In the present work is brought closer to the issue of data mining, and presents the most commonly used techniques and algorithms used for this purpose. Then carried out a practical analysis of selected methods. This paper discusses the problem of credit risk analysis and the possibility of automating the process of making credit decisions using data mining models. The final section compares the effects of different data mining algorithms in applications strictly business. The results obtained models tested were satisfactory, to implement them in making credit decisions.W niniejszej pracy jest przybliżono zagadnienie eksploracji danych oraz przedstawiono najczęściej wykorzystywane techniki i algorytmy stosowane w tym celu. Następnie przeprow...
One of the major challenges facing the retail finance market including banks is the issue of credit ...
The constant need to assess loans makes risk evaluation a very important problem for the banking sec...
This book focuses on the alternative techniques and data leveraged for credit risk, describing and a...
The article presents the basic techniques of data mining implemented in typical commercial software....
This thesis focuses on comparison of selected data mining methods for solving classification tasks w...
In the financial market, banking sector is one of the major sectors. The main objective of a bank is...
Diploma thesis describes credit risks and application of data mining in management of this risk. The...
Nowadays, the three major credit risks at banks and financial institutions are effective in various ...
Any decision to grant loan, it is fraught with risk. If the risk is higher, than the losses caused b...
The interest collected by the main borrowers is collected to pay back the principal borrowed from th...
Behaviour scoring is used in several companies to score the customers according to credit risk by a...
This paper performs a comparative analysis of two kind of methods for extracting credit risk rules. ...
Procjena dobrih ili loših potencijalnih klijenata za kreditiranje, temeljem korištene baze podataka ...
Credit scoring model have been developed by banks and researchers to improve the process of assessin...
This master work describes the most widely used artificial intelligence methods and the possibilitie...
One of the major challenges facing the retail finance market including banks is the issue of credit ...
The constant need to assess loans makes risk evaluation a very important problem for the banking sec...
This book focuses on the alternative techniques and data leveraged for credit risk, describing and a...
The article presents the basic techniques of data mining implemented in typical commercial software....
This thesis focuses on comparison of selected data mining methods for solving classification tasks w...
In the financial market, banking sector is one of the major sectors. The main objective of a bank is...
Diploma thesis describes credit risks and application of data mining in management of this risk. The...
Nowadays, the three major credit risks at banks and financial institutions are effective in various ...
Any decision to grant loan, it is fraught with risk. If the risk is higher, than the losses caused b...
The interest collected by the main borrowers is collected to pay back the principal borrowed from th...
Behaviour scoring is used in several companies to score the customers according to credit risk by a...
This paper performs a comparative analysis of two kind of methods for extracting credit risk rules. ...
Procjena dobrih ili loših potencijalnih klijenata za kreditiranje, temeljem korištene baze podataka ...
Credit scoring model have been developed by banks and researchers to improve the process of assessin...
This master work describes the most widely used artificial intelligence methods and the possibilitie...
One of the major challenges facing the retail finance market including banks is the issue of credit ...
The constant need to assess loans makes risk evaluation a very important problem for the banking sec...
This book focuses on the alternative techniques and data leveraged for credit risk, describing and a...