When the business case is to predict fraud detection, which objective should be chosen in Vertex AI?

In the context of predicting fraud detection, choosing regression or classification as the objective in Vertex AI is appropriate due to the nature of the problem being fundamentally about classification. Fraud detection typically involves categorizing transactions or activities into two classes: fraudulent or non-fraudulent.

Using classification models allows you to leverage labeled data where each transaction is tagged as either a fraud or not. By training a model on this labeled dataset, the objective is to accurately classify new, unseen transactions based on the patterns learned from historical data. This predictive modeling approach is essential for detecting and flagging potentially fraudulent activities in real time.

Regression, while relevant in other contexts, is more suited to predicting continuous numeric outcomes rather than categorical outcomes, which is not the primary focus of resolving fraud cases. Clustering, time series forecasting, and dimensionality reduction are all methodologies that serve different purposes. Clustering is more about grouping similar data points, making it less suitable for a direct yes/no outcome like fraud detection. Time series forecasting focuses on predicting future values based on past sequences, and dimensionality reduction is used for simplifying datasets without losing significant patterns or information. Hence, none of these approaches align as directly with the specific objective of fraud detection.