From the course: AI in Risk Management and Fraud Detection

Unlock this course with a free trial

Join today to access over 24,900 courses taught by industry experts.

Feature engineering and preprocessing in ChatGPT

Feature engineering and preprocessing in ChatGPT

From the course: AI in Risk Management and Fraud Detection

Start my 1-month free trial Buy for my team

Feature engineering and preprocessing in ChatGPT

“

- [Instructor] Once your dataset is clean, the next critical step is feature engineering. The process of creating new input variables that help your model detect fraud more effectively. Think of it as giving your model better clues to work with. For example, raw transaction amount alone might not reveal much, but if you calculate the ratio of transaction amount to credit limit, that tells you how close a user is to maxing out their credit, which can be a fraud signal. Another useful feature is average transaction size, which is the balance over the number of transactions. This captures how much money is typically moved per transaction. Unusually large amounts, especially if infrequent can signal fraudulent activity or account misuse. You might also compute international transaction percentage, which is the number of international transactions over the number of transactions plus one. This measures the proportion of international transactions relative to total activity, helping…

Contents