Most reconciliation solutions provide the data normalization and matching capabilities needed to identify and categorize exceptions. These features have helped buy-side firms to streamline post processing and improve the quality of financial records. However, the reconciliation process can become cumbersome when an exception is identified, and the investigation process begins. It is at this moment when different data sources (such as corporate actions, failed trades, collateral, missing funds, etc.) need to be accessed to troubleshoot a break and determine the root cause of an exception. The time and effort taken to open different applications and web pages, rummage through the information, and then research and manually compare results diminishes the productivity of the operations team or IT staff.
The issue of inefficiency isn’t just constrained to investigating data. Often, multiple staff members are investigating the same issue without any knowledge of the redundant activity.
Although Artificial Intelligence (AI) and Machine Learning may have helped to expedite the mapping of data and the establishment of matching rules for reconciliation, it has not produced significant long-term gains in worker productivity, especially since the majority of time spent on reconciliation involves researching breaks, rather than merely finding them. Nor has it alleviated the pressure of margin compression or the need for financial institutions to find better ways of optimizing their resources.
A more effective method is to improve the research functionality within the reconciliation engine to reduce the effort spent on investigating why breaks occur.
Integrating common forms of research data with sophisticated security cross-referencing logic results in a more efficient investigation process. Portfolio accountants and other back-office staff can use the enhanced research functionality and data to troubleshoot breaks related to failed trades, collateral, corporate actions, securities lending, and many others.
Reconciliation solutions need to actively participate alongside the end-users to quickly identify the reason for an exception, rather than placing the burden on end-users to uncover the root cause. This approach to exception management leads to improved scalability and greater visibility across the back-office where the tentacles of an exception are far-reaching. The impact of a failed trade can wreak havoc across the reconciliation, settlement and trading teams. Using a dynamic, synergistic view of exceptions and related investigation research will enable buy-side firms to improve their overall operational efficiency and gain much-needed transparency throughout the entire trade life cycle.
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Julian Trostinsky - Global Director - Solutions Engineering
Julian Trostinsky is a Global Director of Solutions Eng..
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