Machine Learning and AI have become inevitable workhorses behind algorithms in data- centric domains such as Fintech. While dealing with sensitive data in such domains, it becomes essential to make sure that the data is used in an ethical and fair manner, and predictions made by learning algorithms are explainable. The machine learning community has in recent years begun investigations into the mathematical underpinnings of ethics, fairness and explainability. While several attempts have been made to capture these subtle notions, there is no global consensus as to what is the right definition of ethics and fairness. In this project, we wish to explore theoretical and algorithmic underpinnings of ethics, fairness and explainability in AI with a focus on Fintech applications.