DETECTING ANOMALIES IN FINANCIAL TRANSACTIONS:

This project aims to detect anomalies in financial transactions using machine learning algorithms.

Goal

• Enhance fraud detection through improved feature engineering, incorporating transaction frequency and user behavior.
• Implement real-time monitoring with sub-second latency, providing prompt insights into transaction patterns.
• Utilize behavioral analytics to identify evolving patterns, contributing to the reduction in false positives.

Methodology

• Three machine learning models are employed: Logistic Regression, Decision Tree, and XGBoost.
• Each model predicts anomalies independently based on the features engineered.
• The predictions of the three models are combined using a Majority Voting rule.
• The final prediction is determined by the majority vote of the three models, ensuring a more robust result.

How to Run the Project

1. Clone the project repository by executing the following command in the terminal:

   git clone https://github.com/yukii1004/Daft
   

2. Navigate to the project directory.

3. Install the required dependencies by running:

   pip install -r Requirements.txt
   

4. Finally, run the web application by executing:

   streamlit run App.py
   

Team Members:

• Abhinav
• Nitin Karthick RP
• Roahith R
• Rudranarayan Panda