Description:DETAILED DESCRIPTION
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the following description. Specific language will be used to describe the system and method, but it will be understood that no limitation of the scope of the invention is thereby intended. Alterations, modifications, and variations in the illustrated system, and further applications of the principles of the invention as illustrated, are contemplated and would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive. Reference throughout this specification to “an embodiment,” “another embodiment,” or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases “in an embodiment,” “in another embodiment,” and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises," "comprising," or any other variations thereof are intended to cover a non-exclusive inclusion. This means that a process or method comprising a list of steps does not exclude other steps that are not explicitly listed but may be inherent to such processes or methods. Similarly, when one or more devices, sub-systems, structures, components, or elements are described as "comprising... a," this does not, without more constraints, preclude the inclusion of additional devices, sub-systems, structures, components, or elements that may be part of the system or method. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative and should not be construed as limiting.
System Overview
In an embodiment, the invention provides a cybersecurity risk detection system for digital payments comprising multiple security layers. The system consists of:
• A Transaction Monitoring Module that continuously scans payment transactions for suspicious activities.
• A Risk Assessment Engine that applies machine learning algorithms to detect fraudulent transaction patterns.
• An Encryption Mechanism that secures payment credentials and transaction details using cryptographic techniques.
• A Multi-Factor Authentication (MFA) Module that verifies user identity using various authentication factors, such as biometrics or OTP.
• A Blockchain-Based Ledger that ensures transaction immutability and transparency.
• A Real-Time Alert System that triggers notifications when fraudulent activities are detected.
Operation of the System
In an embodiment, when a transaction is initiated, the Transaction Monitoring Module analyzes the transaction data in real-time. If anomalies are detected, the Risk Assessment Engine evaluates the risk score based on historical fraud patterns and machine learning models. If a transaction is deemed high-risk, the MFA Module prompts additional verification. Simultaneously, the Encryption Mechanism ensures that sensitive data is securely transmitted. The transaction details are then stored in a Blockchain Ledger to ensure integrity and prevent tampering. If the system detects a confirmed cyber threat, the Real-Time Alert System triggers notifications to the user and the financial institution, allowing immediate action to be taken.
In another embodiment, the system updates its fraud detection models by continuously learning from new transaction data, thereby enhancing the detection accuracy over time. The blockchain ledger ensures that any unauthorized changes to transaction data are prevented.
In another embodiment, the system integrates artificial intelligence-driven behavioral analysis to detect unusual user activity that might indicate a security breach.
This invention enhances cybersecurity in digital payment systems by leveraging real-time monitoring, AI-based fraud detection, encryption, multi-factor authentication, and blockchain security to provide a comprehensive fraud prevention framework.

, Claims:CLAIMS
I/We claim:
1. A system for detecting cybersecurity risks in digital payment transactions, comprising:
a. A transaction monitoring module configured to analyze payment data for anomalies;
b. A risk assessment engine utilizing machine learning algorithms to identify fraudulent patterns;
c. An encryption mechanism to secure sensitive user credentials;
d. A multi-factor authentication (MFA) module to validate user identity before transaction approval;
e. A blockchain-based ledger for immutable transaction records; and
f. A real-time alert system to notify users and financial institutions of suspicious activities.

2. The system of claim 1, wherein the transaction monitoring module uses artificial intelligence to assess real-time risk factors based on transaction history and user behavior.
3. The system of claim 1, wherein the risk assessment engine is trained using a dataset of past fraudulent transactions to improve detection accuracy.
4. The system of claim 1, wherein the encryption mechanism employs quantum-resistant cryptographic algorithms for enhanced security.
5. The system of claim 1, wherein the MFA module integrates biometric authentication for improved user verification.
6. The system of claim 1, wherein the blockchain-based ledger is designed to store hashed transaction details to ensure privacy and tamper resistance.
7. The system of claim 1, wherein the real-time alert system utilizes automated responses to block or flag suspicious transactions.
8. A method for mitigating cybersecurity risks in digital payments, comprising the steps of:
a. Monitoring digital payment transactions for suspicious activity;
b. Applying machine learning techniques to classify risk levels;
c. Encrypting transaction data to prevent unauthorized access;
d. Verifying user identity through multi-factor authentication;
e. Logging transaction details in a blockchain ledger; and
f. Triggering real-time alerts upon detection of high-risk transactions.
9. The method of claim 8, wherein the machine learning model continuously updates itself based on newly detected fraud patterns to improve accuracy