  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.
