UPI FRAUD DETECTION USING MACHINE LEARNING

Approved

Relevance and Originality

This project tackles a critical issue in the digital payment landscape by focusing on Unified Payments Interface (UPI) and its growing vulnerability to fraud. The relevance of developing a machine learning-based fraud detection system is underscored by the increasing adoption of UPI, making it imperative to enhance its security. The originality of the project lies in its tailored approach to using historical transaction data, which allows for the identification of unique patterns specific to UPI, thus contributing to existing research and solutions in financial technology.

Methodology

The project methodology revolves around leveraging historical transaction data to build a machine learning model for fraud detection. While this approach is sound, further details on the specific algorithms to be used and the criteria for model training and validation would strengthen the methodology. Additionally, outlining the data preprocessing steps and how the model will handle imbalanced datasets, which is common in fraud detection, would enhance the rigor of the study.

Validity & Reliability

The project's validity is supported by its focus on real transaction data and its objective to accurately differentiate between fraudulent and legitimate activities. To improve reliability, it would be beneficial to specify the sources of historical data and any measures taken to ensure its quality and representativeness. Discussing potential biases in the data and how they may impact the model's performance would also enhance the credibility of the findings.

Clarity and Structure

The project is structured clearly, outlining the objectives and the significance of the proposed solution. However, improving the clarity of technical terms related to machine learning and fraud detection would make the project more accessible to a broader audience. Organizing the content into well-defined sections—such as background, methodology, expected outcomes, and implications—would improve the overall flow and readability.

Result Analysis

The anticipated result analysis focuses on enhancing the security of UPI transactions by reducing false positives and accurately identifying fraudulent activities. To enrich the analysis, providing specific metrics for evaluating model performance, such as precision, recall, and F1 score, would offer a clearer picture of the system's effectiveness. Additionally, discussing the potential implications of successful implementation, including impacts on user trust and financial institution operations, would provide valuable insights into the project's significance in the broader context of digital payments.