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.
