@article{1449593,
      recid = {1449593},
      author = {Locatelli, Rossella. and Pepe, Giovanni. and Salis,  Fabio.},
      title = {Artificial intelligence and credit risk : the use of  alternative data and methods in internal credit rating /},
      pages = {1 online resource (xvii, 104 pages)},
      abstract = {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 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.},
      url = {http://library.usi.edu/record/1449593},
      doi = {https://doi.org/10.1007/978-3-031-10236-3},
}