As businesses continue to shift toward online credit card payments, there is a rising need to have an effective fraud detection solution capable of real-time, actionable alerts. By Polong Lin and Pavan Kattamuri.
Because all of the Google Cloud products used in this solution are serverless and fully managed, this means you won’t need to spend time setting up and maintaining infrastructure, enabling you to focus on getting the solution up and running in an hour.
This blog post will be a technical dive into how the solution works:
- Preparing the data on BigQuery
- Building the fraud detection model using BigQuery ML
- Hosting the BigQuery ML model on AI Platform to make online predictions on streaming data using Dataflow
- Setting up alert-based fraud notifications using Pub/Sub
- Creating operational dashboards for business stakeholders and the technical team using Data Studio
Whether you are part of the fraud protection team in a financial institution or an online retailer trying to reduce fraudulent losses, real time ML solutions are most impactful when they serve the ultimate business goals and efficiently adapt to the changing environment. With data stored on BigQuery, it becomes easy to train machine learning models using BigQuery ML without needing to set up or procure infrastructure, saving time, money and complexity when productionizing the design pattern. Excellent read!
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