Low-Code AI

Chapter 5. Using AutoML to Detect Fraudulent Transactions

In this chapter, you build a Vertex AI AutoML model to predict whether a financial transaction is fraudulent or not. You will clean and explore the dataset in a Google Colab notebook environment before creating a managed dataset on Vertex AI as you did in Chapter 3. Once you have created a managed dataset, you will use AutoML to create a classification model to predict if a transaction is fraudulent or not. Along the way, the chapter discusses classification models in general and the corresponding metrics that are commonly used to evaluate them.

The overall workflow of this chapter is very similar to what you worked through in Chapter 4 for the problem of predicting advertising media channel sales. For this reason, in many places in this chapter you will see more concise details where the conversations would be very similar. If you get stuck in these sections, please refer back to Chapter 4 for more details.

The Business Use Case: Fraud Detection for Financial Transactions

Your task in this chapter, as mentioned, is to build a model to predict whether a financial transaction is fraudulent or legitimate. Your new company is a mobile payment service that serves hundreds of thousands of users. Fraudulent transactions are fairly rare and are usually caught by other protections. However, the unfortunate truth is that some of these are slipping through the cracks and negatively impacting your users. Your company can rectify ...