| 期刊全稱 | Artificial Intelligence and Credit Risk | | 期刊簡稱 | The Use of Alternati | | 影響因子2023 | Rossella Locatelli,Giovanni Pepe,Fabio Salis | | 視頻video | http://file.papertrans.cn/163/162186/162186.mp4 | | 發(fā)行地址 | Analyzes methodological problems regarding the use of artificial intelligence in measuring credit risk.Offers a technical analysis of the first experiences in using AI tools to measure credit risk.Dis | | 圖書封面 |  | | 影響因子 | .This book focuses on the alternative techniques and data leveraged for credit risk, describing and analysing the array of methodological approaches for the usage of techniques and/or alternative data for regulatory and managerial rating models. During the last decade the increase in computational capacity, the consolidation of new methodologies to elaborate data and the availability of new information related to individuals and organizations, aided by the widespread usage of internet, set the stage for the development and application of artificial intelligence techniques in enterprises in general and financial institutions in particular. In the banking world, its application is even more relevant, thanks to the use of larger and larger data sets for credit risk modelling. The evaluation of credit risk has largely been based on client data modelling; such techniques (linear regression, logistic regression, decision trees, etc.) and data sets (financial, behavioural, sociologic, geographic, sectoral, etc.) are referred to as “traditional” and have been the de facto standards in the banking industry. The incoming challenge for credit risk managers is now to find ways to leverage the | | Pindex | Book 2022 |
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