The ML engineer needs to train the fraud detection model using a SageMaker built-in algorithm. Which algorithm is the appropriate choice for this task?

LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is highly efficient and effective for classification tasks, such as fraud detection, especially with tabular data. Its speed and accuracy make it a strong choice for production-grade ML systems. Linear Learner is suitable for linear regression and classification but may not capture complex non-linear relationships as effectively as tree-based models for fraud detection. K-means clustering is an unsupervised learning algorithm used for grouping data points, not for supervised classification like fraud detection. Neural Topic Model (NTM) is used for discovering abstract topics in text documents, which is irrelevant for a typical fraud detection task involving structured transactional data.