Artificial intelligence has been used to build personalized financial offerings that are improving customer experiences in the banking, insurance, fintech, and investment industries. A systematic literature review of 62 peer-reviewed articles using Scopus, Web of Science, IEEE Xplore, and Google Scholar was done to examine customer outcomes and determinants of adoption. Quantitative methods prevailed (54.8%), followed by qualitative (25.8%), and mixed-method (19.4%) researches giving more insight into ethics and transparency. The most used AI technologies were machine learning algorithms and recommendation systems, where NLP and deep learning were used moderately. The most prominent of them were customer satisfaction (45 studies), trust (38), engagement (34), loyalty (29), and decision quality (22). Perceived usefulness, ease of use, trust, data privacy, ethical perception in AI, and regulatory assurance were all factors that affected adoption. Explainable, transparent, and ethical AI practices proved to be critical to long-term adoption and engagement principles to make responsible AI applications in financial services.