Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms

Abstract

Recent advances in formal models of multi-agent systems provide an approach to reconciliations across a wide variety of enterprise reconciliation demands within shared, counterparty-facing environments. Independence and autonomy is achieved by distributed decision making, coupled with resolution of recommendations to a central regulatory or corporate entity providing a rights- and restructuring framework to ground properties of covers, identities, insurance, indemnities and obligations. Derivative trade and cash-flow reconciliation is integrated with IKEA-like tools for authorities managing the financially supervised and politically segregated demand for margin and collateral accounts, with appropriate symmetries maintained across all components of the flow. Accounting reconciliation, embedded with such automation tools, agents and servers, broadens the scope, cohesion and substantive pay-as-you-go cost benefits. Business and financial relationships are founded on trust, largely but not solely financial, and on the management of crime and systemic risk. Establishing trust relies on verification of business partners. In the financing and financing-related industries, risk associated with neither aspect can be left unattended. Failure to do so can impose unnecessary and escalating costs of resolution. Failure by institutions reliant on supervision implies regulatory and public endorsement of potential violations. Private-sector institutions, including banks, impose significant financial costs in their risk provisioning, and these are excluded from Virtual Associates analysis. Recent technical cooperation in preventing or reconstructing ransomware attack consequences easily fits within the established framework of the Associates concept.