SELF-LEARNING SCORING MODELS – INTRODUCTION OF AN ON-LINE APPROACH TO RISK ASSESMENT
Ryszard Kozera 1  
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1
Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences (SGGW), Nowoursynowska 159, 02-776 Warsaw, Poland
2
Faculty of Mathematics and Information, Warsaw University of Technology, PL Politechniki 1, 00-661 Warsaw, Poland
Publish date: 2014-06-05
 
Adv. Sci. Technol. Res. J. 2014; 8(22):42–50
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ABSTRACT
The problem considered in this article involves the construction of evaluation model, which could subsequently be used in the fieldof modeling and risk management. The research work is finalizedby a construction of a new model on the basis of observa-tions of the models used for risk management and knowledge of information theory, machine learning and artificialneural networks. The developed tools are trained on-line, using their ability for automatic deduction rules based on data, during model application for evaluation tasks. The model, consequently changes the data analysis stage, limits the scope of the necessary expertise in the area, where the assessment model can be used and, to some extent, the shape of the model becomes independent from the current range of available data. These features increase its ability to general-ize and to cope with the data of previously undefinedclasses, as well as improve its resistance to gaps occurring in the data. Performance of the model presented in this paper is tested and verifiedon the basis of real-life data, which would resemble a potentially real practical application. Preliminary tests performed within the scope of this work indicate that the developed model can form a starting point for further research as some of the used mechanisms have a fairly high efficiency and flexibility.