Agent-Based Decision-Support Framework for Credit Risk Early Warning

Author(s):  
Yan Peng ◽  
Diming Ai ◽  
Like Zhuang
2019 ◽  
Vol 22 (6) ◽  
pp. 1467-1485 ◽  
Author(s):  
Juan Du ◽  
Hengqing Jing ◽  
Kim-Kwang Raymond Choo ◽  
Vijayan Sugumaran ◽  
Daniel Castro-Lacouture

2015 ◽  
Author(s):  
L. K. Kirkman ◽  
John K. Hiers A. ◽  
L. L. Smith ◽  
L. M. Conner ◽  
S. L. Zeigler ◽  
...  

2021 ◽  
Vol 55 (5) ◽  
pp. 2890-2898 ◽  
Author(s):  
Tami C. Bond ◽  
Angela Bosco-Lauth ◽  
Delphine K. Farmer ◽  
Paul W. Francisco ◽  
Jeffrey R. Pierce ◽  
...  

2021 ◽  
Vol 14 (5) ◽  
pp. 211
Author(s):  
Iryna Yanenkova ◽  
Yuliia Nehoda ◽  
Svetlana Drobyazko ◽  
Andrii Zavhorodnii ◽  
Lyudmyla Berezovska

This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.


Author(s):  
David Kik ◽  
Matthias Gerhard Wichmann ◽  
Thomas Stefan Spengler

AbstractLocation choice is a crucial planning task with major influence on a company’s future orientation and competitiveness. It is quite complex, since multiple location factors are usually of decision-relevance, incomparable, and sometimes conflictual. Further, ongoing urbanization is associated with locational dynamics posing major challenges for the regional location management of companies and municipalities. For example, respecting urban space as location factor, a scarcity growing over time leads to different assessment and requirements on a company’s behalf. For both companies and municipalities, there is a need for location development which implies an active change of location factor characteristics. Accordingly, considering locational dynamics is vital, as they may be decisive in the location decision-making. Although certain dynamics are considered within conventional Facility Location Problem (FLP) approaches, a systematic consideration of active location development is missing so far. Consequently, they may propagate long-term unfavorable location decisions, as major potentials associated with company-driven and municipal development measures are neglected. Therefore, this paper introduces a comprehensive decision support framework for the Regional Facility Location and Development planning Problem (RFLDP). It provides an operationalization of development measures, and thus anticipates dynamic adaptations to the environment. An established multi-criteria approach is extended to this new application. A complementary guideline ensures its meaningful applicability by practitioners. Based on a real-life case study, the decision support framework’s strength for practical application is demonstrated. Here, major advantages over conventional FLP approaches are highlighted. It is shown that the proposed methodology results in alternative location decisions which are structurally superior.


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