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Credit risk analysis and lending decisions: new Machine Learning techniques

Author

Listed:
  • Cristina Caprara

    (Crif)

  • Daniele Vergari

    (Crif)

Abstract

Open banking and digital revolution: the credit chain is experiencing a profound change by the entry of new players and innovative methods of customers’ engagement. In this scenario, Machine Learning techniques offer a competitive advantage if applied to make more efficient the tools supporting credit governance. The latest techniques of local interpretable models (Lime), combined with the techniques for the results’ profiling, also allow to overcome the limits of interpretability and to encourage their application also in the regulatory area

Suggested Citation

  • Cristina Caprara & Daniele Vergari, 2020. "Credit risk analysis and lending decisions: new Machine Learning techniques," BANCARIA, Bancaria Editrice, vol. 1, pages 49-53, January.
    • Handle: RePEc:ban:bancar:v:1:y:2020:m:january:p:49-53
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    File URL: https://bancaria.it/en/credit-risk-analysis-and-lending-decisions-new-machine-learning-techniques
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    More about this item

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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