The process of building a fraud model with a feature store involves several steps, including developing Tecton features from data sources, training the model, and launching an inference production service. A key challenge is feeding the production model with the right data to make predictions quickly, which can be achieved using a feature store like Tecton. Real-time fraud problems require very fresh features, making it essential to have the ability to create complex features using the freshest information possible. The entities parameter in Tecton's TemporalFeaturePackage helps to materialize historical data and return specific features at a given timestamp. A feature service is used to serve multiple feature packages to different models, and model training involves extracting a training dataframe from Tecton and passing it to the model for training. The model inference stage involves loading the trained model, getting the Tecton feature vector, layering in additional transaction information, making the prediction, and returning the result.