Algorithmic scoring models

Nurlybayeva, K. K.; Balakayeva, Gulnar

doi:10.12988/ams.2013.13048

Cited by 6 publications

(5 citation statements)

References 5 publications

(4 reference statements)

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“…A remarkable, large and essential literature review was presented in the paper by Hand and Henley (1997), which discuss important issues of classification methods applied to credit scoring. Other literature reviews were also conducted but only focused on some types of classification methods and discussion of the methodologies, namely Xu et al (2009), Shi (2010), Lahsasna et al (2010a) and Nurlybayeva and Balakayeva (2013). Also, Garcia et al (2014) performed a systematic literature review, but limiting the study to papers published between 2000 and 2013, these authors provided a short experimental framework comparing only four credit scoring methods.…”

Section: Introductionmentioning

confidence: 99%

Classification methods applied to credit scoring: Systematic review and overall comparison

Louzada

Ara

Fernandes³

2016

Surveys in Operations Research and Management Science

133

121

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The need for controlling and effectively managing credit risk has led financial institutions to excel in improving techniques designed for this purpose, resulting in the development of various quantitative models by financial institutions and consulting companies. Hence, the growing number of academic studies about credit scoring shows a variety of classification methods applied to discriminate good and bad borrowers. This paper, therefore, aims to present a systematic literature review relating theory and application of binary classification techniques for credit scoring financial analysis. The general results show the use and importance of the main techniques for credit rating, as well as some of the scientific paradigm changes throughout the years.

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Section: Introductionmentioning

confidence: 99%

Classification methods applied to credit scoring: Systematic review and overall comparison

Louzada

Ara

Fernandes³

2016

Surveys in Operations Research and Management Science

133

121

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“…En los últimos años se ha venido trabajando en diferentes técnicas para abordar el riesgo crédito, por ejemplo la utilización de sistemas de inferencia borrosa en el análisis de solvencia de un cliente para pagar un crédito [7], el uso de redes de neuronas artificiales con el fin de generar un nuevo enfoque de asignación de créditos [8], la búsqueda de modelos de calificación mediante data minig [9] y el modelamiento de la dinámica que contiene la asignación de créditos en una organización [10].…”

Section: Iunclassified

“…De esta manera cualquier registro que tuviera una aprobación diferente a la predominancia del cluster, le sería asignado un valor promedio de score representativo para el cluster, haciendo que cada registro tome la predominancia del grupo en la aprobación [13] [14]. Lo anterior genera una segunda subestructura genética, que determina la segunda etapa en la construcción del modelo: w 1 w 2 …….. w NO Para evaluar la calidad de esta subestructura con respecto a la construcción del modelo de score (TKS), la función de aptitud se define: (9) Dónde: nd: indica el número de clientes que conforman la base de datos para el aprendizaje. FA 2 : indica la función de aptitud para un individuo, en términos del inverso del error cuadrático medio.…”

Section: U K+1unclassified

Fuzzy model Takagi Sugeno with structured evolution for determining consumer credit score

Perez

Peña

Lochmuller

2015

2015 10th Iberian Conference on Information Systems and Technologies (CISTI)

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Resumen -La calificación (score) y el conocimiento del comportamiento de pago de un cliente para reducir el tiempo de otorgamiento de un crédito de consumo es una de las necesidades que poseen las entidades financieras dedicadas a brindar este tipo de servicios. Para la calificación de los clientes, estas entidades se basan en información cualitativa y cuantitativa de un cliente, lo que hace difícil una calificación homogénea. Debido a la necesidad de disminuir los tiempos de respuesta con respecto a la aprobación o rechazo de una solicitud de crédito, es importante la utilización de modelos que ayuden al análisis de un crédito en tiempo real. Por esto, en este artículo se desarrolla y analiza un modelo basado en los principios de la computación evolutiva, y en los principios de un modelo borroso del tipo Takagi Sugeno, para la estimación del score en la asignación de créditos de consumo. Para la optimización del aprendizaje, el modelo propuesto se ve sometido a un proceso por evolución, basado en el modelo EVOP, el cual guía el aprendizaje del modelo, en torno a dos parámetros como son: el parámetro de generación, y el parámetro de mutación, generando así una evolución estructurada que llevará al modelo, a diferentes estados en el aprendizaje. Los resultados obtenidos por el modelo propuesto, permiten disminuir el tiempo de otorgamiento de un crédito de consumo, al igual que permitió evidenciar la sensibilidad del modelo frente al score, de acuerdo con la variación del monto solicitado por un cliente. Palabras Clave -Mapa de Kohonen; Agrupación Neuronal; Calicficación; Takagi Sugeno, Capacidad de Pago, Estrategias por Evolución, EVOP.Abstract -The rating (score) and knowledge of the payment behavior of a client to reduce the time of granting of consumer credit is one of the requirements that financial institutions have dedicated to providing these services. For qualifying customers, these entities are based on qualitative and quantitative information of a client, making it difficult a homogeneous rating. Because of the need to reduce response times regarding the approval or rejection of a credit application is important to use models that help to analyze a real-time credit. Hence, this paper develops and analyzes based on the principles of evolutionary computation model, and the principles of a fuzzy Takagi Sugeno type model, to estimate the score in the allocation of consumer loans. To optimize learning, the proposed model is subjected to a process of evolution, based on the EVOP model, which guides learning model, based on two parameters such as: the generation parameter and the parameter mutation thus generating a structured evolution that will take the model to different states in learning. The results obtained by the proposed model allows to decrease the time of granting of consumer credit, as allowed demonstrate the sensitivity of the model against the score, according to the variation of the amount requested by a customer.

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“…Where Score is defined as value assigned to the borrower and reflecting its relative creditworthiness (Nurlybayeva & Balakayeva, 2013 ). This approach, however, does not give an answer to the question of the probability of default of the 1 st Borrower relative to the 2 nd Borrower.…”

Section: Introductionmentioning

confidence: 99%

Some Approaches to the Calibration of Internal Rating Models

Rudakova¹,

Ipatyev²

2015

RES

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This article covers the peculiarities of calibration of internal rating models which are the most popular approach to assessing credit risks. The authors address the most common approaches and methods used for rating models calibration, as well as propose their own algorithm for calibration, the main feature of which is taking into account the forecasted probability of default on the portfolio. Research methods include regression analysis, time series analysis (ARIMA models development). Reliability of the proposed approach has been verified on the basis of the portfolio, based on the debt obligations of Russia financial institutions. The advantage of the proposed approach towards determination of the average default probability of credit portfolio is that one gets a tool that allows you to construct a rating model that is "forward looking", respectively, appear to more quickly adapt to the changing patterns of rating environment.

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Algorithmic scoring models

Cited by 6 publications

References 5 publications

Classification methods applied to credit scoring: Systematic review and overall comparison

Classification methods applied to credit scoring: Systematic review and overall comparison

Fuzzy model Takagi Sugeno with structured evolution for determining consumer credit score

Some Approaches to the Calibration of Internal Rating Models

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