Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data

Credit to the private sector has risen rapidly in European emerging markets but its risk evaluation has been largely neglected. Using retail-loan banking data from the Czech Republic we construct two credit risk models based on logistic regression and Classification and Regression Trees. Both methods are comparably efficient and detect similar financial and socio-economic variables as the key determinants of default behavior. We also construct a model without the most important financial variable (amount of resources) that performs very well. This way we confirm significance of socio-demographic variables and link our results with specific issues characteristic to new EU members.credit scoring, discrimination analysis, banking sector, pattern recognition, retail loans, CART, European Union

Default Predictors and Credit Scoring Models for Retail Banking

This paper develops a specification of the credit scoring model with high discriminatory power to analyze data on loans at the retail banking market. Parametric and non- parametric approaches are employed to produce three models using logistic regression (parametric) and one model using Classification and Regression Trees (CART, nonparametric). The models are compared in terms of efficiency and power to discriminate between low and high risk clients by employing data from a new European Union economy. We are able to detect the most important characteristics of default behavior: the amount of resources the client has, the level of education, marital status, the purpose of the loan, and the number of years the client has had an account with the bank. Both methods are robust: they found similar variables as determinants. We therefore show that parametric as well as non-parametric methods can produce successful models. We are able to obtain similar results even when excluding a key financial variable (amount of own resources). The policy conclusion is that socio-demographic variables are important in the process of granting credit and therefore such variables should not be excluded from credit scoring model specification.credit scoring, discrimination analysis, banking sector, pattern recognition, retail loans, CART, European Union

Credit-Scoring Methods (in English)

The paper reviews the best-developed and most frequently applied methods of credit scoring employed by commercial banks when evaluating loan applications. The authors concentrate on retail loans – applied research in this segment is limited, though there has been a sharp increase in the volume of loans to retail clients in recent years. Logit analysis is identified as the most frequent credit-scoring method used by banks. However, other nonparametric methods are widespread in terms of pattern recognition. The methods reviewed have potential for application in post-transition countries.banking sector, credit scoring, discrimination analysis, pattern recognition, retail loans

Comparing Guessing Games with homogeneous and heterogeneous players: Experimental results and a CH explanation

We investigate the decisions of individuals in simple and complex environments. We use a version of the Guessing Game (Beauty-contest Game) as a vehicle for our investigation, employing mathematically talented students. We find that our subjects think in complex environments more carefully before making decisions. We rationalize our findings using the Cognitive Hierarchy (CH) model proposed by Camerer, Ho, and Chong (2002). We relate our results to the emerging literature on the decision making of collective actors.Guessing Game

Default predictors and credit scoring models for retail banking

This paper develops a specification of the credit scoring model with high discriminatory power to analyze data on loans at the retail banking market. Parametric and non- parametric approaches are employed to produce three models using logistic regression (parametric) and one model using Classification and Regression Trees (CART, nonparametric). The models are compared in terms of efficiency and power to discriminate between low and high risk clients by employing data from a new European Union economy. We are able to detect the most important characteristics of default behavior: the amount of resources the client has, the level of education, marital status, the purpose of the loan, and the number of years the client has had an account with the bank. Both methods are robust: they found similar variables as determinants. We therefore show that parametric as well as non-parametric methods can produce successful models. We are able to obtain similar results even when excluding a key financial variable (amount of own resources). The policy conclusion is that socio-demographic variables are important in the process of granting credit and therefore such variables should not be excluded from credit scoring model specification

Server-side navigation service benchmarking tool

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Calibration of Interest Rate Models - Transition Market Case

A methodology to calibrate multifactor interest rate model for transition countries is proposed. The usual methodology of calibration with implied volatility cannot be used as there are no markets for regularly traded derivatives. The existence of such a markets is essential for this calibration. The paradigm used is the Brace-Gatarek- Musiela model of interest rates (Brace, Gatarek and Musiela (1997)), which models the evolution of LIBOR (London InterBank Offered Rate) market interest rates, together with the Orthogonal GARCH model proposed by Alexander (2002), and further generalized by van der Weide (2002). The estimated model is used for the analysis of interest rate markets with shorter-end maturities in the 4 Visegrad countries (Slovak Republic, Czech Republic, Poland and Hungary).Interest rate; Interest rate models, Calibration, Transition countries

Benefits of remote control on the railway lines

Railway transport in the Czech Republic largely uses transit corridors, which are significantly more used than the rest of the railway network. This assumption leads to an increase in the safety of these lines and gradual double-tracking. All these aspects lead to the fact that the given lines are controlled from dispatching centres, while other lines are controlled from regional dispatch offices. The above parameters also apply to other non-corridor but still busy lines. It can therefore be concluded that this trend of remote control brings significant operational and technological advantages. The most visible benefit is the personnel saving of operational employees, but another significant benefit, which is not so obvious at first glance, is the benefit of a more efficient organization and management of railway transport. This impact brings the effect of smoother traffic, which manifests itself in a lower burden on the environment, thanks to fewer train starts and stops, or there will be no complete stop, but only a reduction in train speed. Furthermore, this influence is evident in the more frequent overtaking of trains not in railway stations, but in inter-station sections. All these advantages lead to an increase in an important operational indicator, which is the throughput of the track

Artificial intelligence in operational applications of railway infrastructure manager

The main goal of this article is to present the possibility of using artificial intelligence in operational application of railway infrastructure manager. Railway 4.0 can be controlled fully digitally – operational applications based on the artificial intelligence can optimize the technology of railway traffic control. Thereafter, a wide range of equipment could be controlled by application through a secure interface

Environmental Comparison of Different Transport Modes

The paper describes the energy consumption and GHG production comparison of three transport modes – road, rail and waterborne. The calculations are done according to the legislation in force – standard EN 16 258:2012 Methodology for calculation and declaration of energy consumption and GHG emissions of transport services (freight and passengers). The results have high informative value because they take into account energy consumption and emissions from primary and secondary consideration. The calculation is done by real fuel consumption values (road and waterborne) and by simulation of energy consumption (railway). The energy and emission coefficients from the standard EN were used for estimating the results according to the well-to-wheels and tank-to-wheels principles