IDEAS home Printed from https://ideas.repec.org/a/aio/aucsse/v3y2011i39p174-185.html
   My bibliography  Save this article

Assessing The Performance Of Non-Banking Financial Institutions – A Knowledge Discovery Approach

Author

Listed:
  • Adrian Costea

    (Bucharest University of Economics Faculty of Cybernetics, Statistics and Informatics in Economy Bucharest, Romania)

Abstract

This paper proposes a framework for assessing the performance of non-banking financial institutions (NFIs). Firstly, we present an overview of the non-banking financial institutions’ sector in Romania and, then, the CAAMPL system which is used to evaluate the performance of banks. We argue that this system is suboptimal when applied to assessing NFIs’ performance and that the Knowledge Discovery in Databases (KDD) process could offer specific methods that may be used to developing better systems. Next, we discuss different concepts that are closely related with the KDD process: data, information and knowledge. Finally, we present the KDD process and we show how our research problem can be formalized as a KDD process.

Suggested Citation

  • Adrian Costea, 2011. "Assessing The Performance Of Non-Banking Financial Institutions – A Knowledge Discovery Approach," Annals of University of Craiova - Economic Sciences Series, University of Craiova, Faculty of Economics and Business Administration, vol. 3(39), pages 174-185.
    • Handle: RePEc:aio:aucsse:v:3:y:2011:i:39:p:174-185
    as

    Download full text from publisher

    File URL: http://feaa.ucv.ro/AUCSSE/0039v3-025.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
    2. Liliana Eva Donath & Laura Mariana Cismas, 2008. "Determinants of Financial Stability," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 11(29), pages 27-44, (3).
    3. International Monetary Fund, 2010. "Romania: Financial Sector Stability Assessment," IMF Staff Country Reports 2010/047, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Wenjun & Liu, Dong & Liu, Xiao & Pan, Lin, 2013. "Fuzzy overlapping community detection based on local random walk and multidimensional scaling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6578-6586.
    2. Yi-Chen Chung & Hsien-Ming Chou & Chih-Neng Hung & Chihli Hung, 2021. "Using Textual and Economic Features to Predict the RMB Exchange Rate," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(6), pages 1-8.
    3. Malliaris, A.G. & Malliaris, Mary, 2011. "Are foreign currency markets interdependent? evidence from data mining technologies," MPRA Paper 35261, University Library of Munich, Germany.
    4. Carrizosa, Emilio & Martín-Barragán, Belén & Morales, Dolores Romero, 2011. "Detecting relevant variables and interactions in supervised classification," European Journal of Operational Research, Elsevier, vol. 213(1), pages 260-269, August.
    5. Chen-Yang Cheng, 2014. "Indoor localization algorithm using clustering on signal and coordination pattern," Annals of Operations Research, Springer, vol. 216(1), pages 83-99, May.
    6. Dursun Delen & Marilyn G. Kletke & Jin-Hwa Kim, 2005. "A Scalable Classification Algorithm for Very Large Datasets," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 4(02), pages 83-94.
    7. Renato Bruni, 2007. "Reformulation of the support set selection problem in the logical analysis of data," Annals of Operations Research, Springer, vol. 150(1), pages 79-92, March.
    8. Fernandez del Pozo, J. A. & Bielza, C. & Gomez, M., 2005. "A list-based compact representation for large decision tables management," European Journal of Operational Research, Elsevier, vol. 160(3), pages 638-662, February.
    9. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    10. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2006. "Robust Learning from Bites for Data Mining," Technical Reports 2006,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    11. Richard Hendra & Aaron Hill, 2019. "Rethinking Response Rates: New Evidence of Little Relationship Between Survey Response Rates and Nonresponse Bias," Evaluation Review, , vol. 43(5), pages 307-330, October.
    12. Li, Hui & Sun, Jie, 2009. "Hybridizing principles of the Electre method with case-based reasoning for data mining: Electre-CBR-I and Electre-CBR-II," European Journal of Operational Research, Elsevier, vol. 197(1), pages 214-224, August.
    13. Patricia E. N. Lutu & Andries P. Engelbrecht, 2013. "Positive-versus-Negative Classification for Model Aggregation in Predictive Data Mining," INFORMS Journal on Computing, INFORMS, vol. 25(4), pages 792-807, November.
    14. Jorge Mejia & Shawn Mankad & Anandasivam Gopal, 2019. "A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections," Information Systems Research, INFORMS, vol. 30(4), pages 1363-1386, December.
    15. Marianne Hörlesberger & Ivana Roche & Dominique Besagni & Thomas Scherngell & Claire François & Pascal Cuxac & Edgar Schiebel & Michel Zitt & Dirk Holste, 2013. "A concept for inferring ‘frontier research’ in grant proposals," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 129-148, November.
    16. Matevž Kunaver & Árpád Bűrmen & Iztok Fajfar, 2022. "Automatic Grammatical Evolution-Based Optimization of Matrix Factorization Algorithm," Mathematics, MDPI, vol. 10(7), pages 1-22, April.
    17. Hekimoğlu, Mustafa & Sevim, Ismail & Aksezer, Çağlar & Durmuş, İpek, 2019. "Assortment optimization with log-linear demand: Application at a Turkish grocery store," Journal of Retailing and Consumer Services, Elsevier, vol. 50(C), pages 199-214.
    18. Ajaya K. Swain & Valeria R. Garza, 2023. "Key Factors in Achieving Service Level Agreements (SLA) for Information Technology (IT) Incident Resolution," Information Systems Frontiers, Springer, vol. 25(2), pages 819-834, April.
    19. Yalcin, Ahmet Selcuk & Kilic, Huseyin Selcuk & Delen, Dursun, 2022. "The use of multi-criteria decision-making methods in business analytics: A comprehensive literature review," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    20. Iyad Suleiman & Maha Arslan & Reda Alhajj & Mick Ridley, 2017. "Prediction Model of School Readiness," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 1-55, September.

    More about this item

    Keywords

    knowledge discovery; data mining; prudential supervision; non-banking financial institutions; financial performance;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aio:aucsse:v:3:y:2011:i:39:p:174-185. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anca Bandoi The email address of this maintainer does not seem to be valid anymore. Please ask Anca Bandoi to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/fecraro.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.