A Hybrid Blockchain Architecture Employing ML Models for Real-time Fraud Detection System

Abstract

The present invention relates to a hybrid blockchain architecture employing ML models for real-time fraud detection system. The proposed system is designed to secure financial transactions, e-commerce, and online services. The proposed system leverages blockchain’s transparency and immutability with ML’s predictive capabilities to detect fraud instantly. It integrates public and private blockchains, ensuring privacy by securing sensitive data while allowing transparency for non-sensitive information. Federated ML models continuously monitor transactions for suspicious patterns, activating alerts and preventative actions without centralized data repositories, preserving user privacy. Smart contracts automate response actions upon fraud detection, and oracles provide real-time external data. This adaptive, privacy-preserving system maintains high accuracy through continuous feedback and model updates, delivering a secure and efficient solution for fraud detection across various sectors.

Specification

Description:TECHNICAL FIELD OF INVENTION

The present invention relates to a hybrid blockchain architecture employing ML models for real-time fraud detection system.

BACKGROUND OF THE INVENTION

The background information herein below relates to the present disclosure but is not necessarily prior art.

The rise of digital transactions has provided unparalleled convenience but also heightened the risks of fraud. With increasing online financial activities, traditional fraud detection systems struggle to cope with the dynamic and evolving nature of fraudulent tactics. Despite improvements in cybersecurity, centralized fraud detection models often face limitations, such as delayed response times, privacy vulnerabilities, and dependency on a single point of failure, leaving them susceptible to increasingly sophisticated attacks. In response, decentralized architectures, like blockchain, have emerged as robust solutions, valued for their transparency, security, and immutability. Blockchain's distributed ledger ensures that once data is recorded, it cannot be altered, making it ideal for tracking transactions and preventing unauthorized alterations. However, while blockchain enhances transparency, it lacks intrinsic intelligence to detect fraudulent patterns autonomously.

Machine Learning (ML), on the other hand, has proven highly effective in analyzing complex patterns and behaviors to predict and identify anomalies. Leveraging ML in fraud detection has become an industry standard for its ability to learn from vast amounts of data and recognize subtle indicators of fraud that human analysts or rule-based systems might miss. Yet, these ML models often require centralized data repositories for training, posing privacy and security risks, especially with sensitive financial data. To address these challenges, a hybrid solution that combines the best features of blockchain's security and ML's predictive analytics offers a promising pathway for real-time fraud detection.

This invention introduces a Hybrid Blockchain Architecture Employing ML Models for Real-time Fraud Detection System, a pioneering system designed to provide instantaneous fraud detection and prevention by synergizing blockchain's transparency and ML's analytical precision. This hybrid approach is characterized by the interplay of public and private blockchain components, which manage transaction data with dual levels of security, and decentralized ML models that offer real-time insights into transactional anomalies. By splitting the blockchain into private and public components, sensitive information is protected within the private blockchain, while non-sensitive transactional metadata is openly recorded on the public blockchain, maintaining both data privacy and transparency.

This novel architecture thus addresses the core challenges in fraud detection: protecting user privacy, maintaining transparency, achieving real-time responsiveness, and adapting to emerging fraud patterns.

There are various drawbacks prior art/existing technology. Hence there was a long felt need in the art.

OBJECTIVE OF THE INVENTION

The primary objective of the present invention is to provide a hybrid blockchain architecture employing ML models for real-time fraud detection system.

This and other objects and characteristics of the present invention will become apparent from the further disclosure to be made in the detailed description given below.

SUMMARY OF THE INVENTION

Accordingly, the following invention provides a hybrid blockchain architecture employing ML models for real-time fraud detection system. The proposed system enhances real-time fraud detection across financial transactions and online services. By combining blockchain's transparency with ML's predictive capabilities, the system detects and mitigates fraudulent activities while safeguarding sensitive data. A hybrid blockchain design employs public and private blockchains to protect user privacy and enable secure, efficient data sharing.

Federated learning models analyze transactions across multiple nodes without centralizing data, preserving privacy. Smart contracts automate responses to detected fraud, while oracles provide real-time external data. The system continuously updates its ML models using flagged transactions to adapt to evolving fraud tactics, ensuring proactive fraud prevention.

DETAILED DESCRIPTION OF THE INVENTION

As used in the description herein and throughout the claims that follow, the meaning of "a," "an," and "the" includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.

The present invention is related to a hybrid blockchain architecture employing ML models for real-time fraud detection system.

The present invention discloses a cutting-edge system combining blockchain and machine learning (ML) technologies to enhance real-time fraud detection in financial transactions, e-commerce, and online services. The system leverages the immutability and transparency of blockchain with the predictive capabilities of ML to detect and mitigate fraudulent activities instantly.

The system operates on a hybrid blockchain architecture, where private and public blockchains interact to secure sensitive data and support computational efficiency. Transactions are continuously monitored by ML models that identify suspicious patterns, trigger alerts, and initiate preventive actions. The use of federated learning models ensures that user privacy is maintained across multiple nodes, avoiding centralized data repositories.

The proposed system comprises of following key components:

Hybrid Blockchain Network: Utilizes both public and private blockchain structures, enabling secure data sharing across organizations while protecting sensitive data. The public blockchain holds non-sensitive transaction data, while the private blockchain safeguards critical information like user credentials and financial details.

Machine Learning (ML) Fraud Detection Models: ML models are trained on historical fraud data and are deployed within the blockchain network to detect anomalous transaction patterns in real-time. Advanced models, such as anomaly detection with convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), are employed for pattern recognition.

Federated Learning System: Ensures that models can be trained on decentralized data without centralizing user information, preserving privacy and reducing computational bottlenecks.

Smart Contracts: Autonomous scripts within the blockchain that automatically execute predefined actions (such as freezing a transaction) when the ML model flags a high-risk activity.

Oracles for Data Integration: Oracles are employed to gather real-time data from external sources, including transaction histories, customer profiles, and behavioral analytics, ensuring the fraud detection system remains informed by up-to-date information.

Methodology:

Data Collection and Preprocessing: The system collects transactional data from various sources, including customer behavior data and historical fraud data. This data is sanitized and preprocessed before being fed into ML models to train them on legitimate and fraudulent transaction patterns.

Federated Learning for Decentralized Model Training: The ML models are trained across multiple nodes in a decentralized manner using federated learning, which eliminates the need for centralized data storage. Each participating node (e.g., bank branches or e-commerce servers) trains the model locally and shares only the model weights with the main server. This approach reduces data breaches and privacy concerns.

Hybrid Blockchain Architecture Implementation: Public Blockchain: Transaction metadata (non-sensitive) is stored in the public blockchain to enhance transparency and accountability across organizations.

Private Blockchain: Sensitive information (e.g., user credentials) is stored in the private blockchain, which is accessible only by authorized parties. Smart contracts ensure that transactions are validated and secure while maintaining compliance with regulations.

Real-Time Fraud Detection Using ML Models: As transactions occur, the ML models deployed on the blockchain nodes analyze them in real-time. If a transaction exhibits unusual behavior (e.g., high-value transfer from a rarely used account), the model flags it, triggering an alert. In severe cases, the system initiates automatic actions (e.g., blocking the transaction or alerting relevant parties) using pre-programmed smart contracts.

Blockchain-Orchestrated Anomaly Response: Once a transaction is flagged as suspicious, a corresponding entry is created in the public blockchain to maintain transparency. A notification is sent to the concerned institution, allowing them to review the transaction and take further action.

Feedback and Model Update Loop: Flagged transactions are continuously used as feedback to update the ML models, ensuring they adapt to evolving fraud tactics. This feedback loop enhances the accuracy of future predictions, fostering a proactive fraud detection system.

System Architecture:

Data Layer: Private Blockchain Network: Stores sensitive transaction data with restricted access. Public Blockchain Network: Records anonymized metadata of transactions, ensuring transparency across organizations.

Processing Layer:

Federated Learning Module: Manages decentralized ML model training and updates.

ML Model Pool: Contains various anomaly detection models that analyze transaction data.

Data Oracles: Integrate real-time data from external sources, ensuring up-to-date information for accurate detection.

Detection and Execution Layer:

Smart Contracts Module: Executes predefined responses to flagged transactions, ensuring automated response to potential fraud.

Real-Time Transaction Monitor: Continuously scans transactions for fraud patterns based on ML model predictions.

Alert System: Sends notifications to authorized personnel or blocks high-risk transactions based on risk thresholds.

User Interface Layer:

Admin Dashboard: Displays analytics, flagged transactions, and system status for authorized users.

Real-Time Alerts and Notifications: Provides instant alerts through SMS, email, or app notifications for quick responses to flagged activities.

Unique Attributes:

Hybrid Blockchain for Privacy and Transparency: The dual structure of public and private blockchains optimizes transparency without compromising sensitive data.

Decentralized ML Training through Federated Learning: Federated learning allows model training without centralizing sensitive user data, preserving user privacy.

Smart Contracts for Automated Fraud Response: With smart contracts, the system can autonomously respond to fraud, increasing efficiency and reducing manual intervention.

Continuous Learning Capability: The ML models use flagged transaction feedback for ongoing improvements, adapting to new fraud methods and maintaining high detection accuracy.

Applications:

Financial Institutions: Real-time fraud detection for banks, credit card providers, and online payment processors.

E-commerce Platforms: Secure transaction verification to protect buyers and sellers.

Government Services: Fraud prevention in online identity verification, tax processing, and public service portals.

This innovative hybrid blockchain system demonstrates a secure, privacy-preserving, and continuously learning architecture for real-time fraud detection, offering unparalleled transparency and efficiency across sectors.

While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.
, Claims:1. A hybrid blockchain architecture employing ML models for real-time fraud detection system, comprising:

a hybrid blockchain network with interconnected public and private blockchains, wherein the public blockchain stores non-sensitive transaction metadata for transparency, and the private blockchain stores sensitive information accessible only by authorized entities to ensure data privacy;

a machine learning (ML) fraud detection module integrated within the blockchain network, wherein ML models are trained on historical fraud data to identify anomalous transaction patterns in real-time using advanced algorithms including anomaly detection, convolutional neural networks (CNNs), and graph neural networks (GNNs);

a federated learning system that enables decentralized model training across multiple nodes without centralizing user data, thereby preserving data privacy while reducing computational bottlenecks;

a smart contracts module that executes predefined responses, such as transaction blocking, when the ML models flag high-risk activities, ensuring automated and immediate responses to potential fraud; and

oracles for data integration, gathering real-time data from external sources, such as transaction histories and behavioral analytics, to inform ML models and maintain system accuracy,

wherein the architecture provides a privacy-preserving, transparent, and adaptive system for real-time fraud detection across financial transactions, e-commerce platforms, and online services.

Documents