yliopisto
Aineistotyyppi: Pro gradu
Kieli:eng
Julkaistu: 2017
Aiheet:
Linkit: https://jyx.jyu.fi/handle/123456789/54224
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author Rahaman, Md
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Information Technology Informaatioteknologia University of Jyväskylä Jyväskylän yliopisto
author_facet Rahaman, Md Informaatioteknologian tiedekunta Faculty of Information Technology Information Technology Informaatioteknologia University of Jyväskylä Jyväskylän yliopisto Rahaman, Md Informaatioteknologian tiedekunta Faculty of Information Technology Information Technology Informaatioteknologia University of Jyväskylä Jyväskylän yliopisto
author_sort Rahaman, Md
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description 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.
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spellingShingle Rahaman, Md Data mining methodology and application in debt collection industries data mining debtor case prediction and classification methods. Tietotekniikka Mathematical Information Technology 602 tiedonlouhinta velat velallinen analyysi
title Data mining methodology and application in debt collection industries
title_full Data mining methodology and application in debt collection industries
title_fullStr Data mining methodology and application in debt collection industries Data mining methodology and application in debt collection industries
title_full_unstemmed Data mining methodology and application in debt collection industries Data mining methodology and application in debt collection industries
title_short Data mining methodology and application in debt collection industries
title_sort data mining methodology and application in debt collection industries
title_txtP Data mining methodology and application in debt collection industries
topic data mining debtor case prediction and classification methods. Tietotekniikka Mathematical Information Technology 602 tiedonlouhinta velat velallinen analyysi
topic_facet 602 Mathematical Information Technology Tietotekniikka analyysi case data mining debtor prediction and classification methods. tiedonlouhinta velallinen velat
url https://jyx.jyu.fi/handle/123456789/54224 http://www.urn.fi/URN:NBN:fi:jyu-201705312603
work_keys_str_mv AT rahamanmd dataminingmethodologyandapplicationindebtcollectionindustries