Machine Learning for Credit Card Fraud Detection

Credit card fraud poses significant financial and reputational risks, with billions lost annually. Machine learning (ML) is crucial in combating this issue by enabling rapid and accurate detection of fraudulent transactions. This research report highlights ML techniques like Decision Trees, Random Forest, and more, which improve fraud detection rates and reduce false positives. By leveraging ML, financial institutions can enhance security, minimize losses, and maintain customer trust, making it a vital tool in the fight against credit card fraud.

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Research Report

Read the Research Report below ⬇️

BSFS ML for credit card fraud detection.pdf

Download PDF • 3.79MB

You can open the Python code file below through Google Collaborate ⬇️

ML_for_fraud_detection_code.ipynb

Download IPYNB • 23KB

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Project Team :

Project Leader: Gleb Legotkin

Analysts: Andrea Botero Herrera, and Vlad Marinescu.

Association Board :

Guillaume Abaz (President), Michelangelo Mauro (Vice President), Catharina Gärtner (Head of M&A), Gleb Legotkin (Head of Data Analysis), Matteo Nesiti ( Head of Operations).