Abstract
Decisions in financial applications that directly impact citizens are often based on black-box intelligent methods. Given the growing interest in making these decisions more transparent, and the emergent legislation on interpretability and privacy, new solutions to give some insight on such black-boxes, presenting explanations on the decision patterns are being sought. In this paper we propose a method that transfers knowledge from black-box models to more interpretable models to understand the decision patterns in financial applications. Results on credit risk and stock market data show that it is possible to use white-box methods that work on black-box results to show the potential interpretation of the decision patterns.
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This Research is developed under the EU COST Action: Fintech and Artificial Intelligence in Finance funded by Horizon 2020 Framework Programme of the European Union.
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Faria, T., Silva, C., Ribeiro, B. (2021). Interpreting Decision Patterns in Financial Applications. In: Tavares, J.M.R.S., Papa, J.P., González Hidalgo, M. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2021. Lecture Notes in Computer Science(), vol 12702. Springer, Cham. https://doi.org/10.1007/978-3-030-93420-0_28
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DOI: https://doi.org/10.1007/978-3-030-93420-0_28
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