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Yao Zou and Changchun Gao
Credit scoring is an effective tool for banks and lending companies to manage the potential credit risk of borrowers. Machine learning algorithms have made grand progress in automatic and accurate discrimination of good and bad borrowers. Notably, ensemb...
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Mehdi Khashei and Akram Mirahmadi
Risk management is one of the most important branches of business and finance. Classification models are the most popular and widely used analytical group of data mining approaches that can greatly help financial decision makers and managers to tackle cr...
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João A. Bastos
Credit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a classifi...
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Ekaterina V. Orlova
Credit operations are fundamental in the banks? activities and provide a significant share of their income. Under an increased demand for credit resources, credit risks are growth. It keeps the importance of the problem of an increase in the efficiency o...
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Wenjiang Jiao, Xingwei Hao and Chao Qin
CNN is particularly effective in extracting spatial features. However, the single-layer classifier constructed by activation function in CNN is easily interfered by image noise, resulting in reduced classification accuracy. To solve the problem, the adva...
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Naomi Simumba, Suguru Okami, Akira Kodaka and Naohiko Kohtake
Feature selection is crucial to the credit-scoring process, allowing for the removal of irrelevant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improvi...
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Lkhagvadorj Munkhdalai, Keun Ho Ryu, Oyun-Erdene Namsrai and Nipon Theera-Umpon
Credit scoring is a process of determining whether a borrower is successful or unsuccessful in repaying a loan using borrowers? qualitative and quantitative characteristics. In recent years, machine learning algorithms have become widely studied in the d...
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Beibei Niu, Jinzheng Ren and Xiaotao Li
Financial institutions use credit scoring to evaluate potential loan default risks. However, insufficient credit information limits the peer-to-peer (P2P) lending platform?s capacity to build effective credit scoring. In recent years, many types of data ...
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Ioannis E. Livieris, Niki Kiriakidou, Andreas Kanavos, Vassilis Tampakas and Panagiotis Pintelas
Credit scoring is generally recognized as one of the most significant operational research techniques used in banking and finance, aiming to identify whether a credit consumer belongs to either a legitimate or a suspicious customer group. With the vigoro...
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Sergio Edwin Torrico Salamanca
El Credit Scoring es una metodología utilizada en finanzas, para cuantificar el riesgo de crédito de individuos/empresas, este artículo propone la aplicación de esta técnica como una herramienta para medir el riesgo de crédito agregado de los bancos, y d...
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Minerva Arzola, Agustín Mejias
?Vivimos en una sociedad de servicios?. ¿Quien puede imaginar el mundo de hoy sin las telecomunicaciones, el comercio y el transporte, por ejemplo? Las dos últimas décadas han consolidado a la industria de servicios como la responsable del crecimiento ec...
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Cristian Bravo Roman, Sebastian Maldonado, Richard Weber
La gran mayoría de los proyectos de minería de datos que utilizan la metodología KDD en la vida real entregan solamente soluciones estáticas, que con el paso del tiempo pierden la capacidad de explicar los fenómenos para los que fueron construidos inicia...
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Juan Lara Rubio, Manuel Pedro Rodríguez Bolívar, Salvador Rayo Cantón
Pág. 21 - 30
El crecimiento del número de microcréditos junto con las excelentes condiciones para llevar a cabo la actividad microfinanciera en la economía y sistema financiero de la República de Perú están impulsando a las instituciones de microfinanzas (IMF) a una ...
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Ognjen Radovic,Srdan Marinkovic,Jelena Radojicic
Credit scoring attracts special attention of financial institutions. In recent years, deep learning methods have been particularly interesting. In this paper, we compare the performance of ensemble deep learning methods based on decision trees with the b...
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Ognjen Radovic,Srdan Marinkovic,Jelena Radojicic
Pág. 029 - 043
Credit scoring attracts special attention of financial institutions. In recent years, deep learning methods have been particularly interesting. In this paper, we compare the performance of ensemble deep learning methods based on decision trees with the b...
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Silvio Basulto Téllez, MSc.
Pág. 73 - 84
El objetivo de esta investigación es presentar un modelo de evaluación Credit Scoring para perfeccionar la concesión de microcréditos a los Trabajadores por Cuenta Propia, capaz de obtener la probabilidad de incumplimiento con el pago a las sucursales ba...
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Mehdi Khashei and Akram Mirahmadi
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Aida Hanic, Emina ?unic, Adnan D?elihod?ic
Pág. 12 - 27
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Dean Fantazzini
In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the da...
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