Financial institutions turn to blockchain and federated AI for secure fraud detection

The review reports that blockchain-enhanced federated learning systems typically achieve slightly lower raw accuracy than centralized models, often ranging between 85 and 92 percent. This gap is attributed to data heterogeneity across participants, communication overhead, and the complexity of decentralized aggregation. However, the authors emphasize that these systems deliver superior outcomes in contexts where privacy, collaboration, and compliance outweigh marginal accuracy gains.

Financial fraud is escalating in scale, speed, and sophistication as digital transactions become the backbone of global commerce. Banks, insurers, healthcare providers, and public agencies are facing mounting pressure to detect fraud in real time while complying with increasingly strict data protection and transparency regulations. Traditional centralized fraud detection systems, once effective in siloed environments, are now straining under regulatory constraints, cross-border data restrictions, and the growing need for inter-institutional collaboration.

A new academic review argues that the future of fraud detection will be shaped not by a single algorithmic breakthrough, but by how artificial intelligence, federated learning, and blockchain technologies are combined. The study Blockchain-Based Fraud Detection: A Comparative Systematic Literature Review of Federated Learning and Machine Learning Approaches, published in the journal Electronics, provides a clear-eyed assessment of what works, what does not, and why hybrid, privacy-preserving systems are rapidly gaining strategic importance.

Centralized machine learning still dominates accuracy, but at a growing cost

The review confirms that centralized machine learning models remain the strongest performers when raw predictive accuracy is the primary objective. Algorithms such as Random Forest, XGBoost, Support Vector Machines, and deep neural networks consistently achieve accuracy levels above 93 percent when trained on large, labeled datasets. These models excel in high-throughput environments such as credit card transaction monitoring, where speed and precision are critical and data can be processed within a single institutional boundary.

In these settings, centralized architectures offer clear advantages. Data aggregation enables richer feature engineering, faster convergence during training, and lower inference latency during deployment. As a result, many banks and financial service providers continue to rely on centralized machine learning for real-time fraud detection, especially where regulatory frameworks permit internal data pooling.

However, the study notes that this dominance comes with mounting trade-offs. Centralized systems create single points of failure that are attractive targets for cyberattacks. They also raise serious concerns under data protection regimes such as the General Data Protection Regulation, which restricts how personal and transactional data can be shared, stored, and reused. As financial ecosystems become more interconnected, the inability to safely share data across institutions limits the scope of fraud detection, allowing sophisticated, cross-platform fraud schemes to evade detection.

The review also highlights a persistent evaluation problem. Many studies report high accuracy without adequately addressing the extreme class imbalance inherent in fraud datasets, where fraudulent transactions often represent less than one percent of total activity. In real-world deployment, accuracy alone can mask poor recall or high false-positive rates, leading to customer friction, operational costs, and regulatory scrutiny. The authors stress that precision, recall, F1-score, and cost-sensitive metrics are far more meaningful indicators of system performance.

Federated learning and blockchain answer privacy and trust gaps

Rather than pooling raw data, federated learning allows multiple institutions to collaboratively train shared models by exchanging encrypted model updates. Each participant retains control over its own data, addressing privacy concerns while enabling broader pattern recognition across organizations.

The study finds that federated learning systems consistently reduce false positives and improve fraud coverage in multi-institutional environments. When banks or platforms collaborate through federated frameworks, they gain visibility into fraud patterns that would otherwise remain fragmented. This is particularly valuable in combating organized fraud rings, synthetic identity fraud, and cross-border payment fraud.

Blockchain technology plays a critical enabling role in these systems. By recording model updates, aggregation steps, and participation events on an immutable ledger, blockchain introduces transparency and auditability into federated learning workflows. Smart contracts can automate model validation, enforce contribution rules, and ensure that updates meet predefined quality thresholds. This reduces the need for centralized trust brokers and creates a verifiable record of model evolution, a key requirement for regulatory compliance.

The review reports that blockchain-enhanced federated learning systems typically achieve slightly lower raw accuracy than centralized models, often ranging between 85 and 92 percent. This gap is attributed to data heterogeneity across participants, communication overhead, and the complexity of decentralized aggregation. However, the authors emphasize that these systems deliver superior outcomes in contexts where privacy, collaboration, and compliance outweigh marginal accuracy gains.

Latency and scalability remain challenges. Blockchain consensus mechanisms can introduce delays, particularly in public or permissionless networks. Federated learning also requires careful coordination to handle uneven data quality and computational capacity across participants. The study notes that many recent implementations are addressing these issues through permissioned blockchains, adaptive aggregation strategies, and lightweight consensus protocols designed for enterprise use.

Hybrid architectures emerge as the most viable path forward

The study identifies hybrid architectures as the most promising direction for real-world fraud detection. These systems combine the speed and accuracy of centralized models with the privacy and transparency benefits of federated and blockchain-based approaches.

In hybrid setups, centralized machine learning models handle high-frequency, low-latency fraud detection within individual organizations. Federated learning operates in parallel, aggregating insights across institutions to update shared representations of fraud patterns. Blockchain infrastructure ensures that collaboration remains auditable, tamper-resistant, and compliant with regulatory expectations.

The review finds that well-designed hybrid systems often achieve accuracy levels between 90 and 93 percent while significantly reducing false positives and enabling cross-domain fraud detection. This balance makes them particularly attractive in sectors such as banking consortia, insurance networks, healthcare billing systems, and digital payment platforms, where trust and data sovereignty are paramount.

Beyond architecture, the study offers a detailed taxonomy of fraud types and detection techniques. Credit card fraud remains the most extensively studied domain, followed by financial statement fraud and insurance fraud. However, research attention is rapidly expanding to cryptocurrency fraud, online payment fraud, identity theft, healthcare fraud, and emerging threats such as attacks targeting AI models themselves. The authors note that adversarial manipulation of machine learning systems is becoming a new frontier of financial crime, underscoring the need for robust, adaptive detection frameworks.

The review also highlights critical research gaps. Explainability remains underdeveloped in both centralized and federated systems, complicating regulatory oversight and customer communication. Lightweight blockchain protocols suitable for real-time fraud detection are still evolving. Adaptive federated learning techniques capable of handling highly heterogeneous data distributions are needed to improve performance without sacrificing privacy.

Implications for regulators, institutions, and the future of fraud defense

The study makes clear that no single fraud detection approach can satisfy all operational and regulatory requirements. Instead, institutions must align technology choices with their risk profiles, collaboration needs, and compliance obligations.

For regulators, the review suggests that blockchain-enabled federated learning offers a viable framework for supervised collaboration without compromising data protection. Immutable audit trails and automated compliance mechanisms can strengthen oversight while reducing reporting burdens. However, regulatory clarity around decentralized AI systems remains essential to encourage adoption.

For financial institutions, the message is pragmatic. Centralized machine learning remains indispensable for real-time detection, but its limitations are becoming more pronounced. Federated and hybrid systems provide a pathway to scale fraud detection beyond institutional silos, but they require investment in infrastructure, governance, and interoperability.

The future of fraud detection will be defined by integration rather than replacement. As fraudsters exploit the seams between platforms and jurisdictions, defensive systems must become equally interconnected. Blockchain, federated learning, and machine learning each address different dimensions of this challenge. Together, they form a layered defense capable of meeting the demands of an increasingly regulated, data-driven financial world.