Generative AI-Driven Credit Card Fraud Detection System with Block Chain based Cybersecurity Enhancement”

Abstract

ABSTRACT The fast development in digital transactions has paved a way to the surge in credit card fraud, causing major challenges for consumers and financial sectors. Conventional fraud detection techniques frequently struggle to cope up with the modern cyber-attacks that requires a need for ground-breaking solutions. This state of the art solution provides an AI-driven credit card fraud detection system, enhanced with blockchain-based cybersecurity system which is more robust in detecting real-time fraudulent activities. Using generative AI, the system continuously learns and adapts to evolving fraudulent patterns by generating artificial but realistic fraudulent scenarios, enhancing the detection capability of the model. The decentralized and tamper-resistant ledger capabilities of Blockchain technology is incorporated to safeguard transaction data and improve the trustworthiness, thereby reducing vulnerabilities exploited by the fraudsters

Specification

Description:The following specification particularly describes the nature of the invention and the manner in which it is performed:
FIELD OF THE INVENTION:
[001]The contemporary development relates to the arena of detection of fraudulent services in financial transactions, precisely related to the development of a structure for the credit card fraud detection through the application of generative artificial intelligence (Gen-AI) incorporated with cutting-edge block chian based cybersecurity practices to protect the delicate financial data and deliver real-time finding of fraudulent activities.
BACKGROUND OF THE INVENTION:
[002] The description that follows provides the necessary information that is required to realize the current invention
[003]Credit card fraud remains a substantial threat in business and financial activities globally. Conventional fraud detection methods make use of historical data with certain predefined rules for the detection of fraud. Conversely, these schemes tussle to identify dynamic fraudulent systems or adjust to the promptly progressing threats.
[004]In the meantime, cybersecurity openings increase additional risk layers, demanding for the strong security methods and tools to safeguard the cardholder data.
[005]Hence it is required to have an innovative system that can overcome the limitations and challenges that is caused by the existing system
[006]Generative AI, with its capability to mimic incredible data, boosts the prospect to produce probable fraudulent patterns, enhancing the discovery of hidden fraud circumstances. Though the current fraud detection schemes do not completely influence this technology in concurrence with real-time cybersecurity to guard the sensitive financial data. This invention strives to get control over these restrictions by offering an integrated system that adopts generative AI for the detection of credit card fraud and innovative cybersecurity schemes to guard the transaction data.
SUMMARY OF THE PRESENT INVENTION:
[007]The current invention presents a generative AI-driven credit card fraud detection system that mimics prospective fraudulent patterns and acquires from real as well as artificial data to identify real time credit card frauds. In addition this integrates an improved cybersecurity level that affords strong security against cyberattacks including unauthorized access, data breaches and guaranteeing that sensitive transaction data and credit card stay protected. It consists of
(i) A generative AI module that generates unreal fraud scenarios built on real time and historical transaction data.
(ii)A fraud detection engine that integrates real and unreal data to decrease false positives and increase accuracy.
(iii)An improved cybersecurity level that safeguards the transaction system through encryption, real-time threat monitoring and multi-factor authentication (MFA).
(iv)Continuous learning algorithms that inform fraud detection models based on evolving threats, new data and synthetic simulations.
(v)A blockchain ledger for secure transaction validation, ensuring transparency and immutability.
BRIEF DESCRIPTION OF THE DRAWINGS
[008] The diagram provides an additional understanding of current work and is included in and comprises a part of description.
Figure 1 presents the various components of the credit card fraud detection system containing the following components and its description
User Interface: This interface helps the user to interact with the system
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API Gateway: This gateway acts as a central point in connecting the user interface with the backend components
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User Transactions: This component is used to accept the transactions from the users
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Data Collection: This component aggregates all transaction data, user activity logs, and other relevant metadata.
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Generative AI Fraud Detection Model: This component uses machine learning to detect fraudulent transactions
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Anomaly Detection: This is used to identify suspicious patterns from the normal user's behavior
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Alerts & Actions
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Block chain Ledger: This component provides secure and decentralized record of the transactions that are validated
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Smart Contracts: This involves executing the contracts within the blockchain
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Immutable Records: This ensures that the recorded data within the transaction is not modified
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Transaction Validation: This involves validating all the transactions.
DETAILED DESCRIPTION OF THE INVENTION:
[009]The generative AI-driven credit card fraud detection system encompasses a number of modules designed in such a way that they coordinate with each other in order to detect the fraudulent transactions and safeguard the sensitive information of the cardholders:
[010]Generative AI Module
The heart of the system is the generative AI engine, which is trained over the past transaction data that can generate the artificial fraudulent patterns. These artificial patterns simulate a widespread collection of fraudulent scenarios, comprising of scenarios that have not been yet faced in real-world cases. By constantly generating new fraud scenarios, the system confirms that its detection model remains up-to-date and proficient enough to identify earlier undetected fraud techniques.
[011]Cybersecurity Layer
The system comprises an innovative cybersecurity module that offers real-time protection against cyber threats. It provides multi-factor authentication (MFA), encryption and detection of real-time threats. The potential attack vectors are simulated by Generative AI such as man-in-the-middle attack and other related ata breaches, permitting the system to strengthen its defensive measures.
[012]Adaptive Multi-Factor Authentication (MFA)
The MFA module corrects the security requirements dynamically based on real-time risk assessments. For example, transactions having less risk may need a simple password authentication, while transactions having higher risk might trigger biometric or multi-device verification. The generative AI engine plays a significant role in predicting the different ways the attackers can bypass the authentication and alters the security measures accordingly.
[013]Blockchain-Integrated Transaction Validation
The block chain ledger ensures that each transaction is securely logged on thereby achieving the transparency and immutability of transaction records. Generative AI plays a significant role in simulating the fraudulent blockchain interfaces thus permitting the system to detect and prevent potential fraudulent attempts on the blockchain itself.
[014]AI Incident Response System
This module activates an AI-driven incident response protocol soon after the detection of suspicious activity, stops the transaction and activates additional verification processes. The system reroutes the transaction if required, using an advanced secure network. Generative AI simulated attacks assist the system to predict the significant fraud routes, empowering pre-emptive rerouting to protect customer data.
[015]Continuous Learning
The machine learning models of the system constantly change by learning from the real-world fraud cases and AI-generated artificial data. This permits the system to adapt to evolving fraud trends in real-time without demanding for manual updates. The continuous learning ability guarantees that the system continues to be effective in identifying the known and unknown fraudulent schemes. , Claims:WE CLAIM
1. A system for the enhanced credit card fraud detection consisting of a generative AI engine that mimics artificial fraudulent transactions established on past fraud data. The innovation lies in the ability of AI to constantly generate artificial fraud patterns and provide them into the detection engine to increase accuracy, specifically over evolving fraud techniques.
2. The system of claim 1 wherein the AI models are endlessly updated with the help of real-time transaction data as well as the simulated fraud scenarios, independently retraining the detection algorithms. The unique feature of the system is its ability to adjust its fraud detection competencies independently, without the need for manual updates, thereby integrating artificial and real-world data.
3. The system of claim 1 wherein the module is designed for cybersecurity that offers AI-driven adaptive an Adaptive Multi-Factor Authentication (MFA) that vigorously regulates the authentication requirements based on the level of risk of the transaction. The innovation in this module is that the generative AI can be used in forecasting potential bypass methods for authentication, permitting the system to proactively tense the security as needed.
4. The system of claim 1 wherein a blockchain ledger for transaction validation is maintained that securely logs credit card transactions and avoids unauthorized alterations. The innovation here lies
in the combination of generative AI to simulate potential fraudulent blockchain interactions thus permitting the system to stop unauthorized ledger alterations while providing verifiable audit trails.
These claims afford a choice of safeties for the essential functionalities and components of the invention while emphasizing the precise features and their prospective paybacks.

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