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UncetaNIPS18.pdf (1.68 MB)

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2018-11-23

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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/130337

Towards Global Explanations for Credit Risk Scoring

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Abstract

In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The envisaged approach provides a unified solution to approximate non-linear decision boundaries with simpler classifiers while retaining the original classification accuracy. We use a private residential mortgage default dataset as a use case to illustrate the feasibility of this approach to ensure the decomposability of attributes during pre-processing.

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Risc de crèdit, Hipoteques

Subject (English)

Credit risk, Mortgages

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Comunicacions a congressos (Matemàtiques i Informàtica)

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UNCETA, Irene, NIN, Jordi and PUJOL VILA, Oriol. Towards Global Explanations for Credit Risk Scoring. Comunicació a: NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness. Explainability. Vol.  Accuracy, num. and Privacy, pags. Montréal. [consulted: 7 of June of 2026]. Available at: https://hdl.handle.net/2445/130337

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