krunal-nagda/Credit-Card-Fraud-Detection-Capstone-Project---Decision-Tree-and-Random-Forest
In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.
This project helps banking analytics teams build a system to automatically flag suspicious credit card transactions. By inputting historical transaction data, it identifies potentially fraudulent activities, reducing manual review time and preventing financial losses from chargebacks. It's designed for data analysts or fraud prevention specialists within financial institutions.
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Use this if you are a banking analytics professional needing to develop a machine learning model for credit card fraud detection.
Not ideal if you are looking for a pre-built, production-ready fraud detection system rather than a framework for building one.
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May 20, 2021
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