مدل سازی سطح دانش برای مدیریت ریسک سیستمیک در موسسات مالی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|749||2011||11 صفحه PDF||سفارش دهید||6680 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 4, April 2011, Pages 3528–3538
The current subprime mortgage crisis is a typical case for systemic risk in financial institutions. In order to further our understanding and communication about systemic risk management (SRM) in financial institutions, this paper proposes a knowledge level model (KLM) for systemic risk management in financial institutions. There are two parts considered in the proposed KLM: ontologies and problem solving method (PSM). Ontologies are adopted to represent a knowledge base of KLM, which integrates top level ontology and domain level ontologies. And then the problem solving method is given to show the reasoning process of this knowledge. The symbol level of KLM is also discussed which integrates OWL, SWRL and JESS. Further, the discussion about Lehman Brother’s Minibonds case in 2008 is provided to illustrate how proposed KLM is used in practice. With these, first, they will enhance the interchange of information and knowledge sharing for SRM within a financial institution. Second, they will assist knowledge base development for SRM design, for which a prototype of financial systemic risk management decision support system is given in this study. Third, they will support coordination among different institutions by using standardized vocabularies. And finally, from the design science perspective, the whole proposed framework could be meaningful to models in other domains.
Systemic risk refers to the risk or probability of breakdown (losses) in the individual parts of components, and is evidenced by co-movements (correlation) among most or all parts (Kaufman, 2000). The current subprime mortgage crisis is a typical case for systemic risk in financial institutions. The subprime mortgage crisis was triggered by a dramatic rise in subprime mortgage defaults and related foreclosures in the United States, but has brought huge adverse effects to the banks and financial markets all around the world. Many banks, real estate investment trusts (REIT), and hedge funds have suffered significant losses as a result of mortgage payment defaults or mortgage asset devaluation. Many observers have commented that the turmoil in world financial markets has led to a severe and still unfolding economic downturn in most of the Western economies; as a result, the world has come to the brink of the “worst economic downturn” since the Second World War. Because of this crisis, governments and international organizations are calling for increased systemic risk management (SRM) in financial institutions. To repeat the Nobel laureate, Dr. A. Michael Spence, an important challenge going forward is to better understand these dynamics and complexities of SRM in financial institutions as the analytical underpinning of an early warning system with respect to financial instability (Spence, 2008). In order to further our understanding and communication about SRM in financial institutions, a knowledge level model for SRM is proposed in this paper. Knowledge level was first proposed by Newell, which was used to distinguish it from the symbol level of information system (Newell, 1981). Knowledge level modeling is a kind of conceptual modeling method. As defined by Mylopoulos (1992), “Conceptual modeling is the activity of formally describing some aspects of the physical and social world around us for the purposes of understanding and communication”. Knowledge level modeling means capturing and representing knowledge without specific attention being paid to how it will be implemented (Uschold, 1998). It includes ontologies and the problem solving model (PSM), where ontologies are concerned with static knowledge needed for problem solving and PSM with the dynamic reasoning process with knowledge. Ontologies aim at capturing knowledge in a generic way and provide a commonly agreed understanding of a domain, which may be reused and shared across applications and groups (Chandrasekaran, Josephson, & Benjamins, 1999). In this study, ontologies were designed at two levels: top level ontology and domain level ontology. Top level ontology represents the general world knowledge (Uschold, 1998), and in this study ontology from the CYC project is adopted which is an attempt to build a very large knowledge base to facilitate common-sense reasoning1 (Lenat, 1995, Lenat and Guha, 1989 and Lenat et al., 1985). Domain level ontology represents knowledge in a specific domain. In this study a general framework of domain level ontology will be given which shows the key concepts and their relationships in the SRM domain. Problem solving models (PSMs) specify which bodies of knowledge participate in problem solving and how they relate to each other (Uschold, 1998). Many models have been proposed in this area, such as role-limiting method (Marcus, 1988), CommonKADS (Schreliber, Wielinga, & Breuker, 1993), and so on. In this research, a hypothesis-test model is given to detect the systemic risk signal from external institutions which is based on Simon’s decision making theory (Simon, 1996). And in the symbol level, the proposed KLM is integrated within Ontology Web Language (OWL), Semantic Web Rule Language (SWRL) and JESS rules framework which will be very helpful to information system development. The rest of this paper is organized as follows: Section 2 discusses the background of SRM in financial institutions and outlines the technique used in this research; the knowledge model for SRM is proposed in Section 3, which includes ontologies and PSM; a case of Lehman Brothers Minibonds is used to illustrate our approach in Section 4; the concept modeling quality of our proposed model is discussed in Section 5 and finally the paper ends with the conclusion in Section 6.
نتیجه گیری انگلیسی
Knowledge level modeling means capturing and representing knowledge without specific attention being paid to how it will be implemented. The ongoing global financial crisis, which has been treated as the greatest threat to the world economy since the Great Depression in 1930s, reflects the needs and importance of systemic risk management (SRM). In this research, knowledge level modeling is introduced and applied in systemic risk management in financial institutions. The contributions of this paper are as follows: (1) A knowledge level model for SRM in financial institutions has been proposed which integrates domain level ontology, top level ontology and problem solving model. •Domain level ontology is described to investigate the basic concepts and their relationships for SRM in financial institutions. •Top level ontology, adopted from the CYC project in this study, is discussed to support domain level ontology with common-sense knowledge for SRM. • A problem solving model is proposed to detect systemic risk signal based on reasoning through the domain level ontology and top level ontology. The discussion of Lehman Brother’s Minibonds incident in Hong Kong also gives proof to the usefulness of the proposed model in practice. (2)The symbol level of the proposed knowledge level model has also been discussed. In detail, OWL, SWRL and JESS rules are adopted to successfully represent them with a formal format. (3)The developed knowledge level model can benefit SRM in many ways. •It will enhance interchange of information and knowledge sharing for SRM within a financial institution. •It will assist knowledge base development for SRM design. Actually, a prototype of financial systemic risk management decision support system is given in this study. •They will support coordination among different institutions by using standardized vocabularies, e.g., they will allow integration of different SRM systems. However, this work is only the beginning of the KLM research for SRM in financial institutions. Much more work is needed in the future: (1)The evaluation of KLM used case study which is an informal valuation method. A more solid evaluation, such as field experiment, could be done in a future study. (2)The discussion about top level ontology is very limited and more details will be left to future work. For example, the top level ontology used here is from the CYC project, in order to integrate with domain level ontology which is represented with OWL and SWRL; what was done in this study was to download the OWL knowledge base from OpenCyc. However, it would also be possible to translate the domain level ontology with CYC knowledge representation language. (3) The discussion of KLM application will be left to future work. As mentioned previously, the proposed KLM will benefit to SRM in financial institutions in many ways, however the individual performance has not been discussed in this paper, and will be left to future work.