Knowledge-based exception handling in securities transactions

Abstract

With rising trading volumes and increasing risks in securities transactions, the securities industry is making an effort to achieve straight through processing to shorten the trade lifecycle and minimize transaction risk. While attempting to shorten the settlement cycle, the trade information must be passed within the trade lifecycle in a timely and accurate fashion. Exception handling is critical to make sure trades that give rise to exceptions or trades containing errors need to be detected and reconciled in compressed timescales. In order for a knowledge level solution for exception handling, the technology of intelligent agents is applied in this research. Intelligent agents with their knowledge base and properties of autonomy, activity and pro-activity are well suited for business exception handling. Based on analysis on exceptions occurred in securities transactions and process of exception reconciliation, several types of intelligent agents are proposed and a multi-agent framework is presented for exception handling in securities trading. Furthermore, business knowledge such as business rules and strategies are extracted from securities trading and settlement practice, and applied to the design of individual agents to make them act autonomously and collaboratively to fulfil the goal of exception reconciliation. By separating business logic from business model, such business rules approach can enhance the flexibility and adaptability of our agent-based exception handling system.