Abstract
A computer based decision support system is proposed the basic tasks of which are modeling and forecasting of financial processes and credit risk estimation. The system is developed on the basis of system analysis principles, i.e. it has hierarchic architecture, there exists the possibility for taking into consideration some stochastic and information uncertainties, forming alternatives for models and forecasts, and tracking the computing procedures during all stages of data processing. A modular architecture is implemented that provides a possibility for the further easy enhancement and modification of the system functional possibilities with new forecasting and parameter estimation techniques, and the risk models. High quality of final result is achieved thanks to appropriate tracking of the computing procedures at all stages of data processing: preliminary data processing, model constructing, and forecasts estimation with appropriate sets of statistical quality criteria. Example is given for modeling selected processes and credit risk estimation. The example presented shows that the system developed has good perspectives for practical use.
Highlights
Very often it is possible to reach acceptable quality of forecasting dynamic processes using available on market data processing instruments
The information of a model structure provides a possibility for selection of parameters estimation techniques for candidate models among which are: ordinary and nonlinear LS (NLS), recursive LS (RLS), maximum likelihood (ML), and some versions of Markov Chain Monte Carlo (MCMC)
The results of computing experiments lead to the conclusion that today scoring models and Bayesian networks belong to the set of the best instruments for banking system due to the fact that BN provide a possibility for detecting “bad” clients and to reduce financial risks caused by the clients
Summary
A computer based decision support system is proposed the basic tasks of which are modeling and forecasting of financial processes and credit risk estimation. The system is developed on the basis of system analysis principles, i.e. it has hierarchic architecture, there exists the possibility for taking into consideration some stochastic and information uncertainties, forming alternatives for models and forecasts, and tracking the computing procedures during all stages of data processing. High quality of final result is achieved thanks to appropriate tracking of the computing procedures at all stages of data processing: preliminary data processing, model constructing, and forecasts estimation with appropriate sets of statistical quality criteria. Example is given for modeling selected processes and credit risk estimation. Decision support system; modeling and forecasting of financial processes; credit risk estimation; system analysis principles
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