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Ozgulbas N.,Baskent University | Koyuncugil A.S.,Capital Markets Board of Turkey
WMSCI 2011 - The 15th World Multi-Conference on Systemics, Cybernetics and Informatics, Proceedings | Year: 2011

The aim of this study is detecting operational risk factors affected financial performance of SMEs by using data mining. For this purpose we used CHAID (Chi-Square Automatic Interaction Detector) decision trees-one of the data mining algorithms -, which is one of the best ways to identify financial profiles of firms and determine operational risk factors. The study covered 1.876 Small and Medium Enterprises (SMEs) in Organized Industrial Region (OIR) of Ankara in 2008. It was found that firms should emphasize the proportion of export to sales, proportion of R&D expenses to sales, ready to Basel-II, power of competition in market, knowledge about Basel-II, partnership status, proportion of energy expenses to total expenses, awareness about finance, using financial consultant, auditing, person responsible from financial management, person responsible from financial strategies. Source


Koyuncugil A.S.,Capital Markets Board of Turkey | Ozgulbas N.,Baskent University
Expert Systems with Applications | Year: 2012

One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an early warning system (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation. © 2011 Elsevier Ltd. All rights reserved. Source


Koyuncugil A.S.,Capital Markets Board of Turkey | Ozgulbas N.,Baskent University
Journal of Medical Systems | Year: 2010

It is very important to identify the appropriate donor in organ transplantation under the time constraint. Clearly, adequate time must be spent in appropriate donor research in that kind of vital operation. On the other hand, time is very important to search for other alternatives in case of inappropriate donor. However, the possibility for determining the most probable donors as fast as possible has an great importance in using time efficiently. From this point view, the main objective of this paper is developing a system which provides probabilistic prior information in donor transplantation via data mining. While the sytem development process, the basic element is the data of successful organ transplantations. Then, the hidden information and patterns will be discovered from this data. Therefore, this process requires the data mining methods from its definition. In this study, an appropriate donor detection system design based on data mining is suggested. © Springer Science + Business Media, LLC 2008. Source


Koyuncugil A.S.,Capital Markets Board of Turkey | Ozgulbas N.,Baskent University
Journal of Medical Systems | Year: 2010

The aims of this study are to provide a standard CUR value, to determine financial and organizational factors which affect the capacity utilization and develop road maps for increasing capacity utilization. To reach these aims by an objective method, we used data mining method that discovers hidden and useful pattern in a large amount of data. Two different method of data mining were used in two stages for this study. In first step, standard value of CUR was determined by K-means Clustering Analysis. CHAID Decision Tree Algorithm as a second method was implemented for determination of impact factors that provided steps for road maps. The study was concerned Turkish Ministry of Health public hospitals. 592 hospitals were covered and financial and operational data of the year 2004 were used in the study. Finally two different road maps were developed and suggestions were made according the results of the study. © 2009 Springer Science+Business Media, LLC. Source


Koyuncugil A.S.,Capital Markets Board of Turkey | Ozgulbas N.,Baskent University
Journal of Medical Systems | Year: 2012

The aim of this study is to develop a Financial Early Warning System (FEWS) for hospitals by using data mining. A data mining method, Chi-Square Automatic Interaction Detector (CHAID) decision tree algorithm, was used in the study for financial profiling and developing FEWS. The study was conducted in Turkish Ministry of Health's public hospitals which were in financial distress and in need of urgent solutions for financial issues. 839 hospitals were covered and financial data of the year 2008 was obtained from Ministry of Health. As a result of the study, it was determined that 28 hospitals (3.34%) had good financial performance, and 811 hospitals (96.66%) had poor financial performance. According to FEWS, the covered hospitals were categorized into 11 different financial risk profiles, and it was found that 6 variables affected financial risk of hospitals. According to the profiles of hospitals in financial distress, one early warning signal was detected and financial road map was developed for risk mitigation. © 2011 Springer Science+Business Media, LLC. Source

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