Boosting Fund Protection through Implementation of Algorithmic Intelligence to Identify Deceptive Actions in Digital Transaction Ecosystems - Dr. Olli Virtanen (Emerging Frontiers Library for The American Journal of Interdisciplinary Innovations and Research, 2026)
The rapid expansion of digital transaction ecosystems has fundamentally transformed global financial systems, enabling high-speed, cross-border monetary exchanges. However, this transformation has also introduced complex vulnerabilities in the form of deceptive financial behaviors, algorithmically generated fraud, and privacy-invasive transactional manipulation. This research proposes a comprehensive algorithmic intelligence framework designed to enhance fund protection by detecting deceptive actions in digital transaction environments. The study integrates advanced privacy-preserving machine learning techniques with federated and distributed learning paradigms to construct a robust fraud detection architecture. Foundational contributions are drawn from privacy-preserving decision systems and federated learning models, including Akavia et al. (2019), Aminifar et al. (2021), and Li et al. (2020), which demonstrate the effectiveness of decentralized and secure predictive modeling in adversarial environments. A core analytical foundation is also established through differential privacy principles (Dwork & Lei, 2009), homomorphic encryption frameworks (Benarroch et al., 2017), and secure collaborative prediction systems (Giacomelli et al., 2019), ensuring that sensitive financial data remains protected during model training and inference processes. The proposed architecture incorporates hybrid gradient boosting frameworks and federated decision tree systems (Fang et al., 2020; Li et al., 2020), enabling distributed anomaly detection across digital transaction nodes. Additionally, interpretability mechanisms derived from GBDT model analysis (Fang et al., 2018) enhance transparency in fraud classification decisions. The findings indicate that algorithmic intelligence significantly improves detection accuracy for deceptive transaction patterns while maintaining data privacy and computational efficiency. The system effectively identifies coordinated fraud attempts, minimizes false negatives, and strengthens overall fund protection mechanisms in digital ecosystems. However, challenges remain in computational scalability, adversarial model manipulation, and real-time deployment across heterogeneous financial infrastructures. The study concludes that privacy-preserving algorithmic intelligence represents a critical advancement in securing digital financial systems and ensuring sustainable fund protection in increasingly complex transactional environments.