Credit risk evaluation by using nearest subspace method

AbstractIn this paper, a classification method named nearest subspace method is applied for credit risk evaluation. Virtually credit risk evaluation is a very typical classification problem to identify “good” and “bad” creditors. Currently some machine learning technologies, such as support vector machine (SVM), have been discussed widely in credit risk evaluation. But there are many effective classification methods in pattern recognition and artificial intelligence have not been tested for credit evaluation. This paper presents to use nearest subspace classification method, a successful face recognition method, for credit evaluation. The nearest subspace credit evaluation method use the subspaces spanned by the creditors in same class to extend the training set, and the Euclidean distance from a test creditor to the subspace is taken as the similarity measure for classification, then the test creditor belongs to the class of nearest subspace. Experiments on real world credit dataset show that the nearest subspace credit risk evaluation method is a competitive method

Medición de valor en riesgo en cartera de clientes a través de modelos logísticos y simulación de Montecarlo

Los credit score son análisis discriminatorios que proporcionan herramientas de decisión para evaluar los riesgos de crédito en una entidad financiera -- El establecer los parámetros de riesgo de impago de los clientes ayuda a mitigar las pérdidas monetarias que afectan directamente en activos y patrimonio -- Estos parámetros calculados con modelos logísticos, combinados con simulaciones Montecarlo basados en distribuciones Bernoulli y niveles de confianza (VaR) aportan una herramienta dinámica que puede estructurar nuevas políticas de productos financieros y mejoramiento de análisis de pérdida -- Para este trabajo se toma la información de los clientes de una cooperativa de ahorro y crédito ubicada en Armenia – Quindío y de la cual se obtuvo un modelo establecido en 10 variables socioeconómicas las cuales arrojaron un modelo discriminatorio logístico, en el que se la cartera de clientes que aún tienen sus créditos en pago, mostrando el riesgo de impago de la siguiente cuota y su interpretació

What are female SMEs leaders like? Applied study in Costa Rica

El estudio analiza las características de las emprendedoras costarricenses y sus empresas, buscando aportar conocimiento, así como propuestas de apoyo para ese conglomerado. La metodología combina un enfoque cuantitativo a partir de datos del segundo informe nacional de las Pymes del Observatorio de Costa Rica en la materia, así como cualitativo mediante entrevistas a informantes claves. Los resultados muestran cómo las mujeres inician un negocio motivadas principalmente por independencia, en edades entre los 35 y 49 años, su nivel de escolaridad al iniciar es universitario mayormente, desarrollan negocios principalmente en el sector servicios y su participación en las micros, pequeñas y medianas empresas formales del país es de 16.98%. Los principales obstáculos percibidos: su liderazgo y empoderamiento, así como los roles en la sociedad. Como recomendaciones, se plantea implementar programas educativos fomentadores del empoderamiento y programas de acompañamiento con desarrollo de negocios.This study is an analysis of the characteristics of Costar Rican female entrepreneurs and their businesses. The aim is to provide insight and make proposals to support this economic sector. The method involves a quantitative approach based on the data in the second national report by the Observatory of Costa Rican SMEs. It also includes a qualitative approach: Key Informant Interviews. The results show that women start businesses between 35 and 49 years of age, mainly motivated by the idea of independence. Most of them have higher educational level and most of their businesses are conducted in the service sector. Their market share in the formal SMEs in the country is 16.98%. The main challenges they face are leadership and empowerment, as well as their roles in society. The authors recommend the implementation of educational programs that promote empowerment and businesses development support programs

A Hybrid Machine Learning Approach for Credit Scoring Using PCA and Logistic Regression

Credit scoring is one mechanism used by lenders to evaluate risk before extending credit to credit applicants. The method helps distinguish credit worthiness of good credit applicants from the bad credit applicants.  Credit scoring involves a set of decision models and with their underlying techniques helps aid lenders in issuing of consumer credit. Logistic regression (LR) is an adjustment of linear regression with flexibility on its preposition of data and is also able to handle qualitative indicators. The major shortcoming of Logistic regression model is the inability to deal with cooperative (over fitting) effect of the variables. PCA is a feature extraction model that is used to filter out irrelevant un-needed features and hence, it lowers model training time and costs and also increases model performance. This study evaluates the shortcomings of simple models and proposes to develop an efficient and robust machine learning technique combining Logistic and PCA models to evaluate firms in the deposit taking SACCO sector. To achieve this, experimental methodology is adopted.  The proposed hybrid model will be two staged. First stage will be to transform the original variables to get new uncorrelated variables. This will be done using Principal Component Analysis (PCA). Stage two is the use of LR on the principal component values to compute the credit scores. Inferences and conclusions were made based on the analysis of the collected data using Matlab.

Models and methodologies for credit scoring in personal banking: A literature review

Este trabajo pretende aportar literariamente una revisión de los modelos para la calificación del riesgo crediticio (modelos de Credit Score) utilizados en el otorgamiento de crédito personal; teniendo en cuenta los métodos de Abdou & Pointon (2011); Glennon, Kiefer, Larson, & Choi (2008); Saavedra-García & Saavedra-García (2010), se pretende crear un esquema de orden para explicar los múltiples modelos matemáticos y econométricos utilizados en el credit score, con el fin de generar un listado actualizado que esté sustentado por académicos y expertos en el tema.This paper provides a literature review on risk scoring models for credit granting in personal banking. The methods by Abdou & Pointon (2011), Glennon, Kiefer, Larson, & Choi (2008), and Saavedra-García (2010) are considered. The aim is to create a sorting scheme to explain the multiple mathematical and econometrical models used for credit scoring and to produce an up-to-date list supported by scholars and experts in the field

CREDIT SCORING MODELS WITH AUC MAXIMIZATION BASED ON WEIGHTED SVM

Credit scoring models are very important tools for financial institutions to make credit granting decisions. In the last few decades, many quantitative methods have been used for the development of credit scoring models with focus on maximizing classification accuracy. This paper proposes the credit scoring models with the area under receiver operating characteristics curve (AUC) maximization based on the new emerged support vector machines (SVM) techniques. Three main SVM models with different features weighted strategies are discussed. The weighted SVM credit scoring models are tested using 10-fold cross validation with two real world data sets and the experimental results are compared with other six traditional methods including linear regression, logistic regression, k nearest neighbor, decision tree, and neural network. Results demonstrate that weighted 2-norm SVM with radial basis function (RBF) kernel function and t-test feature weighting strategy has the overall better performance with very narrow margin than other SVM models. However, it also consumes more computational time. In considering the balance of performance and time, least squares support vector machines (LSSVM) with RBF kernel maybe a better choice for large scale credit scoring applications.Credit scoring, AUC, SVM, features weighting