From the course: SQL for Finance Professionals
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Case study: Detecting fraud - SQL Tutorial
From the course: SQL for Finance Professionals
Case study: Detecting fraud
- [Instructor] One of the most common applications of predictive modeling in finance is using anomaly detection to deduct fraudulent credit card transactions. SQL comes in handy when finance professionals have vast amounts of transaction data to work with in creating their fraud detection models. But in practice, SQL is not the only tool they use. Python or R are two other coding languages that many consider essential in building predictive models. This is because they come with built-in packages or libraries that are open-source and specifically designed for the purpose of machine learning. In this case study, we'll walk through what building fraud detection models look like in the context of all these tools together. SQL is likely the first tool you'll encounter in the process of building your fraud detection model. The engineers who have built the data pipelines used to retrieve the transaction data most likely have the data…
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