Optimization algorithms are used in quantitative finance and risk management to hedge and optimally re-balance portfolios of financial assets. As performance, numerical stability and practical applicability of optimization algorithms still remains a challenge in financial modeling, we look at Machine Learning practice to improve accuracy of financial optimization modeling. Moreover, we investigate how we can enhance formulating financial optimization problems with Artificial Intelligence algorithms such as Natural Language Processing and Neural Nets. Natural language-like interfaces for formulating financial optimization problems such as cloud APIs and chat-bots for portfolio selection will be discussed and demoed.