Credit Card Fraud Detection

[Keerthi421]

Problem Description: Fraudulent transactions are a major threat in banking and insurance, causing substantial financial losses and damaging customer trust. Swift, accurate fraud detection is essential to mitigate these risks. By implementing a real-time fraud detection model, businesses can identify suspicious transactions as they happen, reducing fraud and enhancing security for their customers.

Model Description: The model will use classification algorithms like Random Forest, Gradient Boosting, and Neural Networks to analyze transaction data, including amount, time, and user behavior. SMOTE and Undersampling will address the imbalanced nature of fraud data, while Isolation Forest will detect unusual, potentially fraudulent transactions not labeled in the dataset. This combination of techniques will ensure that the model is optimized to catch fraud quickly and accurately.

Estimated Time for Completion: Implementation: 1-2 weeks for data prep, model selection, and training. Testing: 1 week for fine-tuning and metric evaluation. Deployment: 1 week for API integration and real-time functionality. Expected Outcome: The model will enable highly precise, real-time fraud detection with minimal false positives, allowing businesses to prevent fraud before it impacts their bottom line. Key metrics include AUC-ROC, Precision, Recall, and F1-Score, which are critical for accurate fraud detection.

Additional Context: Initially, public datasets like the Kaggle Credit Card Fraud Detection dataset will be used, with real-world data further refining the model. Once deployed, the model will provide seamless transaction security, establishing businesses as secure, customer-focused organizations.

Please assign me this issue . I would like to work on this

@yashasvini121

[yashasvini121]

Sure @Keerthi421, go ahead!