The Manual Investigation Problem
T+1 mandates don't leave room for slow workflows. Yet most mid-to-large asset managers are still running settlement fail investigations the same way they did under T+2: an analyst gets a fail notification, opens Aladdin, pulls the trade record, switches to a DTCC portal for DTC data, pivots to Bloomberg for counterparty context, and tries to triangulate root cause across three disconnected systems. That process routinely consumes 3-4 hours per fail. Multiply that across a busy week and you're looking at real throughput constraints — and the risk that some fails don't get fully investigated before cascading.
How an AI Agent Handles the Investigation
An AI Labor Company agent is trained on your operations team's actual fail investigation workflow — the logic your senior analysts use inside Aladdin, DTCC, and Bloomberg. Once deployed, it ingests fail notifications automatically, cross-references trade records against DTC settlement data and Bloomberg counterparty feeds, identifies root cause categories (custodian delay, counterparty fail, standing instruction mismatch, etc.), and delivers a structured investigation report to an analyst for final review and action. The analyst confirms, escalates, or closes — no data hunting required. Teams in this position typically see investigation time fall from 3-4 hours to under 30 minutes.
The Business Case
Settlement fails have direct cost consequences: fail fees, buy-in exposure, and regulatory scrutiny. The faster your team identifies root cause, the faster you can cure a fail or prevent a cascade. An agent running at this speed also frees experienced ops analysts to focus on pattern analysis and exception management rather than data retrieval — effectively expanding capacity without adding headcount. At $400K-$1M in annual trade ops labor and an efficiency improvement typically in the 65-85% range, the economics are straightforward. The agent is generally live and producing results within about 5 weeks.
Does the agent require direct write access to Aladdin or DTCC?
No. The agent reads and ingests data from your existing systems and produces a structured report for analyst review. All resolution actions remain with your team.
How does it handle the variety of fail types — counterparty, custodian, standing instruction?
The agent is trained on your team's existing categorization logic. It applies the same root cause taxonomy your analysts use, so the output maps directly to your downstream workflow.
Can it handle multi-custodian environments with different data formats?
Yes. The agent is designed for exactly this complexity — normalizing data from Aladdin, DTCC, and Bloomberg into a unified investigation view regardless of custodian.