The user has requested enhancement of the downloaded file. All in-text references underlined in b... more The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately.
– The aim of this paper is to present how credit scoring models can be used in financial institut... more – The aim of this paper is to present how credit scoring models can be used in financial institutions, in this case in banks, in order to simplify credit lending. Unlike traditional models of credit analysis, scoring models provides valuation based on numerical score who represent clients' possibility to fulfil their obligation. Using credit scoring models, bank can create a numerical snapshot of consumers risk profile. One of the most important characteristic of scoring models is objectivity where two clients with the same characteristics will have the same credit rating. This paper presents some of credit scoring models and the way that financial institutions use them.
Assuming that the credit is one of the most important banking products it follows that the qualit... more Assuming that the credit is one of the most important banking products it follows that the quality assessment of customer creditworthiness is an essential factor for reducing the risk. With the intention to make a good assessment of creditworthiness many models and algorithms have been developed. Data mining algorithms for classification are very suitable for determining the validity of the application for credit. This paper presents an analysis of the effectiveness of the algorithms for classification of credit applications when they are used alone (as single classifier) as well as comparison with ensemble techniques usage. The techniques used as single classifiers are Neural Networks, Decision Trees and Support Vector Machines (SVM), and ensemble techniques AdaBoost and Bagging. K-fold cross-validation is used for model validation. Experiment is conducted in the Bosnian commercial bank dataset and results according to classification parameters such as accuracy and AUC are presented.
The user has requested enhancement of the downloaded file. All in-text references underlined in b... more The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately.
– The aim of this paper is to present how credit scoring models can be used in financial institut... more – The aim of this paper is to present how credit scoring models can be used in financial institutions, in this case in banks, in order to simplify credit lending. Unlike traditional models of credit analysis, scoring models provides valuation based on numerical score who represent clients' possibility to fulfil their obligation. Using credit scoring models, bank can create a numerical snapshot of consumers risk profile. One of the most important characteristic of scoring models is objectivity where two clients with the same characteristics will have the same credit rating. This paper presents some of credit scoring models and the way that financial institutions use them.
Assuming that the credit is one of the most important banking products it follows that the qualit... more Assuming that the credit is one of the most important banking products it follows that the quality assessment of customer creditworthiness is an essential factor for reducing the risk. With the intention to make a good assessment of creditworthiness many models and algorithms have been developed. Data mining algorithms for classification are very suitable for determining the validity of the application for credit. This paper presents an analysis of the effectiveness of the algorithms for classification of credit applications when they are used alone (as single classifier) as well as comparison with ensemble techniques usage. The techniques used as single classifiers are Neural Networks, Decision Trees and Support Vector Machines (SVM), and ensemble techniques AdaBoost and Bagging. K-fold cross-validation is used for model validation. Experiment is conducted in the Bosnian commercial bank dataset and results according to classification parameters such as accuracy and AUC are presented.
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Papers by Adnan Dzelihodzic