Journal of African Development

Dynamic financial systems face a highly challenging dilemma, as the complexity of financial fraud stems from the rapidly evolving nature of adversarial strategies and the swift operations in finance facilitated by the digital medium. Traditional fraud detection systems, including those with AI capabilities on top of blockchain, are mostly reactive, where the anomaly is detected post-execution of transactions, frequently resulting in slow actions, high false positives, and irrevocable loss. The constraints are even augmented in the high-volume ecologies of the globe, whereby the trends in transactions keep on changing, and therefore fixed thresholds and rule-based solutions do not work. To address these issues, the paper proposes a novel cognitive blockchain model capable of predicting fraud before transaction settlement through adaptive neuro-symbolic reasoning, behavioural fingerprinting, and a distributed, memory-based ledger. The architecture is built using four layers, a perception layer that provides real-time transaction sensing and behavioral profiling, a cognitive layer using temporal knowledge graphs and Markov decision process-based anticipatory inferences, a blockchain layer that is embedded with Cognitive-Oriented Smart Contracts (COSC) that dynamically tune validation criteria and an adaptive governance layer that continuously optimizes its fraud detection rules based on multi-modal data fusion. The framework functions on a mixed-up middle opinion technique, which guarantees its scope as well as safety without demanding a trade-off in transaction throughput. To analyse its performance, a synthetic and semi-synthetic dataset, in the form of transaction data of a realistic fraud profile, was created to create a simulated high-volume financial environment. According to the experimental results, the accuracy of anticipating fraud was found to be 87 per cent, the degrees of false positives shrank by 35 per cent, and adding latency to blockchain was less than 5 per cent, as opposed to traditional blockchain approaches of fraud detection. The significance of these results is the evidence that such a proposed framework could be used to stop all fraudulent activities when the overhead of the operation is minimal. At the same time, the integrity of transactions could be assured in volatile and adversarial conditions. The proposed paradigm of cognitive blockchain introduced in this paper sets a new marker of predictive security financial with scalability, robustness and regulatory compliance, or in other words, a solution to fraud mitigation in next-generation financial systems