Blockchain-Based Fraud Detection and Prevention System Enhanced with AI

Abstract

Traditional centralized systems often fail to prevent fraud and ensure data integrity, especially as cyber threats grow more complex. This paper proposes a blockchain-based framework enhanced with artificial intelligence to address these limitations. Blockchain provides secure, tamper-proof storage and smart contract–based access control, while AI enables real-time anomaly detection by analyzing behavioral patterns. The system is built using Ethereum smart contracts and machine learning models, with a modular architecture connecting frontend, backend, and AI components. Evaluation shows over 92% accuracy in fraud detection, efficient response times, and reliable audit trails. The approach proves scalable and suitable for sensitive sectors such as healthcare and finance, offering a secure, intelligent, and decentralized solution for modern data protection.

Introduction

• Data is a vital asset in sectors like healthcare, finance, and education.

• Rising data breaches and cyberattacks expose weaknesses in traditional centralized security systems:

  • Single points of failure

  • Reactive threat detection

  • Inflexible access controls

• There is a need for a proactive, decentralized, intelligent security model.

2. Proposed Solution

“Securing Data with Blockchain and AI”:
A hybrid, modular system combining:

• Blockchain: Provides immutable, tamper-proof logs, transparent audit trails, and smart contracts for automated access control.

• Artificial Intelligence (AI): Enables real-time anomaly detection, behavioral analytics, and predictive threat mitigation.

3. Key Features

• Smart Contracts: Automate permissions, reduce manual errors, and enforce policy compliance.

• AI Engine:

  • Uses both supervised (e.g., Random Forest, Logistic Regression) and unsupervised (e.g., Autoencoders, Isolation Forest) models.

  • Trained on simulated datasets with user behaviors and anomalies.

• Scalability: Microservices-based architecture allows independent upgrades.

• Separation of Concerns: Sensitive data stored off-chain; only hashes and metadata are on-chain for security and compliance.

4. Literature Support

• Extensive literature review shows increasing convergence of blockchain and AI in fraud detection and security.

• Techniques like ensemble learning, transformers, graph analytics, and self-supervised learning have been integrated or inspired the current system.

• Studies confirm the need for real-time, explainable, and scalable security infrastructures.

5. Methodology

• Data Collection: Public and synthetic datasets with user login/access patterns.

• Model Training:

  • Preprocessing (normalization, encoding)

  • Supervised learning for known threats

  • Unsupervised learning for novel anomalies

• Tech Stack:

  • Frontend: React.js (dashboard, alerts)

  • Backend: Node.js/Express.js (coordinates AI & blockchain)

  • Blockchain: Private Ethereum (via Ganache), Solidity smart contracts

  • AI Frameworks: TensorFlow, Scikit-learn

6. Evaluation & Results

???? AI Performance:

• Top Models: Autoencoder & Random Forest

• Metrics Achieved:

  • F1-score: > 0.90 (high reliability)

  • Real-time alert accuracy: > 92%

  • Low false positives and minimal latency

?? Blockchain Performance:

• Verified immutability of logs via hash mismatch checks.

• Fast block confirmation and low latency even under high load.

• All transactions auditable and verifiable by multiple nodes.

???? Scalability Testing:

• System remained stable under concurrent access.

• Microservices allowed smooth updates and component scaling.

7. Impact & Applicability

The framework is ideal for data-sensitive sectors:

• NGOs, Finance, Healthcare, Legal—where data privacy, integrity, and auditability are critical.

• Enhances trust, reduces human oversight, and meets regulatory compliance.

• Offers modular, real-time, and explainable fraud detection.

Conclusion

The increasing sophistication of cyber threats and the limitations of centralized security systems necessitate the development of intelligent, transparent, and resilient data protection frameworks. This paper introduced a novel system that integrates blockchain and artificial intelligence to proactively detect and prevent fraudulent activities in sensitive data environments. The proposed framework addresses the core challenges outlined in the abstract namely, the lack of transparency, the vulnerability to tampering, and the inefficiency of traditional fraud detection methods by combining the immutable nature of blockchain with the predictive capabilities of AI. The architecture was systematically designed to support real-time behavior analysis, decentralized audit logging, and automated access control. AI models were trained on synthetic and real-world datasets to detect anomaliesv and malicious patterns, while smart contracts recorded transactions immutably on a blockchain ledger. Evaluation metrics including precision, recall, F1-score, and block validation time demonstrated the system\'s effectiveness, efficiency, and scalability. The results confirmed that the integration of AI and blockchain not only enhances fraud detection accuracy but also ensures data traceability and auditability without reliance on third-party intermediaries. This solution is particularly applicable to sectors such as finance, healthcare, education, and non-governmental organizations, where data integrity and privacy are mission-critical. By offering a proactive, decentralized, and intelligent security approach, the framework directly contributes to strengthening digital trust and user autonomy in high-risk environments. For future work, the system can be extended by integrating decentralized storage solutions like IPFS for full off-chain file management, and real-time blockchain platforms such as Ethereum Mainnet or Hyperledger Fabric for production-level deployment. Furthermore, the AI engine can be enhanced with federated learning models to improve privacy during model training. Introducing support for decentralized identifiers (DIDs) and role-based cryptographic access could further elevate user control and identity security.

References

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Copyright

Copyright © 2025 Dr. Girish Kumar, Miss. Yamuna B. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.