Credit scoring is perceived as an important innovation for microfinance institutions in managing the credit risk of their clients. Increase in defaults has necessitated microfinance institutions to improve their credit analysis by incorporating objective procedures. Various studies have confirmed that credit scoring can increase efficiency and reduce costs for microfinance. However, the take up...

Unrepresentative data samples are likely to reduce the utility of data classifiers in practical application. This study presents a hybrid mining approach in the design of an effective credit scoring model, based on clustering and neural network techniques. We used clustering techniques to preprocess the input samples with the objective of indicating unrepresentative samples into isolated and in...

Machine Learning is considered as a subfield of Artificial Intelligence and it is concerned with the development of techniques and methods which enable the computer to learn. In this paper introduce SVM. It is techniques and methodologies developed for machine learning tasks Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. S...

With the development of electronic commerce, the credit problem has become a bottleneck for the development of E-commerce, through analysis of E-commerce credit evaluation factors, using the factor analysis and dynamic adjustment mode, construction of electronic commerce credit evaluation model. Through the experimental simulation, the model can effectively reduce the influence of electronic co...

Various types of Technology Credit Guarantees (TCGs) have been issued to support technology development of start-up firms. Technology evaluation has become a critical part of TCG system. However, general technology credit scoring models have not been applied reflecting the special phenomena of start-ups, which are distinguishable from those of established firms. Furthermore, somewhat complicate...

Aiming at the insufficiency of credit scoring models, this paper puts out a new approach by using combining forecast model for personal credit scoring. Based on linear regression and logistic regression models, this paper constructed a combining model and used particle swarm optimization (PSO) to search each single model’s weight. In order to control the type II error rate,the particle’s fitnes...

This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as ...

Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic ...

An integrated approach of system dynamics simulation and fuzzy inference system for retailers’ credit scoring Vahid Baradaran & Maryam Keshavarz To cite this article: Vahid Baradaran & Maryam Keshavarz (2015) An integrated approach of system dynamics simulation and fuzzy inference system for retailers’ credit scoring, Economic Research-Ekonomska Istraživanja, 28:1, 959-980, DOI: 10.1080/1331677...

Credit scoring models are important tools in the credit granting process. These models measure the credit risk of a prospective client based on idiosyncratic variables and macroeconomic factors. However, small and medium sized enterprises (SMEs) are subject to the effects of the local economy. From a data set with the localization and default information of 9 million Brazilian SMEs, provided by...