The advent of generative models in finance has seen a revolution in risk analysis and portfolio optimization. However, major challenges to these models include the need for big datasets during training and their inability to adjust to new financial data. This abstract explores how meta-learning can be used to overcome these limitations. Data scarcity and non-stationarity are the problems with conventional generative models. Financial markets always change, rendering useless any models that cannot adapt, but at times financial datasets are scarce and expensive. One solution to this problem is meta-learning. By learning from a combination of financial datasets, meta-learning systems can learn what information is generalizable regarding distributions of financial data. Consequently, they are able to produce sensible samples and quickly adapt themselves when provided new data sets. This improved flexibility provides exciting opportunities in finance. More realistic future market simulations will generate improved risk assessments. Additionally, meta-learning builds variable portfolios based on real-world market dynamics thereby supporting portfolio optimization too. It is also capable of detecting anomalies that deviate from the norm thus helping identify financial irregularities as well as detectable trends out of normality that it identifies. Nevertheless, Meta-Learning in Finance is a nascent field with much promise behind it; so including them helps generate precise financial decisions.