Archives of Current Research International

Artificial intelligence (AI) is revolutionizing cybersecurity in digital currency transactions by enhancing fraud detection and risk mitigation. This study leverages machine learning techniques, including logistic regression and random forest classifiers, to evaluate AI’s efficacy in detecting fraudulent transactions using datasets such as the REKT Database, Elliptic Crypto Transaction Dataset, Cipher Trace AML Reports, and IEEE DataPort Financial Transactions Dataset. The analysis employs confusion matrix evaluation, fairness-aware machine learning techniques, and regression modeling to assess AI’s impact on fraud prevention. Findings reveal that AI-driven security measures reduced fraudulent activities by up to 76.86%, underscoring their effectiveness. However, the models exhibited a high false-negative rate of 89.54%, signaling a significant risk of undetected illicit transactions. Algorithmic bias is also evident, with a disparate impact ratio of 0.7793, indicating fairness concerns in AI fraud detection. These limitations highlight the need for adversarial training, fairness-aware optimization techniques, and anomaly detection refinements to improve model reliability. Additionally, regulatory frameworks, such as the EU AI Act, must be considered to ensure compliance, ethical fairness, and transparency in AI-powered cybersecurity applications. This study emphasizes the dual role of AI in strengthening fraud detection while presenting new challenges, such as algorithmic bias and adversarial vulnerabilities. To enhance AI’s effectiveness, quantum-proof encryption, fairness-aware fraud detection models, and transparent governance frameworks are recommended. These findings contribute to the ongoing discourse on AI-driven cybersecurity in digital finance, offering insights into mitigating risks while harnessing AI’s potential for securing digital currency ecosystems.