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001449593 001__ 1449593
001449593 003__ OCoLC
001449593 005__ 20230310004408.0
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001449593 019__ $$a1344490806
001449593 020__ $$a9783031102363$$q(electronic bk.)
001449593 020__ $$a3031102363$$q(electronic bk.)
001449593 020__ $$z9783031102356$$q(print)
001449593 020__ $$z3031102355
001449593 0247_ $$a10.1007/978-3-031-10236-3$$2doi
001449593 035__ $$aSP(OCoLC)1344542027
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001449593 050_4 $$aHG3751$$b.L63 2022eb
001449593 08204 $$a658.8/8$$223/eng/20220928
001449593 1001_ $$aLocatelli, Rossella.
001449593 24010 $$aArtificial Intelligence e credit risk.$$lItalian
001449593 24510 $$aArtificial intelligence and credit risk :$$bthe use of alternative data and methods in internal credit rating /$$cRossella Locatelli, Giovanni Pepe, Fabio Salis.
001449593 264_1 $$aCham :$$bPalgrave Macmillan,$$c2022.
001449593 264_4 $$c©2022
001449593 300__ $$a1 online resource (xvii, 104 pages)
001449593 336__ $$atext$$2rdacontent
001449593 336__ $$astill image$$2rdacontent
001449593 337__ $$acomputer$$2rdamedia
001449593 338__ $$aonline resource$$2rdacarrier
001449593 504__ $$aIncludes bibliographical references (pages 101-2) and index.
001449593 504__ $$aIncludes bibliographical references and index.
001449593 50500 $$tIntroduction --$$tHow AI Models Are Built --$$tAI Tools in Credit Risk --$$tThe Validation of AI Techniques --$$tPossible Evolutions in AI Models.
001449593 506__ $$aAccess limited to authorized users.
001449593 520__ $$aThis 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 new AI toolbox on new (unconventional) data to enhance the models predictive power, without neglecting problems due to results interpretability while recognizing ethical dilemmas. Contributors are university researchers, risk managers operating in banks and other financial intermediaries and consultants. The topic is a major one for the financial industry, and this is one of the first works offering relevant case studies alongside practical problems and solutions. Rossella Locatelli is Full Professor of Banking at the University of Insubria, Italy. Giovanni Pepe is KPMG Partner since May 2015 where he works in the Financial Risk Management line of services with a focus on the quantitative aspects of credit risk. Fabio Salis is Chief Risk Officer of Creval since 2018. Formerly, he was Head of Risk Management at Banco Popolare since 2012, where he led important projects such as validation of credit and operational risk models and EBA stress test.
001449593 588__ $$aDescription based on print version record.
001449593 650_0 $$aCredit$$xManagement.
001449593 650_0 $$aFinancial risk management.
001449593 650_0 $$aArtificial intelligence$$xFinancial applications.
001449593 655_0 $$aElectronic books.
001449593 7001_ $$aPepe, Giovanni.
001449593 7001_ $$aSalis, Fabio.
001449593 77608 $$iPrint version:$$aLocatelli, Rossella$$tArtificial Intelligence and Credit Risk$$dCham : Springer International Publishing AG,c2022$$z9783031102356
001449593 77608 $$iPrint version:$$aLOCATELLI, ROSSELLA.$$tARTIFICIAL INTELLIGENCE E CREDIT RISK.$$d[S.l.] : PALGRAVE MACMILLAN, 2022$$z3031102355$$w(OCoLC)1322812352
001449593 852__ $$bebk
001449593 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-10236-3$$zOnline Access$$91397441.1
001449593 909CO $$ooai:library.usi.edu:1449593$$pGLOBAL_SET
001449593 980__ $$aBIB
001449593 980__ $$aEBOOK
001449593 982__ $$aEbook
001449593 983__ $$aOnline
001449593 994__ $$a92$$bISE