Interpretability of neural networks: a credit card default model example
Recently developed techniques aimed at answering interpretability issues in neural networks are tested and applied to a retail banking case
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Ksenia Ponomareva and Simone Caenazzo show the feasibility of overcoming the interpretability hurdles around the application of neural networks in the estimation of credit risk for a portfolio of credit cards
Historically, the widespread use of advanced deep learning models in sensitive fields such as medicine and finance has been hindered by a fundamental lack of interpretability regarding the outcomes of such models. Simpler techniques such as linear or logistic
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