Towards Responsible AI for Financial Transactions

• URL: http://arxiv.org/abs/2206.02419v1
• Date: Mon, 6 Jun 2022 08:29:47 GMT
• Title: Towards Responsible AI for Financial Transactions
• Authors: Charl Maree and Jan Erik Modal and Christian W. Omlin
• Abstract summary: We provide an explanation for a deep neural network that is trained on a mixture of numerical, categorical and textual inputs for financial transaction classification. We then test the robustness of the model by exposing it to a targeted evasion attack.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 study, we address the first principle by providing an explanation for a deep neural network 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.

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