Abstract
This report proposes an approach to detect anomalies in credit card data using machine learning techniques. Credit card data is prone to fraudulent transactions, and detecting anomalies can help minimize financial losses. The proposed approach involves preprocessing the data by removing missing values and outliers, reducing the number of features using dimensionality reduction techniques, and using supervised learning algorithms such as LSTM, MLP, and ANN methods to identify anomalies.
We compare the performance of different algorithms and select the best algorithm based on its ability to accurately detect anomalies. The selected algorithm is evaluated using metrics such as precision, recall, and F1-score. Our results demonstrate the effectiveness of the proposed approach in accurately identifying anomalies in credit card data. The proposed approach can be extended to other types of financial data to improve security and prevent fraud in the financial industry.
In summary, the proposed approach to anomaly detection for credit card data using machine learning techniques can help financial institutions detect fraudulent transactions and minimize financial losses. The report provides a comprehensive analysis of the approach and its effectiveness, making it a valuable resource for researchers and practitioners in the field of financial fraud detection.