Intelligent payment authentication and fraud detection system for secure e-commerce transactions

Abstract

Intelligent payment authentication and fraud detection system (100) for secure e-commerce transactions, comprising: a biometric authentication module configured to verify a user's identity using fingerprint recognition and / or facial recognition; an artificial intelligence-based fraud detection engine operatively connected to the biometric authentication module and configured to: Analyze device fingerprints, including IP address, browser metadata, and geolocation; monitor user behavior, including typing speed, click patterns, and transaction history; Assessing the transaction context, including amount, merchant category and timing; Real-time transaction risk classification using a hybrid AI model combining supervised, unsupervised, and reinforcement learning techniques; a dynamic payment authorization module configured to generate a one-time, encrypted QR code or payment token to confirm high-risk transactions; a blockchain-based fraud logging module that is in communication with the fraud detection engine and is configured to: Recording detected fraudulent activities in a decentralized ledger; Implementation of zero-knowledge proof mechanisms to enable the sharing of fraud information between financial institutions while preserving privacy; a secure communications module configured to establish encrypted connections using at least AES-256 and RSA protocols for transaction processing and cross-platform data transfer.

Description

[0001] The present invention relates to the field of digital payment infrastructure and cybersecurity, and more particularly to an intelligent payment authentication and fraud detection system for use in electronic commerce and electronic transactions.

[0002] With the rapid growth of e-commerce and digital payment platforms, the frequency and sophistication of fraudulent activities have also increased. Incidents such as card-not-present (CNP) fraud, account takeover, and unauthorized transactions have become increasingly common and pose a significant threat to both consumers and financial institutions. Traditional fraud detection systems, which primarily rely on rule-based algorithms and traditional multi-factor authentication (MFA), are proving inadequate to address these evolving threats.

[0003] These existing systems often suffer from high false positive rates, resulting in legitimate transactions being incorrectly classified as fraudulent. This is not only frustrating for users but also undermines the credibility and operational efficiency of service providers. Furthermore, many authentication mechanisms—particularly those relying on one-time passwords (OTPs) sent via SMS or email—are vulnerable to social engineering attacks such as phishing or SIM swapping. These vulnerabilities expose users to potential identity theft and financial loss, further undermining trust in digital payment systems. In addition to these technical deficiencies, usability remains a major issue. The need for manual verification steps and frequent interruptions for authentication can make the payment process cumbersome and inefficient.To overcome these limitations, there is an urgent need for a seamless, intelligent and secure fraud detection and authentication system that can operate in real time and adapt to new threats.

[0004] To solve this problem, the present invention provides an intelligent authentication and fraud detection system for secure e-commerce transactions.

[0005] The system aims to provide a unified, intelligent payment authentication and fraud detection system that improves the security, reliability and usability of e-commerce and digital payment transactions.

[0006] The system is designed to perform real-time fraud detection using a hybrid artificial intelligence model that combines supervised learning for known fraud patterns, unsupervised learning for anomaly detection, and reinforcement learning for continuous performance improvement based on feedback.

[0007] The system includes biometric authentication capabilities, including fingerprint scanning and facial recognition, to ensure secure identity verification and eliminate reliance on traditional OTP-based methods that are vulnerable to phishing, SIM swapping, and other social engineering attacks.

[0008] The system uses a blockchain-based fraud logging mechanism to create a decentralized, tamper-proof record of fraudulent activity. By using zero-knowledge proofs (ZKPs), the system ensures the privacy-preserving exchange of fraud information between banks, financial institutions, and merchants.

[0009] The system generates dynamic payment authorization tokens, including one-time QR codes or encrypted tokens, that must be verified by the user before transaction approval. This minimizes the risk of replay attacks and ensures that high-risk transactions are securely validated.

[0010] The system supports end-to-end encrypted communication through advanced cryptographic protocols such as AES-256 and RSA, ensuring secure data transmission across multiple channels such as NFC, Bluetooth, and secured cloud environments.

[0011] The system is designed to integrate seamlessly with existing digital payment infrastructures, including point-of-sale (POS) terminals, e-commerce gateways, and mobile wallet platforms, without requiring significant changes to their architecture or workflows.

[0012] The system ultimately provides a scalable, intelligent, and privacy-friendly solution that reduces false positives, prevents pre-transaction fraud, and strengthens user trust in digital financial systems.

[0013] In one embodiment, the present invention provides an intelligent payment authentication and fraud detection system for secure e-commerce transactions. The system integrates biometric verification, artificial intelligence-based fraud detection, and blockchain-based fraud logging into a single, unified framework. The system is designed to secure digital and e-commerce transactions by analyzing transaction data, user behavior, and device fingerprints in real time to assess risk. It employs a hybrid AI model that incorporates supervised, unsupervised, and reinforcement learning techniques to identify known fraud patterns, uncover anomalies, and continuously improve accuracy. If suspicious activity is detected, the system prompts for additional authentication through biometric modules such as fingerprint or facial recognition.For high-risk transactions, it generates dynamic QR codes or tokens for secure payment authorization, reducing reliance on vulnerable OTPs and increasing resilience against social engineering attacks.

[0014] The system also includes a blockchain-based fraud logging component that ensures a decentralized, tamper-proof record of fraudulent transactions. This ledger, protected by zero-knowledge proofs (ZKPs), enables the secure exchange of fraud information between banks, merchants, and financial institutions without compromising user privacy. The system is designed to integrate seamlessly with existing payment infrastructures such as POS terminals, mobile wallets, and e-commerce platforms. Its multi-layered architecture, which combines hardware and software security measures, enables a robust, scalable, and user-friendly approach to payment authentication and fraud prevention.By intelligently analyzing user behavior, device metadata, and transaction context, the system significantly reduces false alarms while ensuring secure and convenient digital transactions. The invention is explained again below with reference to the figure. It shows: Fig. : an intelligent authentication and fraud detection system for secure e-commerce transactions.

[0015] Fig.demonstrates an intelligent authentication and fraud detection system for secure e-commerce transactions. The system (100) consists of an integrated architecture of hardware and software modules that provide intelligent, real-time fraud prevention and secure transaction authentication. At the heart of the system (100) is a hybrid, AI-powered fraud detection engine that analyzes multiple parameters, including device fingerprints (IP address, browser metadata, geolocation), user behavior (typing speed, click patterns, transaction history), and transaction context (amount, time, merchant category). The engine uses a combination of supervised learning to detect known fraud signatures, unsupervised learning to detect anomalies, and reinforcement learning for continuous improvement based on emerging fraud trends.If a transaction is deemed high-risk, the system (100) triggers biometric authentication via fingerprint or facial recognition. A liveness detection feature also provides protection against spoofing attacks such as deepfakes.

[0016] To increase security and enable cross-platform exchange of fraud information, the system (100) includes a blockchain-based fraud logging module. This tamper-proof, decentralized ledger records failed or suspicious authentication attempts and detected fraud events. By using zero-knowledge proofs (ZKPs), the system (100) enables the secure and private exchange of fraud-related data between financial institutions and merchants without exposing sensitive user data. Furthermore, the system (100) includes a dynamic payment authorization module that generates short-lived encrypted QR codes or tokens for transaction approval, thus reducing the risk of replay or man-in-the-middle attacks.Communication between system components is secured using AES-256 and RSA encryption protocols, and integration is supported across various platforms such as POS terminals, mobile wallets, and e-commerce applications. The system (100) thus provides a comprehensive, adaptable, and privacy-preserving approach to transaction security. List of reference symbols 100 systems

Claims

[1] Intelligent payment authentication and fraud detection system (100) for secure e-commerce transactions, which includes the following: a biometric authentication module configured to verify a user's identity using fingerprint and / or facial recognition; an artificial intelligence-based fraud detection engine that is operationally linked to the biometric authentication module and configured to: To analyze device fingerprints, including IP address, browser metadata and geolocation; to monitor user behavior, including typing speed, click patterns, and transaction history; Evaluation of the transaction context, including amount, merchant category and timing; Real-time classification of transaction risk using a hybrid AI model that combines supervised, unsupervised, and reinforcement learning techniques; a dynamic payment authorization module configured to generate a unique, encrypted QR code or payment token to confirm high-risk transactions; a blockchain-based fraud logging module that communicates with the fraud detection engine and is configured to: Recording of detected fraudulent activities in a decentralized ledger; Implementation of zero-knowledge proof mechanisms to enable the exchange of fraud information between financial institutions while respecting privacy; a secure communication module configured to establish encrypted connections using at least AES-256 and RSA protocols for transaction processing and cross-platform data transfer. [2] System (100) according to claim 1, wherein the biometric authentication module comprises activity detection to prevent spoofing attacks using images, videos or deepfake technologies. [3] System (100) according to claim 1, wherein the AI-based fraud detection machine analyzes device fingerprints including IP address, browser metadata and geolocation. [4] System (100) according to claim 1, wherein the AI-based fraud detection engine monitors user behavior patterns such as typing speed, click time and historical transaction data. [5] System (100) according to claim 1, wherein the blockchain-based logging module uses a private, authorized blockchain network to ensure secure and efficient validation across financial institutions. [6] System (100) according to claim 1, wherein the dynamic payment authorization module is configured to generate short-lived encrypted tokens that expire after a single use or a defined period of time. [7] System (100) according to claim 1, wherein the secure communication module supports multi-channel authentication, including NFC, Bluetooth and cloud-based encrypted transmission. [8] System (100) according to claim 1, wherein the fraud detection machine is continuously updated by reinforcement learning and improves the accuracy based on the results of past transactions.

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