FinTech startups require agility and speed to bring financial products to market quickly. Machine learning offers opportunities in areas like fraud detection and credit risk assessment. However, building the necessary data science infrastructure can be time-consuming for small teams. Azure Machine Learning Automated ML (AutoML) can accelerate this process by automatically training and evaluating multiple machine learning pipelines. Startups can focus on solving financial problems rather than experimenting with algorithms. Traditional machine learning development involves significant complexity and effort in model testing and feature engineering. Azure Machine Learning AutoML streamlines this by creating and optimizing various training pipelines. Identifying the specific financial problem, such as loan default prediction or transaction forecasting, is crucial before using AutoML. For loan default prediction, a classification problem, AutoML can evaluate multiple models to identify strong candidates. Forecasting transaction volume for planning and operational needs is a time-series forecasting problem. Machine learning in FinTech needs to be integrated into production systems and evolve from no-code to code-based solutions. Model accuracy is not the sole metric; precision, recall, and business impact are also vital. Explainability is essential in FinTech for understanding and validating model decisions, especially in sensitive areas. AutoML shifts data scientists' focus from manual model training to higher-value tasks like problem definition and model validation. Ultimately, Azure Machine Learning AutoML helps FinTech startups accelerate experimentation and integrate machine learning into their products efficiently.