Abstract Smart contract vulnerabilities have caused billions of dollars in losses across decentralized finance. Finding reliable ways to detect such vulnerabilities has been a long-standing challenge for researchers. The growing capabilities of large language models (LLMs) are promising, but the factors that determine their reliability and capabilities remain poorly understood. This study investigates whether increasing inference-time computation using techniques like extended reasoning and structured prompting always improves vulnerability detection capability. It also identifies the most influential factors to select a model for this task. Using four prompting techniques, it evaluates 14 LLMs from seven families on 54 Solidity contracts. The experiment reveals a clear gap in detection capability across model classes. While six frontier models do not report false positives on verified-clean contracts, all three small open-source models report vulnerabilities in every case throughout the experiment. Moreover, a 11.5% drop in F1 score for one model was observed when increasing inference-time compute by enabling extended thinking. Also, prompting strategy has a limited effect on detection capability compared to model selection. The results challenge common assumptions and offer practical insights into the use of LLMs for smart contract vulnerability detection.
The rapid digitization of financial services has created increasingly complex environments in which financial institutions must process large volumes of heterogeneous transaction data while simultaneously protecting customers, detecting fraud, managing financial risks, and complying with regulatory requirements. Traditional centralized and rule-based fraud detection systems face significant challenges associated with data volume, processing latency, evolving fraudulent behaviors, class imbalance, and the increasing sophistication of cyber-enabled financial crimes. This paper proposes a distributed intelligent analytics framework for blockchain-based fraud detection and risk management in financial institutions. The framework integrates distributed big data analytics, artificial intelligence, machine learning, blockchain, graph-based learning, and intelligent decision support into a unified architecture. Distributed computing provides scalable processing of heterogeneous financial datasets, while artificial intelligence identifies anomalous transactions and predicts potential risks. Blockchain provides a complementary integrity, traceability, and verification layer for financial transactions. Graph Neural Networks can further model relationships among customers, accounts, devices, merchants, and transactions, enabling the detection of complex fraud patterns that may not be visible through transaction-level analysis. The framework builds on Ramareddy's work on distributed big data analytics for scalable knowledge discovery in heterogeneous systems and Chhunchha's investigation of blockchain's influence on financial institutions. Recent research also indicates growing interest in machine learning, graph-based models, federated learning, and blockchain for financial fraud detection. The proposed framework addresses important challenges including scalability, privacy, class imbalance, concept drift, explainability, cybersecurity, and regulatory compliance. The paper argues that combining distributed analytics with blockchain and AI can provide financial institutions with a more scalable, transparent, adaptive, and intelligent approach to fraud prevention and financial risk management.