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Artificial intelligence (AI) enables machines to learn from human experience,adjust to new inputs, and perform human-like tasks. AI is progressing rapidlyand is transforming the way businesses operate, from process automation tocognitive augmentation of tasks and intelligent process/data analytics.However, the main challenge for human users would be to understand andappropriately trust the result of AI algorithms and methods. In this paper, toaddress this challenge, we study and analyze the recent work done inExplainable Artificial Intelligence (XAI) methods and tools. We introduce anovel XAI process, which facilitates producing explainable models whilemaintaining a high level of learning performance. We present an interactiveevidence-based approach to assist human users in comprehending and trusting theresults and output created by AI-enabled algorithms. We adopt a typicalscenario in the Banking domain for analyzing customer transactions. We developa digital dashboard to facilitate interacting with the algorithm results anddiscuss how the proposed XAI method can significantly improve the confidence ofdata scientists in understanding the result of AI-enabled algorithms.

Towards Explainable Artificial Intelligence in Banking and Financial Services