Loss Severity Distribution Estimation Of Operational Risk Using Gaussian Mixture Model For Loss Distribution Approach

Banks must be able to manage all of banking risk; one of them is operational risk. Banks manage operational risk by calculates estimating operational risk which is known as the economic capital (EC). Loss Distribution Approach (LDA) is a popular method to estimate economic capital(EC).This paper propose Gaussian Mixture Model(GMM) for severity distribution estimation of loss distribution approach(LDA). The result on this research is the value at EC of LDA method using GMM is smaller 2 % - 2, 8 % than the value at EC of LDA using existing distribution model

Studi Komparasi Terhadap Kapabilitas Generalisasi Dari Jaringan Saraf Tiruan Berbasis Incremental Projection Learning

One of the essences of supervised learning in neural network is generalization capability. It is an ability to give an accurate result for data that are not learned in learning process. One of supervised learning method that theoretically guarantees the optimal generalization capability is incremental projection learning. This paper will describe an experimental evaluation of generalization capability of the incremental projection learning in neural networks%2C called projection generalizing neural networks%2C for solving function approximation problem. Then%2C Make comparison with other general used neural networks%2C i.e. back propagation networks and radial basis function networks. Base on our experiment%2C projection generalizing neural networks doesn%5C%27t always give better generalization capability than the two other neural networks. It gives better generalization capability when the number of learning data is small enough or the noise variance of learning data is large enough. Otherwise%2C it does not always give better generalization capability. Even though%2C In case the number of learning data is big enough and the noise variance of learning data is small enough%2C projection generalizing neural networks gives worse generalization capability than back propagation network