AI-Driven Neural Control Architectures for Autonomous Trust Management in Financial Data Platforms

Abstract

The paper has compared a traditional rule-based control system and an AI-based Neural Trust Orchestration Layer (NTOL) financial data platform control system. The results show that NTOL achieves high alterations in the accuracy, detection rates and response times. It reduces silent data failures by more than 65 per cent, detects more accurately the accuracy of the trust score and finds compliance drift. NTOL can also respond to system failure far quicker and can handle large workloads. The findings represent that the neural systems can learn patterns, recognize early threats and take better measures. NTOL is the company that provides superior and safer platform the management of trust in the modern financial environment.