@techreport{TR-IC-PFG-23-65,
   number = {IC-PFG-23-65},
   author = {Leonardo Almeida Reis and Marcos Medeiros Raimundo},
   title = {{Explainability in Credit Scoring Neural Networks}},
   month = {December},
   year = {2023},
   institution = {Institute of Computing, University of Campinas},
   note = {In English, 10 pages.
    \par\selectlanguage{english}\textbf{Abstract}
       Credit  scoring  is  a  crucial element in the economic sector,
       relying   on   an   individual's  trustworthiness  in  honoring 
       financial    commitments.    Institutions    providing   credit  
       increasingly   leverage   Artificial   Intelligence   (AI)  for 
       widespread  credit  assessments.  However,  the  integration of
       Machine   Learning   (ML)   models   raises  ethical  concerns, 
       particularly  regarding  biases  inherent  in  AI  models. This
       document  explainabity  methods based on feature importance and
       emphasizes  the  need for transparency in data utilization. The
       research  aims  to  enhance  model  explainability and proposes
       techniques  for  a  clearer  understanding of model operations.
       \par  The  study  employs  Feature  Importance Techniques, like
       Permutation Importance, in order to study which features impact
       the  model  the  most.  To  address  data  generation anomalies
       created  by  the previous method, Novelty Detection Algorithms,
       including  Isolation Forest and Density Forest, are introduced.
       Additionally,  we also explore Counterfactual Explanations as a
       method  to  explain  ML  model  outcomes  and  how to change an
       specific data to retrieve a desired prediction.
  }
}