Explainable Machine Learning Models for Credit Rating in Colombian Solidarity Sector Entities Article Swipe
María Andrea Arias Serna
,
Jhon Jair Quiza Montealegre
,
Luis Fernando Móntes-Gómez
,
Leandro Uribe Clavijo
,
Andrés Orozco‐Duque
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jrfm18090489
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/jrfm18090489
This paper proposes a methodology for implementing a custom-developed explainability model for credit rating using behavioral data registered during the lifecycle of the borrowing that can replicate the score given by the regulatory model for the solidarity economy in Colombia. The methodology integrates continuous behavioral and financial variables from over 17,000 real credit histories into predictive models based on ridge regression, decision trees, random forests, XGBoost, and LightGBM. The models were trained and evaluated using cross-validation and RMSE metrics. LightGBM emerged as the most accurate model, effectively capturing nonlinear credit behavior patterns. To ensure interpretability, SHAP was used to identify the contribution of each feature to the model predictions. The presented model using LightGBM predicted the credit risk assessment in accordance with the regulatory model used by the Colombian Superintendence of the Solidarity Economy, with a root-mean-square error of 0.272 and an R2 score of 0.99. We propose an alternative framework using explainable machine learning models aligned with the internal ratings-based approach under Basel II. Our model integrates variables collected throughout the borrowing lifecycle, offering a more comprehensive perspective than the regulatory model. While the regulatory framework adjusts itself generically to national regulations, our approach explicitly accounts for borrower-specific dynamics.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jrfm18090489
- https://www.mdpi.com/1911-8074/18/9/489/pdf?version=1756824295
- OA Status
- hybrid
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413927010
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4413927010Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jrfm18090489Digital Object Identifier
- Title
-
Explainable Machine Learning Models for Credit Rating in Colombian Solidarity Sector EntitiesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-02Full publication date if available
- Authors
-
María Andrea Arias Serna, Jhon Jair Quiza Montealegre, Luis Fernando Móntes-Gómez, Leandro Uribe Clavijo, Andrés Orozco‐DuqueList of authors in order
- Landing page
-
https://doi.org/10.3390/jrfm18090489Publisher landing page
- PDF URL
-
https://www.mdpi.com/1911-8074/18/9/489/pdf?version=1756824295Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1911-8074/18/9/489/pdf?version=1756824295Direct OA link when available
- Concepts
-
Solidarity, Credit rating, Solidarity economy, Business, Artificial intelligence, Machine learning, Computer science, Financial system, Political science, Law, PoliticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
18Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4413927010 |
|---|---|
| doi | https://doi.org/10.3390/jrfm18090489 |
| ids.doi | https://doi.org/10.3390/jrfm18090489 |
| ids.openalex | https://openalex.org/W4413927010 |
| fwci | 0.0 |
| type | article |
| title | Explainable Machine Learning Models for Credit Rating in Colombian Solidarity Sector Entities |
| biblio.issue | 9 |
| biblio.volume | 18 |
| biblio.last_page | 489 |
| biblio.first_page | 489 |
| topics[0].id | https://openalex.org/T11653 |
| topics[0].field.id | https://openalex.org/fields/14 |
| topics[0].field.display_name | Business, Management and Accounting |
| topics[0].score | 0.9936000108718872 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1402 |
| topics[0].subfield.display_name | Accounting |
| topics[0].display_name | Financial Distress and Bankruptcy Prediction |
| topics[1].id | https://openalex.org/T11652 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9819999933242798 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Imbalanced Data Classification Techniques |
| topics[2].id | https://openalex.org/T11496 |
| topics[2].field.id | https://openalex.org/fields/20 |
| topics[2].field.display_name | Economics, Econometrics and Finance |
| topics[2].score | 0.954200029373169 |
| topics[2].domain.id | https://openalex.org/domains/2 |
| topics[2].domain.display_name | Social Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2003 |
| topics[2].subfield.display_name | Finance |
| topics[2].display_name | Credit Risk and Financial Regulations |
| is_xpac | False |
| apc_list.value | 1200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1299 |
| apc_paid.value | 1200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1299 |
| concepts[0].id | https://openalex.org/C2780641677 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8571773767471313 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q815726 |
| concepts[0].display_name | Solidarity |
| concepts[1].id | https://openalex.org/C205208723 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5801313519477844 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q372765 |
| concepts[1].display_name | Credit rating |
| concepts[2].id | https://openalex.org/C2777576009 |
| concepts[2].level | 4 |
| concepts[2].score | 0.5235064625740051 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1783877 |
| concepts[2].display_name | Solidarity economy |
| concepts[3].id | https://openalex.org/C144133560 |
| concepts[3].level | 0 |
| concepts[3].score | 0.4452369511127472 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q4830453 |
| concepts[3].display_name | Business |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.40773820877075195 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C119857082 |
| concepts[5].level | 1 |
| concepts[5].score | 0.3270486295223236 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[5].display_name | Machine learning |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.31980663537979126 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C73283319 |
| concepts[7].level | 1 |
| concepts[7].score | 0.29356706142425537 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1416617 |
| concepts[7].display_name | Financial system |
| concepts[8].id | https://openalex.org/C17744445 |
| concepts[8].level | 0 |
| concepts[8].score | 0.23296970129013062 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[8].display_name | Political science |
| concepts[9].id | https://openalex.org/C199539241 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[9].display_name | Law |
| concepts[10].id | https://openalex.org/C94625758 |
| concepts[10].level | 2 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7163 |
| concepts[10].display_name | Politics |
| keywords[0].id | https://openalex.org/keywords/solidarity |
| keywords[0].score | 0.8571773767471313 |
| keywords[0].display_name | Solidarity |
| keywords[1].id | https://openalex.org/keywords/credit-rating |
| keywords[1].score | 0.5801313519477844 |
| keywords[1].display_name | Credit rating |
| keywords[2].id | https://openalex.org/keywords/solidarity-economy |
| keywords[2].score | 0.5235064625740051 |
| keywords[2].display_name | Solidarity economy |
| keywords[3].id | https://openalex.org/keywords/business |
| keywords[3].score | 0.4452369511127472 |
| keywords[3].display_name | Business |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.40773820877075195 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/machine-learning |
| keywords[5].score | 0.3270486295223236 |
| keywords[5].display_name | Machine learning |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.31980663537979126 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/financial-system |
| keywords[7].score | 0.29356706142425537 |
| keywords[7].display_name | Financial system |
| keywords[8].id | https://openalex.org/keywords/political-science |
| keywords[8].score | 0.23296970129013062 |
| keywords[8].display_name | Political science |
| language | en |
| locations[0].id | doi:10.3390/jrfm18090489 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210217951 |
| locations[0].source.issn | 1911-8066, 1911-8074 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1911-8066 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of risk and financial management |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1911-8074/18/9/489/pdf?version=1756824295 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Journal of Risk and Financial Management |
| locations[0].landing_page_url | https://doi.org/10.3390/jrfm18090489 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5035423117 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-3089-8993 |
| authorships[0].author.display_name | María Andrea Arias Serna |
| authorships[0].countries | CO |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210133585 |
| authorships[0].affiliations[0].raw_affiliation_string | Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín 050026, Colombia |
| authorships[0].institutions[0].id | https://openalex.org/I4210133585 |
| authorships[0].institutions[0].ror | https://ror.org/030kw0b65 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210133585 |
| authorships[0].institutions[0].country_code | CO |
| authorships[0].institutions[0].display_name | Universidad de Medellín |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | María Andrea Arias-Serna |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín 050026, Colombia |
| authorships[1].author.id | https://openalex.org/A5023755888 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9807-0944 |
| authorships[1].author.display_name | Jhon Jair Quiza Montealegre |
| authorships[1].countries | CO |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210133585 |
| authorships[1].affiliations[0].raw_affiliation_string | Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín 050026, Colombia |
| authorships[1].institutions[0].id | https://openalex.org/I4210133585 |
| authorships[1].institutions[0].ror | https://ror.org/030kw0b65 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4210133585 |
| authorships[1].institutions[0].country_code | CO |
| authorships[1].institutions[0].display_name | Universidad de Medellín |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jhon Jair Quiza-Montealegre |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín 050026, Colombia |
| authorships[2].author.id | https://openalex.org/A5118882067 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Luis Fernando Móntes-Gómez |
| authorships[2].countries | CO |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4210133585 |
| authorships[2].affiliations[0].raw_affiliation_string | Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín 050026, Colombia |
| authorships[2].institutions[0].id | https://openalex.org/I4210133585 |
| authorships[2].institutions[0].ror | https://ror.org/030kw0b65 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210133585 |
| authorships[2].institutions[0].country_code | CO |
| authorships[2].institutions[0].display_name | Universidad de Medellín |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Luis Fernando Móntes-Gómez |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín 050026, Colombia |
| authorships[3].author.id | https://openalex.org/A5118882068 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Leandro Uribe Clavijo |
| authorships[3].countries | CO |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210133585 |
| authorships[3].affiliations[0].raw_affiliation_string | Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín 050026, Colombia |
| authorships[3].institutions[0].id | https://openalex.org/I4210133585 |
| authorships[3].institutions[0].ror | https://ror.org/030kw0b65 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210133585 |
| authorships[3].institutions[0].country_code | CO |
| authorships[3].institutions[0].display_name | Universidad de Medellín |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Leandro Uribe Clavijo |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Faculty of Engineering, Universidad de Medellín, Cra. 87 #30-65, Medellín 050026, Colombia |
| authorships[4].author.id | https://openalex.org/A5076040951 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-8582-8015 |
| authorships[4].author.display_name | Andrés Orozco‐Duque |
| authorships[4].countries | CO |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210141572 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Applied Sciences, Instituto Tecnológico Metropolitano, Cl. 73 #76A-354, Medellín 050034, Colombia |
| authorships[4].institutions[0].id | https://openalex.org/I4210141572 |
| authorships[4].institutions[0].ror | https://ror.org/03zb5p722 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210141572 |
| authorships[4].institutions[0].country_code | CO |
| authorships[4].institutions[0].display_name | Instituto Tecnológico Metropolitano |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Andrés Felipe Orozco-Duque |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Applied Sciences, Instituto Tecnológico Metropolitano, Cl. 73 #76A-354, Medellín 050034, Colombia |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1911-8074/18/9/489/pdf?version=1756824295 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Explainable Machine Learning Models for Credit Rating in Colombian Solidarity Sector Entities |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11653 |
| primary_topic.field.id | https://openalex.org/fields/14 |
| primary_topic.field.display_name | Business, Management and Accounting |
| primary_topic.score | 0.9936000108718872 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1402 |
| primary_topic.subfield.display_name | Accounting |
| primary_topic.display_name | Financial Distress and Bankruptcy Prediction |
| related_works | https://openalex.org/W1981879875, https://openalex.org/W3211833959, https://openalex.org/W2470672336, https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W2343949137, https://openalex.org/W2924660100, https://openalex.org/W4387369504, https://openalex.org/W4394896187, https://openalex.org/W3170094116 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.3390/jrfm18090489 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210217951 |
| best_oa_location.source.issn | 1911-8066, 1911-8074 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1911-8066 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Journal of risk and financial management |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1911-8074/18/9/489/pdf?version=1756824295 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Journal of Risk and Financial Management |
| best_oa_location.landing_page_url | https://doi.org/10.3390/jrfm18090489 |
| primary_location.id | doi:10.3390/jrfm18090489 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210217951 |
| primary_location.source.issn | 1911-8066, 1911-8074 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1911-8066 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of risk and financial management |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1911-8074/18/9/489/pdf?version=1756824295 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Risk and Financial Management |
| primary_location.landing_page_url | https://doi.org/10.3390/jrfm18090489 |
| publication_date | 2025-09-02 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4393043930, https://openalex.org/W4406706869, https://openalex.org/W2278845379, https://openalex.org/W3000463950, https://openalex.org/W3125704451, https://openalex.org/W2056132907, https://openalex.org/W3023813075, https://openalex.org/W4389815667, https://openalex.org/W4220910797, https://openalex.org/W4244230525, https://openalex.org/W2054999048, https://openalex.org/W2082137964, https://openalex.org/W4252548608, https://openalex.org/W4287078451, https://openalex.org/W2962862931, https://openalex.org/W4310360152, https://openalex.org/W3194357095, https://openalex.org/W2768348081 |
| referenced_works_count | 18 |
| abstract_inverted_index.a | 3, 7, 135, 175 |
| abstract_inverted_index.R2 | 142 |
| abstract_inverted_index.To | 92 |
| abstract_inverted_index.We | 146 |
| abstract_inverted_index.an | 141, 148 |
| abstract_inverted_index.as | 81 |
| abstract_inverted_index.by | 30, 126 |
| abstract_inverted_index.in | 38, 119 |
| abstract_inverted_index.of | 21, 102, 130, 138, 144 |
| abstract_inverted_index.on | 58 |
| abstract_inverted_index.to | 98, 105, 190 |
| abstract_inverted_index.II. | 164 |
| abstract_inverted_index.Our | 165 |
| abstract_inverted_index.The | 40, 68, 109 |
| abstract_inverted_index.and | 45, 66, 72, 76, 140 |
| abstract_inverted_index.can | 25 |
| abstract_inverted_index.for | 5, 11, 34, 197 |
| abstract_inverted_index.our | 193 |
| abstract_inverted_index.the | 19, 22, 27, 31, 35, 82, 100, 106, 115, 122, 127, 131, 158, 171, 180, 184 |
| abstract_inverted_index.was | 96 |
| abstract_inverted_index.RMSE | 77 |
| abstract_inverted_index.SHAP | 95 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.data | 16 |
| abstract_inverted_index.each | 103 |
| abstract_inverted_index.from | 48 |
| abstract_inverted_index.into | 54 |
| abstract_inverted_index.more | 176 |
| abstract_inverted_index.most | 83 |
| abstract_inverted_index.over | 49 |
| abstract_inverted_index.real | 51 |
| abstract_inverted_index.risk | 117 |
| abstract_inverted_index.than | 179 |
| abstract_inverted_index.that | 24 |
| abstract_inverted_index.used | 97, 125 |
| abstract_inverted_index.were | 70 |
| abstract_inverted_index.with | 121, 134, 157 |
| abstract_inverted_index.0.272 | 139 |
| abstract_inverted_index.0.99. | 145 |
| abstract_inverted_index.Basel | 163 |
| abstract_inverted_index.While | 183 |
| abstract_inverted_index.based | 57 |
| abstract_inverted_index.error | 137 |
| abstract_inverted_index.given | 29 |
| abstract_inverted_index.model | 10, 33, 107, 111, 124, 166 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.ridge | 59 |
| abstract_inverted_index.score | 28, 143 |
| abstract_inverted_index.under | 162 |
| abstract_inverted_index.using | 14, 74, 112, 151 |
| abstract_inverted_index.17,000 | 50 |
| abstract_inverted_index.credit | 12, 52, 89, 116 |
| abstract_inverted_index.during | 18 |
| abstract_inverted_index.ensure | 93 |
| abstract_inverted_index.itself | 188 |
| abstract_inverted_index.model, | 85 |
| abstract_inverted_index.model. | 182 |
| abstract_inverted_index.models | 56, 69, 155 |
| abstract_inverted_index.random | 63 |
| abstract_inverted_index.rating | 13 |
| abstract_inverted_index.trees, | 62 |
| abstract_inverted_index.adjusts | 187 |
| abstract_inverted_index.aligned | 156 |
| abstract_inverted_index.economy | 37 |
| abstract_inverted_index.emerged | 80 |
| abstract_inverted_index.feature | 104 |
| abstract_inverted_index.machine | 153 |
| abstract_inverted_index.propose | 147 |
| abstract_inverted_index.trained | 71 |
| abstract_inverted_index.Economy, | 133 |
| abstract_inverted_index.LightGBM | 79, 113 |
| abstract_inverted_index.XGBoost, | 65 |
| abstract_inverted_index.accounts | 196 |
| abstract_inverted_index.accurate | 84 |
| abstract_inverted_index.approach | 161, 194 |
| abstract_inverted_index.behavior | 90 |
| abstract_inverted_index.decision | 61 |
| abstract_inverted_index.forests, | 64 |
| abstract_inverted_index.identify | 99 |
| abstract_inverted_index.internal | 159 |
| abstract_inverted_index.learning | 154 |
| abstract_inverted_index.metrics. | 78 |
| abstract_inverted_index.national | 191 |
| abstract_inverted_index.offering | 174 |
| abstract_inverted_index.proposes | 2 |
| abstract_inverted_index.Colombia. | 39 |
| abstract_inverted_index.Colombian | 128 |
| abstract_inverted_index.LightGBM. | 67 |
| abstract_inverted_index.borrowing | 23, 172 |
| abstract_inverted_index.capturing | 87 |
| abstract_inverted_index.collected | 169 |
| abstract_inverted_index.dynamics. | 199 |
| abstract_inverted_index.evaluated | 73 |
| abstract_inverted_index.financial | 46 |
| abstract_inverted_index.framework | 150, 186 |
| abstract_inverted_index.histories | 53 |
| abstract_inverted_index.lifecycle | 20 |
| abstract_inverted_index.nonlinear | 88 |
| abstract_inverted_index.patterns. | 91 |
| abstract_inverted_index.predicted | 114 |
| abstract_inverted_index.presented | 110 |
| abstract_inverted_index.replicate | 26 |
| abstract_inverted_index.variables | 47, 168 |
| abstract_inverted_index.Solidarity | 132 |
| abstract_inverted_index.accordance | 120 |
| abstract_inverted_index.assessment | 118 |
| abstract_inverted_index.behavioral | 15, 44 |
| abstract_inverted_index.continuous | 43 |
| abstract_inverted_index.explicitly | 195 |
| abstract_inverted_index.integrates | 42, 167 |
| abstract_inverted_index.lifecycle, | 173 |
| abstract_inverted_index.predictive | 55 |
| abstract_inverted_index.registered | 17 |
| abstract_inverted_index.regulatory | 32, 123, 181, 185 |
| abstract_inverted_index.solidarity | 36 |
| abstract_inverted_index.throughout | 170 |
| abstract_inverted_index.alternative | 149 |
| abstract_inverted_index.effectively | 86 |
| abstract_inverted_index.explainable | 152 |
| abstract_inverted_index.generically | 189 |
| abstract_inverted_index.methodology | 4, 41 |
| abstract_inverted_index.perspective | 178 |
| abstract_inverted_index.regression, | 60 |
| abstract_inverted_index.contribution | 101 |
| abstract_inverted_index.implementing | 6 |
| abstract_inverted_index.predictions. | 108 |
| abstract_inverted_index.regulations, | 192 |
| abstract_inverted_index.comprehensive | 177 |
| abstract_inverted_index.ratings-based | 160 |
| abstract_inverted_index.explainability | 9 |
| abstract_inverted_index.Superintendence | 129 |
| abstract_inverted_index.cross-validation | 75 |
| abstract_inverted_index.custom-developed | 8 |
| abstract_inverted_index.root-mean-square | 136 |
| abstract_inverted_index.borrower-specific | 198 |
| abstract_inverted_index.interpretability, | 94 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5035423117 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4210133585 |
| citation_normalized_percentile.value | 0.57124251 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |