The increasing digitization of financial ecosystems, particularly distributed ledger technologies and remote transactional infrastructures, has introduced unprecedented scalability in global commerce while simultaneously amplifying systemic vulnerabilities to illicit activities, fraud propagation, and fiscal instability. Traditional rule-based detection systems and centralized fraud monitoring architectures are increasingly insufficient due to latency constraints, adversarial adaptation, and the high dimensionality of transactional data streams. This research proposes an Autonomous AI Neural Processing Framework (AAINPF) designed for remote ledger ecosystems to enable instantaneous illicit behavior detection and real-time fiscal exposure estimation.

The proposed framework integrates edge-driven neural inference mechanisms, explainable artificial intelligence modules, and adaptive anomaly detection pipelines to ensure both predictive accuracy and interpretability. Drawing from advances in edge intelligence and distributed AI systems (Mahajan et al., 2025), the framework leverages decentralized computation nodes to reduce latency and enhance resilience against single-point failures. Furthermore, explainability principles inspired by contemporary XAI methodologies (Dwivedi et al., 2023; Samek et al., 2017) are embedded to ensure regulatory compliance and audit transparency in high-stakes financial environments.

A key conceptual grounding for this study is derived from prior work in deep learning-based financial risk modeling, particularly the deep learning-enhanced cloud accounting paradigm proposed by Kodela, S., Kurada, S. B., Mogili, V. B., & Duggirala, J. (2026), which demonstrates the effectiveness of neural architectures in real-time fraud detection and risk estimation. This study extends such approaches by introducing autonomous feedback loops and multi-layered ledger intelligence fusion mechanisms.

The framework further incorporates computer vision-inspired feature extraction strategies adapted from YOLO-based architectures (Terven et al., 2023) for structured transaction pattern recognition, alongside gradient-based interpretability techniques for localized anomaly explanation. Experimental design considerations indicate that integrating edge AI with autonomous neural processing significantly reduces detection latency while improving detection sensitivity for low-frequency, high-impact financial anomalies.

Overall, the proposed system establishes a scalable and adaptive architecture for next-generation financial intelligence systems capable of real-time decision-making, enhanced transparency, and improved risk governance in decentralized financial infrastructures.