FraudShield AI
The project uses APIs and machine learning to analyze email, IP, billing, and business metadata for real-time financial fraud detection with confidence scores.
Created on 16th December 2024
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FraudShield AI
The project uses APIs and machine learning to analyze email, IP, billing, and business metadata for real-time financial fraud detection with confidence scores.
The problem FraudShield AI solves
Fraudulent transactions cause massive losses to third-party merchants like Booking.com, with global online payment fraud projected to exceed $43 billion by 2026, according to Juniper Research. Fraudsters exploit stolen cards to complete bookings, benefiting from successful transactions, while merchants suffer chargeback losses and reputational damage when fraud is detected later. FraudShield AI addresses this by providing real-time fraud detection through advanced AI models and unique metadata integration, proactively flagging suspicious transactions to prevent them from being processed. This reduces chargebacks, minimizes false positives and negatives, and enhances decision accuracy with customizable scoring metrics tailored to business needs. By making fraud detection safer, more efficient, and scalable across industries, FraudShield AI protects merchants from significant financial and operational losses while streamlining their risk management processes.
Challenges we ran into
During the development of our project using React, Vite, Python, and Flask, we faced several challenges. One major issue was the lack of sufficient labeled data online, which required us to generate synthetic data to train our model effectively. This process took considerable effort to ensure the data was realistic and suitable for training. We also had to integrate other APIs into our main inference engine to improve the confidence score of predictions, which added complexity to the system. Additionally, integrating the API between the React frontend and Flask backend was initially challenging due to CORS errors and issues with asynchronous request handling. We resolved these by configuring the CORS middleware in Flask and adjusting the proxy settings in Vite. Despite these hurdles, we successfully integrated the APIs and trained a robust model that delivered the desired results.