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Towards Explainable Artificial Intelligence in Banking and Financial Services

arXiv 2112.08441 ↗First posted 14 Dec 2021cs.LG cs.AIcited by 1

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Abstract (arXiv)

Artificial intelligence (AI) enables machines to learn from human experience, adjust to new inputs, and perform human-like tasks. AI is progressing rapidly and is transforming the way businesses operate, from process automation to cognitive augmentation of tasks and intelligent process/data analytics. However, the main challenge for human users would be to understand and appropriately trust the result of AI algorithms and methods. In this paper, to address this challenge, we study and analyze the recent work done in Explainable Artificial Intelligence (XAI) methods and tools. We introduce a novel XAI process, which facilitates producing explainable models while maintaining a high level of learning performance. We present an interactive evidence-based approach to assist human users in comprehending and trusting the results and output created by AI-enabled algorithms. We adopt a typical scenario in the Banking domain for analyzing customer transactions. We develop a digital dashboard to facilitate interacting with the algorithm results and discuss how the proposed XAI method can significantly improve the confidence of data scientists in understanding the result of AI-enabled algorithms.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 81 references that are in the map. First posted on arXiv 14 Dec 2021.

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28 of 81 references.

References · 28

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16 more
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