Miriam W. Kaara, Jael S. Wekesa, Michael W. Kimwele
The integrity and credibility of educational institutions worldwide are being undermined by the growing issue of academic certificate fraud. The public's trust in the educational system, the integrity of job opportunities, and the legitimacy of authentic credentials are all negatively impacted by academic dishonesty. The majority of academic credentialing systems, both centralized and blockchain-based, focus on academic credential validation and immutability, even though academic institutions have embraced technology to improve academic credentialing procedures. This suggests that their support for intelligent and private fraud detection is minimal. In this work, we suggest an integrated method that combines smart anomaly detection with Federated Learning (FL). This makes it possible for several organizations to build a model for detecting certificate fraud without disclosing private information to other organizations. Each institution's private information is retained and incorporated into a global model. This enables the framework to handle concerns about data ownership and privacy as well as regulatory compliance. The framework uses Extreme Gradient Boosting (XGBoost) to identify anomalies in metadata and Convolutional Neural Networks (CNNs) to detect visual forgeries in certificates. A decentralized node is used to train the models, which are merged using the Federated Averaging (FedAvg) algorithm. The federated model performs well in anomaly detection, according to the experiments conducted. When compared to conventional centralized approaches, it significantly reduces false positives and false negatives, with an accuracy of up to 94% and an AUC of 0.97. The model's findings imply that a federated learning approach would make it possible for institutions to detect fraud in a secure, scalable, and cooperative manner. In this regard, this framework offers a workable way to create digital credential systems that are more trustworthy and protect privacy. Key words: Federated Learning, Blockchain, certificate fraud detection, privacy preservation, distributed datasets, secure collaboration.
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.
Pipelines that pair a large language model with a static analyzer, feeding findings back as repair instructions, appear throughout recent smart contract repair research. They rest on a rarely examined assumption: that the analyzer output serving as the oracle faithfully records what the analyzer found. I report three ways that assumption fails, identified during a four-contract instrument-validation exercise preceding a planned repair study. First, Mythril v0.24.8 can exit without reaching the analysis phase while returning exit status zero, empty standard error, and a findings array byte-identical to that of a genuinely clean scan; the failure is reported in a sibling JSON field that finding-extraction code has no reason to read. Second, 12 of 23 Slither findings in my validation set fell outside the high, medium, and low impact bands, so an unfiltered count measures a composite whose components may not behave alike under repair. Third, keying finding identity on source location breaks across repair rounds. On the one contract carried through three rounds, location-based keying inflated resolved findings from 7 to 12 and introduced findings from 2 to 7. The underlying instability is established in the warning-tracking literature; my contribution is its consequence for repair metrics, where it biases both transition counts upward and can confound comparison between methods producing differently sized .patches. I separately report an executed exploit showing a specification-level authorization defect that produced no high or medium impact finding. I propose calibration procedures for each hazard and release the harness, contracts, and raw analyzer output at doi:10.5281/zenodo.21586404.