Industrial financing operations have become increasingly complex due to expanding supply chains, heterogeneous financial requirements, delayed transaction settlements, and the growing demand for intelligent decision-making mechanisms. Traditional financing approaches often depend on manual assessment, fixed approval procedures, and historical decision patterns, which limit transaction completion efficiency and adaptability in dynamic industrial environments. This research proposes an autonomous optimization method for strengthening transaction completion efficiency in industrial financing operations by integrating optimization theory, intelligent decision mechanisms, and adaptive learning principles. The study develops a conceptual framework that combines financial process modeling, autonomous resource allocation, transaction risk evaluation, and optimization-based scheduling to improve financing execution performance.

The theoretical foundation of the proposed method is derived from convex optimization principles, autonomous planning techniques, and financial innovation frameworks. Convex optimization provides a mathematical foundation for solving constrained financial decision problems by identifying optimal solutions under multiple operational requirements (Boyd and Vandenberghe, 2004). In parallel, intelligent optimization approaches inspired by autonomous planning research demonstrate the capability of adaptive systems to improve decision efficiency in uncertain environments (Karaman and Frazzoli, 2011). The proposed framework applies these concepts to industrial financing scenarios where transaction completion depends on coordinated interactions among financial institutions, enterprises, suppliers, and technological platforms.

The research further incorporates recent developments in intelligent financial optimization. SinghJatav et al. (2025) demonstrated that hybrid reinforcement learning and deep learning approaches can optimize payment delays in supply chain finance by dynamically adjusting decisions based on transactional patterns. Extending this perspective, the proposed autonomous optimization method focuses not only on payment delay reduction but also on improving the overall transaction completion lifecycle, including financing approval, capital allocation, settlement coordination, and operational monitoring.

This research contributes to the intersection of financial technology, optimization theory, and industrial finance management by presenting a structured approach for intelligent financing operations. The proposed method provides theoretical insights and practical guidance for developing next-generation financial systems capable of achieving higher transaction completion efficiency while maintaining operational reliability and risk control.