{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:18:23Z","timestamp":1760231903620,"version":"build-2065373602"},"reference-count":23,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00319\/2020"],"award-info":[{"award-number":["UIDB\/00319\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In microfinance, with more loans, there is a high risk of increasing overdue loans by overloading the resources available to take actions on the repayment. So, three experiments were conducted to search for a distribution of the loans through the officers available to maximize the probability of recovery. Firstly, the relation between the loan and some characteristics of the officers was analyzed. The results were not that strong with F1 scores between 0 and 0.74, with a lot of variation in the scores of the good predictions. Secondly, the loan is classified as paid\/unpaid based on what prediction could result of the analysis of the characteristics of the loan. The Support Vector Machine had potential to be a solution with a F1 score average of 0.625; however, when predicting the unpaid loans, it showed to be random with a score of 0.55. Finally, the experiment focused on segmentation of the overdue loans in different groups, from where it would be possible to know their prioritization. The visualization of three clusters in the data was clear through Principal Component Analysis. To reinforce this good visualization, the final silhouette score was 0.194, which reflects that is a model that can be trusted. This way, an implementation of clustering loans into three groups, and a respective prioritization scale would be the best strategy to organize and assign the loans to maximize recovery.<\/jats:p>","DOI":"10.3390\/fi14060163","type":"journal-article","created":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T07:05:07Z","timestamp":1653635107000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimization of the System of Allocation of Overdue Loans in a Sub-Saharan Africa Microfinance Institution"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0688-7303","authenticated-orcid":false,"given":"Andreia","family":"Ara\u00fajo","sequence":"first","affiliation":[{"name":"Algoritmi Centre, University of Minho, 4800-058 Guimaraes, Portugal"},{"name":"Pelican Rhythms, 4100-456 Porto, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2181-6837","authenticated-orcid":false,"given":"Filipe","family":"Portela","sequence":"additional","affiliation":[{"name":"Algoritmi Centre, University of Minho, 4800-058 Guimaraes, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1851-4339","authenticated-orcid":false,"given":"Filipe","family":"Alvelos","sequence":"additional","affiliation":[{"name":"Algoritmi Centre, University of Minho, 4800-058 Guimaraes, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saulo","family":"Ruiz","sequence":"additional","affiliation":[{"name":"Pelican Rhythms, 4100-456 Porto, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, A., Portela, F., Alvelos, F., and Ruiz, S. (2021). Allocation of overdue loans in a Sub-Saharan Africa micronance institution. Lecture Notes in Computer Science (LNCS)\u2014Computational Science and Its Applications\u2014MIKE 2021, Springer LNCS.","DOI":"10.1007\/978-3-031-21517-9_13"},{"key":"ref_2","first-page":"99","article-title":"Bank failure in Nigeria: A consequence of capital inadequacy, lack of transparency and nonperforming loans","volume":"6","author":"Adeyemi","year":"2011","journal-title":"Banks Bank Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1257\/aer.101.5.1676","article-title":"From financial crash to debt crisis","volume":"101","author":"Reinhart","year":"2011","journal-title":"Am. Econ. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.rdf.2018.08.001","article-title":"Determinants of credit risk in the banking system in sub-saharan africa","volume":"8","author":"Mpofu","year":"2018","journal-title":"Rev. Dev. 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