Towards Explainable Artificial Intelligence in Banking and Financial Services
This addresses the problem of trust in AI for data scientists and users in the banking domain, but it appears incremental as it builds on existing XAI methods with a specific application.
The paper tackles the challenge of understanding and trusting AI results in banking by introducing a novel Explainable AI (XAI) process that maintains high learning performance and uses an interactive evidence-based approach to improve user comprehension.
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.