The thesis presents a comparison of several selected modeling methods used by financial institutions for (not exclusively) decision-making processes. First theoretical part describes well known modeling methods such as logistic regression, decision trees, neural networks, alternating decision trees and relatively new method called "Random forest". The practical part of thesis outlines some processes within financial institutions, in which selected modeling methods are used. On real data of two financial institutions logistic regression, decision trees and decision forest are compared which each other. Method of neural network is not included due to its complex interpretability. In conclusion, based on resulting models, thesis is trying to answers, whether logistic regression (method most widely used by financial institutions) remains most suitable.

In my thesis I will try to compare the profitability of investment strategies based on the books of the eight famous financial gurus. I'll try to explain the process of selection of stocks to model portfolios, and describe its pitfalls and ideas hidden behind them.I will evaluate the performance of model portfolios under current market conditions based on observation of their development. I will try to clarify the trend observed in stocks moves not only in terms of the criteria of tested strategies, but also in terms of important company news that occurred at the time of observation. I will look on the chosen strategies from the short-term point of view, the observation will last several weeks. The outcome of my work should be my own scoring model for finding undervalued stocks based on chosen strategies and criteria that will appear to be successful within my own observation.

Thesis is providing an overview of available theoretical and practical piece of knowledge related to Credit risk management in a non-banking international organization. It focuses on international aspects of the business, underlying risk, its determination and mitigation. Described methods are then applied to real-life risk assessment of particular customer.

Thesis deals with the problems of life insurance. On the basis of comparison of life insurance products for children of chosen commercial insurance companies, it contains evaluation and proposals for improvement of insurance products for children of Generali pojišťovna, a. s.

This diploma thesis analyses problems connected with motor insurance of the company Česká pojišťovna a.s. and contains the project to improvement the offer of this product in order to be competitive and corresponding to the requirements of client.

No description available.

中小企業是台灣經濟貿易發展的命脈，過去以中小企業為主的出口貿易經濟體系，是創造台灣經濟奇蹟的主要動力。隨著2006年底新巴賽爾協定的正式實施，金融機構為符合新協定規範，亦需將中小企業信用評分程序，納入其徵、授信管理系統，以求信用風險評估皆可量化處理。故本研究將資料採礦技術應用於建置中小企業違約風險模型，針對內部評等法中的企業型暴險，根據新協定與金管會的準則，不僅以財務變數為主，也廣泛增加如企業基本特性及總體經濟因子等非財務變數，納入模型作為考慮變數，計算違約機率進而建置一信用評等系統，作為金融機構對於未來新授信戶之風險管理的參考依據。而本研究將以中小企業中製造傳統產業公司為主要的研究對象，建構企業違約風險模型及其信用評等系統，資料的觀察期間為2003至2005年。 本研究分別利用羅吉斯迴歸、類神經網路、和C&R Tree三種方法建立模型並加以評估比較其預測能力。研究結果發現，經評估確立以1:1精細抽樣比例下，使用羅吉斯迴歸技術建模的效果最佳，共選出六個變數作為企業違約機率模型之建模變數。經驗證後，此模型即使應用到不同期間或其他實際資料，仍具有一定的穩定性與預測效力，且符合新巴塞資本協定與金管會的各項規範，表示本研究之信用評等模型，確實能夠在銀行授信流程實務中加以應用。 / To track the development of Taiwan’s economy history, one very important factor that should never be ignored is the role of small enterprise businesses (the SMEs) which has always been played as a main driving force in the growth of Taiwan’s export trade economic system. With the formal implementation of Basel II in the end of 2006, there arises the need in the banking institutions to establish a credit scoring process for the SMEs into their credit evaluation systems in order to conform to the new accords and to quantify the credit risk assessment process. Consequently, in this research we apply data mining techniques to construct the default risk model for the SMEs in accordance to the new accords and the guidelines published by the FSC (the Financial Supervisory Commission). In addition we not only take the financial variables as the core variables but also increase the non- financial variables such as the enterprise basic characteristics and overall economic factors extensively into the default risk model in order to formulate the probability of credit default risk as well as to establish the credit rating system for the enterprise-based at risk for default in the IRB in the second pillars of the Basel II. The data which used in this research is taken from the traditional SMEs industry ranging from the year of 2003 to 2005. We use each of the following three methods, the Logistic Regression, the Neural Network and the C&R Tree, to build the model. Evaluation of the models is carried out using several statistics test results to compare the prediction accuracy of each model. Based on the result of this research under the 1:1 oversampling proportion, we are inclined to adopt the Logistic Regression techniques modeling as our chosen choice of model. There are six variables being selected from the dataset as the final significant variables in the default risk model. After multiple testing of the model, we believe that this model can withstand the testing for its capability of prediction even when applying in a different time frame or on other data set. More importantly this model is in conformity with the Basel II requirements published by the FSC which makes it even more practical in terms of evaluating credit risk default and credit rating system in the banking industry.

Tarptautinių atsiskaitymų banko (BIS) Bazelio II susitarimo nuostatos dėl bankų minimalaus kapitalo apibrėžia reikalavimus kredito rizikos skaičiavimui. Kredito rizikos vertinimo metodai leidžia naudoti vidines įmonių reitingavimo sistemas. Vienas svarbiausių reitingavimo modelio uždavinių – į modelį parinkti tokius finansinius ar nefinansinius rodiklius, kurie geriausiai klasifikuotų įmones pagal jų finansinio pajėgumo lygį. Populiariausias statistinis atrankos rodiklis yra tikslumo koeficientas dar vadinamas Gini indeksu. Tradicinis Gini indeksas buvo apibrėžtas 1914 m. ir iki šiol yra naudojamas pajamų nelygybei populiacijoje apskaičiuoti. 1995 m. Mosleris ir Koševojus pristatė k-matį Gini indekso analogą kaip zonoido tūrį. Šio darbo tikslas – naudojantis zonoidų teorijos idėjomis sukonstruoti apibendrintą reitingavimo modelių Gini indeksą. Pirmoje darbo dalyje pateiktos tradicinės Lorenco kreivės bei Gini indekso sąvokos ir Gini indekso apibendrinimai. Antroje darbo dalyje pagal BIS naudojamas reitingavimo modelio galios sąvokas, apibrėžtas reitingavimo modelio Gini indeksas. Be to, apibrėžti Lorenco kreivės apatinės ir viršutinės aproksimacijų Gini indeksai bei sudaryti šių indeksų apibendrinimai – normos bei tūrio daugiamačiai Gini indeksai. Pabaigoje analizuojamas atskirų finansinių rodiklių Gini indeksų stabilumas bei bendras Gini indeksų – vienamčio, normos ir tūrio – stabilumas ir pateikiamos išvados. / Bank for International Settlements (BIS) Basel II resolutions on banks regulatory capital include requirements for credit risk calculation. Credit risk evaluation methods define the possibility of using the internal rating system. One of the main tasks to build the powerful scoring model is to select financial and non-financial factors that appropriately classify companies according to their financial situation. The most popular statistical measure used for discriminatory analysis is the accuracy ratio or Gini index. General Gini index presented in 1914 is still widely applied to measure income inequality in the population. The k-dimensional analogue of Gini index as volume of zonoid was defined only in 1995 by Mosler and Koshevoy. The main purpose of this paper is to build the generalized Gini index of scoring model following the theory of zonoids. In the first part of the paper the usual Lorenz curve, traditional Gini index and its summary measures are presented. The second part presents the definition of the scoring models Gini index according to scoring model power measures applied in BIS resolutions. Furthermore the Gini indexes of Lorenz curve bottom and top approximations are defined and two its summary measures – norm and volume Gini indexes are constructed. Finally the stability of separate financial ratios Gini indexes and the general stability of univariate, norm and volume Gini indexes are analysed and final conclusions are presented.

信用風險或信用違約意旨金融機構提供給客戶服務卻未得償還的機率，故其在銀行信貸決策的領域是常被鑽研的對象，因為其對於金融機構所扮演的角色尤其重要，對商業銀行來說更是常難以解釋或控制，然而拜現今進步的科技所賜，金融機構可以藉由操控較過去低的成本即可進一步發展強健且精煉的系統與模型去做預測還有信用風險的控管，有鑑於對客戶的評分自大數據時代來臨起，即使是學生亦開始有了可以評鑑的痕跡，憑藉前人所實驗或仰賴的基本考量面向如客戶基本資料、財力狀況或是其於該公司今昔的借貸訊息，再輔以藉由開放資料所帶來的資訊，發想可能影響信用違約率的變數如外在規範對該客戶的紀錄，想驗證是否真有尚可開發的方向，若有則其影響可以到多深。 眾所皆知從過去到現在即有很多種方法被開創以及提出以預測信用違約率，當然所使用的方法和金融機構本身的複雜性、規模大小以及信貸類型有關，最常見的有判別分析，但其對於變數有嚴格的假設，而新興的方法神經網路可以克服判別分析的缺陷且預測的效能也不錯，但神經網路只給予預測結果而運算過程是未知的，對於想要了解變數間的關係無濟於事，故還是選擇從可以對二元分類做預測亦可以藉由模型係數看到應變數和自變數間關係的羅吉斯迴歸方法著手，而研究過程即是依著前人對於羅吉斯迴歸在信用風險上的繩索摸索，將資料如何清理、變數如何轉換、模型如何建立以及最後如何篩選做一個完整的陳述，縱然長道漫漫，對於研究假設在結果終得驗證也始見曙光，考慮的新面向確有其影響力，而在模型係數上也看到其影響的大小，為了更彰顯羅吉斯迴歸對於變數間提供的訊息，故在最後將研究結果以較文字易讀的視覺化方式作呈現。 / Credit risk or credit default means the probability of non-repayment that banks or financial institutions get after they provide services to their customers. Credit risk is also studied intensively in the field of bank lending strategy because it’s usually hard to interpret and control. However, thanks to advanced technology nowadays, banks can manipulate reduced cost to develop robust and well-trained system and models so as to predict and mange credit risk. In the light of the score on customers from the beginning of big data era, every single one can be tracked to assess even though he or she is student. Relying on common facets like personal information, financial statement and past relationship of loan in a specific bank, come up with possible variables like regulations which influence credit risk according to information from open data. Try to verify if there is a new aspect of modeling and how far it effects. As everyone knows, there are several created and offered methodologies in order to predict credit default. They differ from complexity of banks and institutions, size and type of loan. One of the most popular method is discriminant analysis, but variables are restricted to its assumption. Neural network can fix the flaws of the assumption and work efficiently. Considering the unknown process of calculation in neural network, choose logistic regression as research method which can see the relationship between variables and predict the binary category. With the posterior research on credit risk, make a complete statement about how to clean data, how to transform variables and how to build or screen models. Although the procedure is complicated, the result of this study still validates original hypothesis that new aspect indeed has an impact on credit risk and the coefficient shows how deep it affects.

Master’s thesis deals with analysis and evaluation of all risks for the municipality of Koroužné. An objective of my thesis is suggesting the insurance portfolio on the basis of municipality assets and analysis of risks. It should minimize the incidences of possible realization of the most serious risks of this municipality through the commercial insurance company.