Orchestration of autonomous trusted third-party banking

Area of Science:

• Computer Science
• Information Systems
• Financial Technology

Background:

• Digital transformation is reshaping banking, emphasizing value creation through autonomous solutions.
• Autonomous banking requires on-the-fly processing for risky transactions and robust access control.
• Role and trust delegation are critical in autonomous financial environments.

Purpose of the Study:

• To develop an end-to-end solution for autonomous transaction and task processing in banking.
• To enable secure and efficient handling of risky transactions within autonomous banking systems.

Main Methods:

• Utilized risk graphs, risk bands, and finite state machines to illustrate task delegation.
• Developed a step-by-step task delegation process using a risk ordering relation methodology.
• Embedded the methodology into smart contracts for autonomous execution.

Main Results:

• Demonstrated task delegation with risk ordering relation among six process owners on immutable ledgers.
• Employed Multi-Agent Systems (MAS) to overcome barriers in autonomous transaction processing.
• Introduced a novel approach combining risk graphs and risk ordering relations for task delegation, surpassing traditional Role-Based Access Control (RBAC).

Conclusions:

• The study presents a novel logic for task delegation and policies for autonomous execution.
• The proposed solution is suitable for autonomous banking platforms, drawing parallels with federated identity concepts.
• This approach enhances security and efficiency in digital banking environments.