By Lean Yu, Shouyang Wang, Kin Keung Lai, Ligang Zhou
Credits threat research is without doubt one of the most crucial issues within the box of monetary chance administration. because of contemporary monetary crises and regulatory challenge of Basel II, credits danger research has been the key concentration of economic and banking undefined. specially for a few credit-granting associations similar to advertisement banks and credits businesses, the facility to discriminate reliable shoppers from undesirable ones is essential. the necessity for trustworthy quantitative types that expect defaults correctly is primary in order that the events can take both preventive or corrective motion. for that reason credits hazard research turns into vitally important for sustainability and revenue of corporations. In such backgrounds, this booklet attempts to combine fresh rising help vector machines and different computational intelligence innovations that copy the foundations of bio-inspired details processing to create a few cutting edge methodologies for credits danger research and to supply determination help details for events.
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Additional info for Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector Machines
4. Performance comparison of different models on different credit datasets Although the performance of all the quantitative models is dependent on the quality of data set, their capabilities to mine the inherent relationship in data set are different. 3, support vector machines always have the best accuracy among the top methods. However, except the quality of data set, there are some other factors that affect the performance of support vector machines, such as model type of support vector machines, kernel function, parameters selection, classifier function or criteria, etc.
Currently the credit-granting institutions are paying much more attention to develop efficient and sophisticated tools to evaluate and control credit risks, which can help them to win more market shares without taking too much risk. In recent two decades, credit scoring is becoming one of the primary methods to develop a credit risk assessment tool. Credit scoring is a method to evaluate the credit risk of loan applicants with their corresponding credit score which is obtained from a credit scoring model.
T. 2). 1). 2) is to transform it into a problem of computing the nearest point between two convex polytopes (U, V) and then use a carefully chosen nearest point to solve it. 2) is equivalent to the following problem of computing the minimum distance between U and V, that is, nearest point minimization problem. t. 9), we know that maximum margin of the boundary hyperplanes 2 =|| u * − v * || and w* = δ (u * − v * ) for the specified δ . 9). , 1974). Recently, another fast and competitive algorithm, Iterative Nearest Point Algorithm (NPA), was proposed by Keerthi et al.
Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector Machines by Lean Yu, Shouyang Wang, Kin Keung Lai, Ligang Zhou