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Artificial Intelligence Approach to Credit Risk
Říha, Jan ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
This thesis focuses on application of artificial intelligence techniques in credit risk management. Moreover, these modern tools are compared with the current industry standard - Logistic Regression. We introduce the theory underlying Neural Networks, Support Vector Machines, Random Forests and Logistic Regression. In addition, we present methodology for statistical and business evaluation and comparison of the aforementioned models. We find that models based on Neural Networks approach (specifically Multi-Layer Perceptron and Radial Basis Function Network) are outperforming the Logistic Regression in the standard statistical metrics and in the business metrics as well. The performance of the Random Forest and Support Vector Machines is not satisfactory and these models do not prove to be superior to Logistic Regression in our application.
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Improved Prediction of Social Tags Using Data Mining
Harár, Pavol ; Galáž, Zoltán (referee) ; Kříž, Jiří (advisor)
This master’s thesis deals with using Text mining as a method to predict tags of articles. It describes the iterative way of handling big data files, parsing the data, cleaning the data and scoring of terms in article using TF-IDF. It describes in detail the flow of program written in programming language Python 3.4.3. The result of processing more than 1 million articles from Wikipedia database is a dictionary of English terms. By using this dictionary one is capable of determining the most important terms from article in corpus of articles. Relevancy of consequent tags proves the method used in this case.
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Default Risk Modeling in Chemistry Industry
Jedlička, Jaromír ; Czekus, Robert (referee) ; Režňáková, Mária (advisor)
My thesis is focused on the presentation of a scoring model for companies in chemical industry with use of cluster analysis methods. There is a description of financial risks, financial analysis indicators and models which are used to evaluate financial risks of a company. There is also a mathematical description of hierarchical cluster methods.
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Credit risk management in banks
Pětníková, Tereza ; Blahová, Naděžda (advisor) ; Šedivý, Jan (referee)
The subject of this diploma thesis is managing credit risk in banks, as the most significant risk faced by banks. The aim of this work is to define the basic techniques, tools and methods that are used by banks to manage credit risk. The first part of this work focuses on defining these procedures and describes the entire process of credit risk management, from the definition of credit risk, describing credit strategy and policy, organizational structure, defining the most used credit risk mitigation tools to the regulatory requirements for credit risk management. The second part gives a more detailed view to credit risk measurement and evaluation and possibilities of credit risk hedging. Last part presents credit risk management in practise illustrated by the example of chosen bank.
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The Estimation of Probability of Default Using Logistic Regression
Chalupa, Tomáš ; Dlouhá, Zuzana (advisor) ; Formánek, Tomáš (referee)
The aim of this work is to develop a suitable model that estimates a probability of default of client's loan. As estimation method was used a logistic regression and a probit regression and two definitions of default, 60 and 90 days overdue. The work describes the method of construction, estimation and testing of scoring models and a structure of dataset, which was used in the practical part. Firstly, it was created a theoretical model that was later confronted with estimates. Estimated models were compared by described statistics as McFadden R^2, the ability to diversify was investigated by the Lorenz curve and by the Gini coefficient. It was found that the logistic and the probit regressions have almost the same results, and that 90 days is preferable definition of default than 60 days.
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Practical application of prediction models
Le Quang, Dung ; Menčlová, Barbora (advisor) ; Škerlíková, Tatiana (referee)
This bachelor thesis is focusing on problems when deciding right business partner based on usage of prediction models, namely Springate's and Altman's models, Neumaiers' indices and Taffler index. The goal is to propose a system that will help with selecting a suitable accounting and tax service provider. The thesis is consisted of two parts. Firstly every model is introduced with its methodology. The second part will be testing of a proposed system on selected accounting and tax providers.
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Processing and utilizing of customer data
Bartelová, Jana ; Kunstová, Renáta (advisor) ; Sládková, Pavlína (referee)
The topic of this master dissertation is data mining of customer data for marketing purposes within an enterprise. The information resulting from this process is then used to create targeted marketing campaigns. Nowadays, identifying and exploiting customer's needs is vital for any enterprise. With that in mind, the theoretical part of this dissertation is focused primarily on different methods of data analysis such as segmentation, profiling, customer scoring and determining customer value. A significant segment of this part focuses on web analysis, which studies customer's web browsing behaviour. The practical part of this dissertation is based on a case study of a specific e-shop. The case study identifies and solves problems of emailing realization. Solving these problems using Silverpop Engage brings new opportunities for emailing. The main goal of this dissertation is to show new opportunities of utilizing behavioural data for e-mailing campaigns execution.
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