Enhancing Blockchain Financial Security through DB-BOA Optimization and ADTCN Frameworks

Abstract

Blockchain technology, combined with smart contracts, serves as a revolutionary force in financial operations by enabling automated, transparent, and tamper-proof transaction execution. The conventional stock market infrastructure faces operational shortcomings due to multiple intermediaries, slow settlement times, and security weaknesses. The integration of deep learning models with blockchain-based smart contracts demonstrates emerging potential to enhance security while delivering precise and efficient financial ecosystem operations. The review investigates how artificial intelligence collaborates with distributed ledger technologies through adaptive deep temporal models as well as consensus optimization and secure contract execution on private Ethereum consortium blockchains. This study examines two advanced financial approaches by evaluating their performance as the Adaptive Deep Temporal Context Network (ADTCN) and the Dynamic Butterfly-Billiards Optimization Algorithm (DB-BOA). Important obstacles related to system expansion, together with system compatibility and model visibility, and on-time deployment, are examined. Future directions for creating intelligent, secure, efficient blockchain-based financial systems are established through the identification of present research gaps.