Resolution of breaks using AI reconciliation agents

AI reconciliation agents bring remarkable efficiencies to the process. They streamline the resolution of breaks by bridging the gap between structured platform data and unstructured communication. While initial email drafts for trade breaks are often created using deterministic, templatized rules, large language models (LLMs) can solve the unstructured-to-structured data problem when a counterparty responds. The AI reconciliation agent then interprets the response to determine the next step, which could include amending a trade. If a fund agrees to match a counterparty’s data, an AI agent can automatically amend the trade via API or follow an agentic flow to handle indeterministic parts of the update.

Another way AI agents provide transparency to the human users revolves around P&L, with their capability to divide up P&Ls according to business units when multiple fund managers are taking positions within that fund. If there is one security where they see unusual P&L movement — either it is too high or too low against their expectation — they can use an AI agent or LLM to ask natural language questions about it and try to drill through in terms of: Are the trades booked with the right price? Was there any corporate action which got processed? Is the day-end pricing more appropriate?

The prevention of failures in reconciliation translates to prevention of numerous problems, from corporate action mismatches and missed hedges to exceeding concentration limits, missed margin calls, and many more.

Checking the fund admin

Funds can gain a measure of direct control of their fate by running parallel investment and accounting books of record — shadow accounting — to generate daily NAVs and rapidly reconcile accounts for a pool’s value, which changes on a daily basis. It provides next-level risk management and boosts transparency, visibility, and flexibility concerning positions, exposures, and key financial metrics, such as the income statement and balance sheet. Automated AI reconciliation of internal data gives firms the ability to execute advanced treasury operations like margin replication and simulation, reducing their reliance on counterparties’ margin calculations and using treasury to drive revenue.

AI reconciliation as the warning system of a firm’s ops

When it comes to deploying the army of AI agents, reconciliation is too powerful to ignore. AI is in its element when it comes to helping us deal with unimaginable volumes and complexity of financial data. With data flowing from multiple custodians and brokers, inconsistencies are inevitable. Reconciliation agents streamline investment ops to prevent trade failures and ensure regulatory compliance. This will become even more crucial as digital assets and tokenization become more mainstream.

Inadequate reconciliation brings a litany of nightmares to multiple departments. Superlative AI reconciliation in investment operations brings sweet dreams to funds looking to save time in query response and reporting, makes the best decisions to drive alpha, activates idle cash, boosts trust with auditability and compliance, and achieves internal workflow synchronization. Firms with the most authoritative view of positions, cash, and transactions will carve out a strong advantage in a competitive and volatile market.