Django Projects
0.0 (0 reviews) • 0 downloads
Credit Card Fraud LSTM BILSTM in Python Projects
र 1000
Credit Card Fraud LSTM BILSTM in Python Projects
Technical Details
Domain : Python
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
Database : Sqlite
Tools : Anaconda
Run Tools: VS Code
Secure Payment
Instant Download
GST Invoice
24/7 Support
About This Product
Credit Card Fraud LSTM BILSTM in Python Projects
Abstract
Credit card fraud is a major concern in the financial industry, leading to significant monetary losses each year. Traditional fraud detection systems often struggle to capture sequential transaction patterns and adapt to evolving fraudulent behaviors. This project, Credit Card Fraud Detection Using LSTM and BiLSTM in Python, leverages deep learning models to identify fraudulent transactions by analyzing temporal and sequential dependencies in credit card data. Using Python libraries such as TensorFlow/Keras, Pandas, NumPy, and Scikit-learn, the system preprocesses transaction datasets, normalizes features, and trains Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models to classify transactions as genuine or fraudulent. The approach enhances accuracy, minimizes false positives, and provides a scalable fraud detection solution.
Existing System
Current fraud detection methods rely on rule-based systems, statistical analysis, or basic machine learning algorithms such as Decision Trees, Logistic Regression, or Random Forest. While effective for detecting known fraud patterns, these systems struggle with dynamic and sophisticated fraudulent transactions. They often produce high false positive rates, incorrectly flagging legitimate transactions as fraudulent. Additionally, conventional methods fail to exploit sequential dependencies in transaction data, limiting their ability to capture temporal fraud patterns.
Proposed System
The proposed system introduces a deep learning–based fraud detection framework using LSTM and BiLSTM models. The workflow includes data preprocessing (handling class imbalance using SMOTE or undersampling, feature scaling, and sequence preparation), model training with LSTM and BiLSTM networks to capture forward and backward dependencies, and classification of transactions into fraudulent or genuine. BiLSTM further enhances prediction by considering both past and future contexts in transaction sequences. Compared to existing approaches, this system provides higher accuracy, robustness against evolving fraud tactics, and lower false positives, making it suitable for real-time financial fraud detection in large-scale credit card systems.
Abstract
Credit card fraud is a major concern in the financial industry, leading to significant monetary losses each year. Traditional fraud detection systems often struggle to capture sequential transaction patterns and adapt to evolving fraudulent behaviors. This project, Credit Card Fraud Detection Using LSTM and BiLSTM in Python, leverages deep learning models to identify fraudulent transactions by analyzing temporal and sequential dependencies in credit card data. Using Python libraries such as TensorFlow/Keras, Pandas, NumPy, and Scikit-learn, the system preprocesses transaction datasets, normalizes features, and trains Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models to classify transactions as genuine or fraudulent. The approach enhances accuracy, minimizes false positives, and provides a scalable fraud detection solution.
Existing System
Current fraud detection methods rely on rule-based systems, statistical analysis, or basic machine learning algorithms such as Decision Trees, Logistic Regression, or Random Forest. While effective for detecting known fraud patterns, these systems struggle with dynamic and sophisticated fraudulent transactions. They often produce high false positive rates, incorrectly flagging legitimate transactions as fraudulent. Additionally, conventional methods fail to exploit sequential dependencies in transaction data, limiting their ability to capture temporal fraud patterns.
Proposed System
The proposed system introduces a deep learning–based fraud detection framework using LSTM and BiLSTM models. The workflow includes data preprocessing (handling class imbalance using SMOTE or undersampling, feature scaling, and sequence preparation), model training with LSTM and BiLSTM networks to capture forward and backward dependencies, and classification of transactions into fraudulent or genuine. BiLSTM further enhances prediction by considering both past and future contexts in transaction sequences. Compared to existing approaches, this system provides higher accuracy, robustness against evolving fraud tactics, and lower false positives, making it suitable for real-time financial fraud detection in large-scale credit card systems.
Customer Reviews (0)
No reviews yet. Be the first!
Related Products
⭐ Featured
⭐ Featured
Django Projects
Music Event Booking App in Django Python
Music Event Booking App in Django Python
र 1000
⭐ Featured
Django Projects
Multi Server Management System in Django Python
Multi Server Management System in Django Python
र 1000
⭐ Featured
Django Projects
Multi Authority Access Control in Django Python
Multi Authority Access Control in Django Python
र 1000