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Repayment policy for multiple loans

The Repayment Policy for Multiple Loans is about a given set of loans and a monthly incoming cash flow: what is the best way to allocate the monthly income to repay such loans? In this article, we close the almost 20-year-old open question about how to model the repayment policy for multiple loans p...

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Autores principales: Rios-Solis, Yasmin Agueda, Saucedo-Espinosa, Mario Alberto, Caballero-Robledo, Gabriel Arturo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5400254/
https://www.ncbi.nlm.nih.gov/pubmed/28430786
http://dx.doi.org/10.1371/journal.pone.0175782
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author Rios-Solis, Yasmin Agueda
Saucedo-Espinosa, Mario Alberto
Caballero-Robledo, Gabriel Arturo
author_facet Rios-Solis, Yasmin Agueda
Saucedo-Espinosa, Mario Alberto
Caballero-Robledo, Gabriel Arturo
author_sort Rios-Solis, Yasmin Agueda
collection PubMed
description The Repayment Policy for Multiple Loans is about a given set of loans and a monthly incoming cash flow: what is the best way to allocate the monthly income to repay such loans? In this article, we close the almost 20-year-old open question about how to model the repayment policy for multiple loans problem together with its computational complexity. Thus, we propose a mixed integer linear programming model that establishes an optimal repayment schedule by minimizing the total amount of cash required to repay the loans. We prove that the most employed repayment strategies, such as the highest interest debt and the debt snowball methods, are not optimal. Experimental results on simulated cases based on real data show that our methodology obtains on average more than 4% of savings, that is, the debtor pays approximately 4% less to the bank or loaner, which is a considerable amount in finances. In certain cases, the debtor can save up to 40%.
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spelling pubmed-54002542017-05-12 Repayment policy for multiple loans Rios-Solis, Yasmin Agueda Saucedo-Espinosa, Mario Alberto Caballero-Robledo, Gabriel Arturo PLoS One Research Article The Repayment Policy for Multiple Loans is about a given set of loans and a monthly incoming cash flow: what is the best way to allocate the monthly income to repay such loans? In this article, we close the almost 20-year-old open question about how to model the repayment policy for multiple loans problem together with its computational complexity. Thus, we propose a mixed integer linear programming model that establishes an optimal repayment schedule by minimizing the total amount of cash required to repay the loans. We prove that the most employed repayment strategies, such as the highest interest debt and the debt snowball methods, are not optimal. Experimental results on simulated cases based on real data show that our methodology obtains on average more than 4% of savings, that is, the debtor pays approximately 4% less to the bank or loaner, which is a considerable amount in finances. In certain cases, the debtor can save up to 40%. Public Library of Science 2017-04-21 /pmc/articles/PMC5400254/ /pubmed/28430786 http://dx.doi.org/10.1371/journal.pone.0175782 Text en © 2017 Rios-Solis et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rios-Solis, Yasmin Agueda
Saucedo-Espinosa, Mario Alberto
Caballero-Robledo, Gabriel Arturo
Repayment policy for multiple loans
title Repayment policy for multiple loans
title_full Repayment policy for multiple loans
title_fullStr Repayment policy for multiple loans
title_full_unstemmed Repayment policy for multiple loans
title_short Repayment policy for multiple loans
title_sort repayment policy for multiple loans
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5400254/
https://www.ncbi.nlm.nih.gov/pubmed/28430786
http://dx.doi.org/10.1371/journal.pone.0175782
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