An Analytical Review: Explainable AI for Decision Making in Finance Using Machine Learning
Tanvi Shah, Kishori Shekokar, Amit Barve, Pramod Khandare · 2024
Explainable AI is a type of artificial intelligence which enables the explanation of learning models and states why the system arrived at a particular decision, exploring its logical paradigms, contrary to the inherent black box nature of artificial intelligence. Similarly, machine learning interpretability allows users to comprehend the results of the learning models by providing reasoning for the decisions that it has arrived at. Because of the enormous, continued success in machine learning, including statistically learning from large data, the finance world is becoming increasingly interested in this topic with more transparency in decisions to reduce risk. Machine learning algorithms with AI can shift through vast amounts of historical data to identify patterns and trends, enabling businesses to make accurate predictions about future outcomes. This paper reviews brief historical introduction about XAI, its approaches and taxonomy as well as links to their programming implementations with specific directions for finance.