With the rise of e-commerce and online transactions, fraudulent activities have also increased. Fraudulent activities not only cause financial losses but also lead to a loss of reputation for businesses. Therefore, it is essential to have an automated system in place that can detect fraud in real-time.

The objective of this project is to develop an automated fraud detection system using deep learning techniques to detect and prevent fraudulent activities in real-time.

The proposed system will use deep learning algorithms, such as neural networks, to detect patterns and anomalies in transaction data. The system will be trained on a dataset of historical transactions that include both fraudulent and non-fraudulent activities. The system will learn to recognize patterns and identify fraudulent transactions based on the features extracted from the dataset.The system will employ several techniques to improve the accuracy of fraud detection, including data preprocessing, feature engineering, and model tuning. The system will also be able to adapt to changing patterns of fraudulent activity and update its models accordingly.

The system is expected to achieve high accuracy in detecting fraudulent transactions in real-time, thereby reducing financial losses for businesses. The system will also be scalable, meaning it can handle large volumes of transactions without compromising its accuracy.

In fraud detection, the number of fraudulent transactions is usually much lower than the number of legitimate transactions. This leads to an imbalanced dataset, which can negatively impact the performance of the model. The model may become biased towards the majority class and may fail to detect fraudulent transactions. To tackle this problem I have used oversampling method which is Synthetic Minority Oversampling Technique (SMOTE)

Outliers are data points that are significantly different from the rest of the dataset. In fraud detection, outliers can be an indication of fraudulent activity. However, outliers can also be caused by errors in data collection or processing. It is important to identify and handle outliers appropriately to avoid false positives or false negatives.

Deep learning models are often considered "black boxes" because they are difficult to interpret. This can make it challenging to understand how the model arrived at its decision, which can be a problem when trying to explain the results to stakeholders.

The proposed automated fraud detection system using deep learning techniques will help businesses prevent financial losses and protect their reputation. It will be an essential tool for e-commerce platforms, financial institutions, and other businesses that deal with online transactions.

https://huggingface.co/spaces/ameya123ch/fraud_detection