Towards Responsible AI for Financial Transactions
Chapter, Peer reviewed
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Date
2020Metadata
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Original version
Maree, C., Modal J. E. & Omlin, C. W. (2020). Towards Responsible AI for Financial Transactions. In C. A. Coello (Ed.), IEEE Symposium Series on Computational Intelligence (pp. 16–21). IEEE. https://doi.org/10.1109/SSCI47803.2020.9308456Abstract
The application of AI in finance is increasingly dependent on the principles of responsible AI. These principles-explainability, fairness, privacy, accountability, transparency and soundness form the basis for trust in future AI systems. In this empirical study, we address the first principle by providing an explanation for a deep neural nenvork that is trained on a mixture of numerical, categorical and textual inputs for financial transaction classification. The explanation is achieved through (1) a feature importance analysis using Shapley additive explanations (SHAP) and (2) a hybrid approach of text clustering and decision tree classifiers. We then test the robustness of the model by exposing it to a targeted evasion attack, leveraging the knowledge we gained about the model through the extracted explanation.