Lecture Notes in Networks and Systems
The series “Lecture Notes in Networks and Systems” publishes the latest develop- ments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS.
Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems.
The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output.
The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them.
Credit risk management is of considerable importance for banks, and the most common credit risk models are based on combining client’s private information with credit terms. However, if credit terms are an integral part of initial calculations, then results have to be recalculated for every alteration of credit terms. Thus, banks obtain ‘one-shot’ results from decision support systems that are built with application of these models. In the given paper a credit risk model is proposed. This model is based on a separate analysis of client’s private information and credit terms in order to construct a contour subspace for credit terms that correspond to an equal credit risk value. Application of a proposed model will add advanced options for decision support systems in loan granting, i.e. to visualize a contour subspace of credit terms for a client according to an individual creditworthiness estimation, provide options to choose credit terms from this contour subspace, and manage credit terms on-line according to the dynamics in a creditworthiness estimation.