Credit Card Fraud Detection This use case aims to develop a machine learning model capable of identifying fraudulent credit card transactions in real-time. By accurately detecting fraudulent activity, financial institutions can protect their customers from financial losses, reduce operational costs associated with fraud investigations, and maintain customer trust.
Credit Card Fraud Detection at ABC Financial ABC Financial, a major financial institution, is facing common challenges related to credit card fraud. To combat this issue, ABC Financial has decided to implement a machine learning-based fraud detection system. The data scientist's role is to develop a model that can effectively analyze historical transaction data from ABC Financial's customers and identify patterns indicative of fraudulent activity. The model should be able to detect fraud. The model will be deployed in a real-time environment, meaning it must be able to process incoming transactions quickly and accurately to prevent fraudulent activity from occurring. The model's performance will be evaluated based on its ability to accurately detect fraud while minimizing false positives (legitimate transactions incorrectly flagged as fraudulent).
-
A Jupyter notebook containing runnable code and including markdowns. a. Data analysis : EDA b. Model development : Training c. Model assessment : Evaluation
-
A PowerPoint deck showcasing the key findings and the developed model.