Advanced predictive autonomous agents for multi-portfolio risk analytics and real-time enterprise P&L decisioning: Self-learning AI systems for multi-counterparty derivatives, collateral valuation, and accounting reconciliation

Abstract

Predictive autonomous agents enable real-time profit-and-loss decision making for multi-portfolio strategies with derivative products and collateral dependencies. These self- learning systems automatically process data from a multitude of sources, forecast future developments, update models, and assess brokerage, custodian, and counterparty risk. Related technologies guarantee continuous learning, decisioning, and execution. A formal agent-based approach has been defined and implemented, allowing fast simulations of inter-dealer negotiations, default risks, brokering of collateral, and their possible market impact. P&L decisioning in real time with predictive techniques respon- sive to evolving market conditions is paramount. Requests can be formulated for agent-based modeling systems that meet latency and risk control requirements. Such compliant systems provide required inputs for accredited predictive modeling techniques in the P&L space. For market risk reflecting the uncertainty of scenarios and the inability to cover all significant extreme move- ments, time-series forecasting techniques on factors driving risk sensitivities represent a standard procedure. Contingency analysis on sufficient ranges of market risk generating environments is a desirable regulatory recommendation for the supervisory monitoring of systemic institutions and a requisite process for the local and consolidated compliance frameworks of significant investment firms within the European Union.