METHOD FOR REAL TIME FINANCIAL FRAUD DETECTION IN PAYMENT GATEWAYS

Abstract

The present invention provides a method and system for real-time financial fraud detection in payment gateways. The system collects transaction data from payment transactions, such as user identity, payment method, geolocation, and device information, and analyzes it using advanced machine learning models to detect anomalies and fraudulent patterns. By assigning a fraud risk score to each transaction based on its deviation from normal behavior, the system can flag potentially fraudulent transactions in real-time. Alerts are generated and sent to relevant parties, enabling swift actions to prevent fraud. The invention is scalable, adaptive, and continuously learns from new data, improving fraud detection accuracy over time.

Specification

Description:The embodiments of the present invention generally relate to financial fraud detection systems, particularly for real-time identification of fraudulent activities in payment gateways. The invention employs advanced anomaly detection algorithms and machine learning techniques to monitor and analyze financial transactions as they occur, providing a scalable, efficient, and real-time solution to mitigate fraud risks in payment systems.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

As digital payments and online transactions have become increasingly prevalent, the incidence of financial fraud has surged s , Claims:1. A method for real-time financial fraud detection in payment gateways, comprising the steps of:
Collecting transaction data in real-time from a payment gateway, including transaction amount, user identification, payment method, geolocation, and device fingerprint;
Extracting relevant features from the collected transaction data to identify potential fraud indicators;
Applying a machine learning model to analyze the extracted features and detect anomalous patterns that deviate from established user behavior;
Calculating a fraud risk score based on the detected anomalies; and
Sending a real-time alert if the fraud risk score exceeds a predefined threshold.

2. The method of claim 1, wherein the machine learning model is selected from a group consisting of decision trees, random forests, support vector machines (SVMs), and neural networks.

3. The method of claim 1, further comprising the step of continuously updating the machine learning model using new transaction data to improve detection accuracy.

4. The

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