> For the complete documentation index, see [llms.txt](https://docs.akkio.com/akkio-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.akkio.com/akkio-docs/demo-models/demo-models/predict-credit-card-fraud.md). # Predict Credit Card Fraud {% embed url="" %} Billions of dollars are lost annually to credit card fraud in the United States. While the majority of credit card transactions are legitimate, detecting fraudulent transactions using traditional methods is challenging. Machine learning, particularly no-code machine learning technology like Akkio, can efficiently detect and prevent credit card fraud by identifying patterns in historical data and creating predictive models. Akkio's no-code machine learning technology allows for the fast and easy development and deployment of fraud detection models, enabling the timely warning of customers about potentially fraudulent transactions. This file has nearly 285,000 rows of real credit card transactions from the EU. Because this credit card information is sensitive, the information in each one of the columns has been encoded using a mechanism called principal component analysis, or PCA. There are 28 different pieces of information that come along with each credit card transaction, along with the size of the transaction, in Euros, and finally whether or not that transaction was fraud. That final column is what we’re trying to predict. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.akkio.com/akkio-docs/demo-models/demo-models/predict-credit-card-fraud.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.