Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías
La detección de fraudes en entornos financieros es un área crítica cuyo objetivo es identificar patrones o actividades anómalas que puedan indicar prácticas fraudulentas. Se implementa un sistema de detección de fraudes financieros utilizando modelos supervisados, como Random Forest, Logistic Regres...
- Autores:
-
Bustamante Molano, Luisa Ximena
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad Cooperativa de Colombia
- Repositorio:
- Repositorio UCC
- Idioma:
- spa
- OAI Identifier:
- oai:repository.ucc.edu.co:20.500.12494/56439
- Acceso en línea:
- https://hdl.handle.net/20.500.12494/56439
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales
Aprendizaje
Anomalías
Métricas externas
Fraude financiero
Machine learning
Anomalies
External metrics
Financial fraud
- Rights
- closedAccess
- License
- https://creativecommons.org/licenses/by-nc-nd/4.0/
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Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías |
| title |
Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías |
| spellingShingle |
Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías 000 - Ciencias de la computación, información y obras generales Aprendizaje Anomalías Métricas externas Fraude financiero Machine learning Anomalies External metrics Financial fraud |
| title_short |
Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías |
| title_full |
Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías |
| title_fullStr |
Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías |
| title_full_unstemmed |
Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías |
| title_sort |
Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías |
| dc.creator.fl_str_mv |
Bustamante Molano, Luisa Ximena |
| dc.contributor.advisor.none.fl_str_mv |
Gutiérrez Pórtela, Fernando Hernández Aros, Ludivia |
| dc.contributor.author.none.fl_str_mv |
Bustamante Molano, Luisa Ximena |
| dc.subject.ddc.none.fl_str_mv |
000 - Ciencias de la computación, información y obras generales |
| topic |
000 - Ciencias de la computación, información y obras generales Aprendizaje Anomalías Métricas externas Fraude financiero Machine learning Anomalies External metrics Financial fraud |
| dc.subject.proposal.spa.fl_str_mv |
Aprendizaje Anomalías Métricas externas Fraude financiero |
| dc.subject.proposal.eng.fl_str_mv |
Machine learning Anomalies External metrics Financial fraud |
| description |
La detección de fraudes en entornos financieros es un área crítica cuyo objetivo es identificar patrones o actividades anómalas que puedan indicar prácticas fraudulentas. Se implementa un sistema de detección de fraudes financieros utilizando modelos supervisados, como Random Forest, Logistic Regression y Support Vector Machine, así como modelos no supervisados, como Isolation Forest, Local Outlier Factor y One Class Support Vector Machine. El estudio empleó un enfoque de investigación cuantitativo y experimental con alcance explicativo para aplicar técnicas de aprendizaje automático y medir el rendimiento y la evaluación de los modelos. Se utilizó una metodología de software híbrida (espiral y CRISP-DM), la aplicación se desarrolló utilizando Streamlit en un entorno virtual, que incluye módulos de inicio de sesión, detección e informe de resultados. El modelo One-Class SV destaca por su recall (79%), pero tiene una precisión y una puntuación F1 bajas, lo que puede dar lugar a falsos positivos. Por otro lado, la regresión logística muestra un rendimiento más equilibrado con precisión (1%) y recall (25%). Ambos modelos demuestran una capacidad relativamente mejor para identificar casos positivos. |
| publishDate |
2024 |
| dc.date.accessioned.none.fl_str_mv |
2024-07-22T14:18:05Z |
| dc.date.available.none.fl_str_mv |
2024-07-22T14:18:05Z |
| dc.date.issued.none.fl_str_mv |
2024 |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
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http://purl.org/coar/resource_type/c_7a1f |
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Text |
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info:eu-repo/semantics/bachelorThesis |
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http://purl.org/redcol/resource_type/TP |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
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acceptedVersion |
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Bustamante Molano, L. X. (2024). Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías [Tesis de pregrado, Universidad Cooperativa de Colombia]. Repositorio Institucional Universidad Cooperativa de Colombia. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12494/56439 |
| identifier_str_mv |
Bustamante Molano, L. X. (2024). Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías [Tesis de pregrado, Universidad Cooperativa de Colombia]. Repositorio Institucional Universidad Cooperativa de Colombia. |
| url |
https://hdl.handle.net/20.500.12494/56439 |
| dc.language.iso.none.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.none.fl_str_mv |
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Badholia, “PCA based feature extraction and MPSO based feature selection for gene expression microarray medical data classification,” Measurement: Sensors, vol. 31, p. 100945, Feb. 2024, doi: 10.1016/j.measen.2023.100945. |
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Gutiérrez Pórtela, FernandoHernández Aros, LudiviaBustamante Molano, Luisa Ximena2024-07-22T14:18:05Z2024-07-22T14:18:05Z2024Bustamante Molano, L. X. (2024). Detección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalías [Tesis de pregrado, Universidad Cooperativa de Colombia]. Repositorio Institucional Universidad Cooperativa de Colombia.https://hdl.handle.net/20.500.12494/56439La detección de fraudes en entornos financieros es un área crítica cuyo objetivo es identificar patrones o actividades anómalas que puedan indicar prácticas fraudulentas. Se implementa un sistema de detección de fraudes financieros utilizando modelos supervisados, como Random Forest, Logistic Regression y Support Vector Machine, así como modelos no supervisados, como Isolation Forest, Local Outlier Factor y One Class Support Vector Machine. El estudio empleó un enfoque de investigación cuantitativo y experimental con alcance explicativo para aplicar técnicas de aprendizaje automático y medir el rendimiento y la evaluación de los modelos. Se utilizó una metodología de software híbrida (espiral y CRISP-DM), la aplicación se desarrolló utilizando Streamlit en un entorno virtual, que incluye módulos de inicio de sesión, detección e informe de resultados. El modelo One-Class SV destaca por su recall (79%), pero tiene una precisión y una puntuación F1 bajas, lo que puede dar lugar a falsos positivos. Por otro lado, la regresión logística muestra un rendimiento más equilibrado con precisión (1%) y recall (25%). Ambos modelos demuestran una capacidad relativamente mejor para identificar casos positivos.Fraud detection in financial environments is a critical area aimed at identifying patterns or anomalous activities that may indicate fraudulent practices. A financial fraud detection system is implemented using supervised models, such as Random Forest, Logistic Regression and Support Vector Machine, as well as unsupervised models, such as Isolation Forest, Local Outlier Factor and One Class Support Vector Machine. The study employed a quantitative and experimental research approach with explanatory scope to apply machine learning techniques and measure model performance and evaluation. A hybrid software methodology (Spiral and CRISP-DM) was used, the application was developed using Streamlit in a virtual environment, including login, detection, and result reporting modules. The One-Class SV model stands out for its recall (79%), but has a low accuracy and F1 score, which can lead to false positives. On the other hand, logistic regression shows a more balanced performance with precision (1%) and recall (25%). Both models demonstrate a better ability to identify positive cases.Resumen -- Introducción -- 1. Descripción del problema -- 2. Justificación -- 3. Objetivos -- 3.1. Objetivo general -- 3.2. Objetivos específicos -- 4. Marco referencial -- 4.1. Marco teórico -- 4.1.1. Teoría de las anomalías -- 4.1.2. Teoría de la detección de fraudes financieros -- 4.1.3. Teoría del aprendizaje automático -- 4.2. Estado del arte -- 4.3. Marco conceptual -- 4.4. Marco normativo -- 5. Metodología -- 5.1. Primer ciclo: comprensión del negocio -- 5.2. Segundo ciclo: comprensión de los datos -- 5.3. Tercer ciclo: preparación de datos -- 5.4. Cuarto ciclo: modelamiento -- 5.5. Quinto ciclo: evaluación -- 5.6. Sexto ciclo: despliegue -- 6. Resultado -- 6.1. Objetivos del negocio y objetivos minería de datos -- 6.2. Conjunto de datos y preprocesamiento de datos -- 6.3. Evaluación del modelo basado en enfoque de anomalías -- 6.4. Arquitectura sistema detección fraude financiero -- 6.5. Implementación sistema entorno de pruebas -- 7. Conclusiones -- 8. Recomendaciones -- Referencias --PregradoIngeniería de Sistemas70 p.application/pdfspaUniversidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería de Sistemas, IbaguéIngeniería de SistemasIngenieríasIbaguéIbaguéhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://purl.org/coar/access_right/c_14cb000 - Ciencias de la computación, información y obras generalesAprendizajeAnomalíasMétricas externasFraude financieroMachine learningAnomaliesExternal metricsFinancial fraudDetección del fraude financiero mediante la aplicación de técnicas de aprendizaje automático con enfoque basado en anomalíasTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttp://purl.org/redcol/resource_type/TPinfo:eu-repo/semantics/acceptedVersionA. Singh, A. Jain, and S. E. 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