Transformation of institutional client onboarding processes through agent-based intelligent AI systems in high-risk financial infrastructure

The study is relevant because the processes for connecting institutional clients in high-risk financial systems are becoming increasingly complex due to increasing regulatory requirements, operational risks, and the architectural complexity of the financial infrastructure. Under these conditions, traditional, centralized, linear approaches to organizing integration processes do not provide the necessary levels of manageability, transparency, and sustainability.

Drawing on over 8 years of leadership at Nasdaq directing client integrations and AI enhancements for the cloud-native Nasdaq Risk Platform (NRP) in high-frequency trading (HFT) environments, this article substantiates approaches to transforming institutional client onboarding by using agent-based intelligent AI systems to structurally reduce operational risk and ensure managed, architecturally balanced integration.

The purpose of the article is to scientifically substantiate approaches to transforming the processes of connecting institutional clients in high-risk financial systems by using agent-based intelligent AI systems to reduce operational risk and ensure a managed, architecturally balanced integration of clients into the financial infrastructure.

The study uses systemic and structural-functional approaches to analyze the connection of institutional clients in the financial infrastructure. The methods of logical-analytical generalization and comparative analysis are used to assess the impact of agent-oriented solutions on operational risk and architectural manageability of financial systems; theoretical generalization is used to form scientifically based recommendations for their implementation.

The organizational and process structure of connecting institutional clients has been studied, and its determining influence on the stability of financial operations has been established. The functional role of the agent approach in managing integration interactions, data processing and coordination of procedures has been determined. It has been proven that agent-oriented solutions ensure the localization of operational risks and increase the architectural manageability of financial systems. Key transformation problems related to regulatory compliance, limited transparency of processes, accumulation of hidden architectural complexity and the risk of cascading failures have been identified.

The feasibility of implementing agent-based integration solutions as a tool for structurally reducing operational risks without unduly complicating the architecture of financial systems has been substantiated. It is noted that the effectiveness of such solutions is determined by interpretability, the formalized distribution of responsibility, and the event-oriented organization of integration interactions. Prospects for further research include developing methods for the quantitative assessment of operational risk in agent-oriented financial systems, improving approaches to measuring architectural controllability, and researching hybrid agent-based AI solutions for critical financial infrastructure.