Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/237161 
Erscheinungsjahr: 
2019
Quellenangabe: 
[Journal:] Financial Innovation [ISSN:] 2199-4730 [Volume:] 5 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2019 [Pages:] 1-27
Verlag: 
Springer, Heidelberg
Zusammenfassung: 
Giving loans and issuing credit cards are two of the main concerns of banks in that they include the risks of non-payment. According to the Basel 2 guidelines, banks need to develop their own credit risk assessment systems. Some banks have such systems; nevertheless they have lost a large amount of money simply because the models they used failed to accurately predict customers' defaults. Traditionally, banks have used static models with demographic or static factors to model credit risk patterns. However, economic factors are not independent of political fluctuations, and as the political environment changes, the economic environment evolves with it.
Schlagwörter: 
Fuzzy clustering
Non-performing loan
Credit risk
FIS
Dynamism
ANFIS
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