Abstract
The financial industry is a major player in the digital landscape and a key driver of digital transformation in the economy. In recent times, the financial sector has come under scrutiny due to emerging financial crises, particularly in high-risk areas like credit scoring models where standard AI models may not be fully reliable. This highlights the need for greater accountability and transparency in the use of digital technologies in Fintech. In this paper, we propose a novel approach to enhance the interpretability of AI models by knowledge augmentation using distillation methods. Our aim is to transfer the knowledge from black-box models to more transparent and interpretable models, e.g., decision-trees, enabling a deeper understanding of decision patterns. We apply our method to a credit score problem and demonstrate that it is feasible to use white-box techniques to gain insight into the decision patterns of black-box models. Our results show the potential for improving interpretability and transparency in AI decision-making processes in Fintech scenarios.
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This research was supported by the Portuguese Recovery and Resilience Plan (PRR) through project C645008882-00000055, Center for Responsible AI.
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Silva, C., Faria, T., Ribeiro, B. (2023). Towards Interpretability in Fintech Applications via Knowledge Augmentation. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14115. Springer, Cham. https://doi.org/10.1007/978-3-031-49008-8_9
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