Deep Learning-Enhanced Blockchain Mechanism for Secure Banking Transaction Processing: An Adaptive Smart Contracts approach

The exponential growth of digital banking transactions has intensified the demand for robust consensus mechanisms that can ensure transaction integrity while maintaining scalability and security in distributed ledger systems. Traditional Byzantine Fault Tolerant (BFT) consensus algorithms in banking blockchain networks suffer from limited throughput, high computational overhead, and vulnerability to sophisticated adversarial attacks in high-frequency trading environments. This paper introduces a novel Deep Learning-Enhanced Blockchain Consensus Mechanism (DL-EBCM) that integrates adaptive smart contracts with a hybrid Byzantine fault tolerance approach specifically designed for secure banking transaction processing. The proposed methodology employs a dual-layer consensus architecture combining Delegated Proof of Stake (DPoS) with Deep Reinforcement Learning (DRL) optimization for validator selection and transaction validation. The system incorporates Convolutional Neural Networks (CNN) for transaction pattern recognition, Long Short-Term Memory (LSTM) networks for fraud detection, and Generative Adversarial Networks (GAN) for synthetic transaction generation during stress testing. Experimental validation using real-world banking transaction datasets from multiple financial institutions demonstrates superior performance with 99.8% transaction validation accuracy, 2.3 seconds average consensus time, and 15,000 transactions per second throughput while maintaining Byzantine fault tolerance up to 33% malicious nodes. The framework achieves 45% reduction in energy consumption compared to traditional Proof of Work systems and 67% improvement in consensus finality compared to existing BFT implementations. The proposed approach successfully addresses scalability limitations while ensuring regulatory compliance and maintaining cryptographic security standards required for critical banking infrastructure.