The real cost of outdated reconciliation (and how AI fixes it)
By Team DwellFi
In fund administration, reconciliation is one of those processes everyone accepts as a necessary burden. It’s time-consuming, error-prone, and pulls high-value talent away from strategic work. Yet, despite its inefficiencies, firms continue to rely on the same manual workflows that have existed for decades.
That needs to change.
The problem: A system that doesn’t scale
Private markets are evolving rapidly, but reconciliation processes remain stuck in the past. The challenges are clear:
- Fragmented Data Across Multiple Sources: Custodians, accounting platforms, bank feeds. Teams are constantly switching between systems to verify transactions.
- Manual Exception Handling: Identifying and resolving discrepancies often requires days of back-and-forth investigations.
- Regulatory Complexity: Compliance demands precision, yet outdated processes increase the likelihood of errors.
- Talent Drain: High-caliber professionals are stuck doing reconciliation instead of focusing on client servicing, portfolio insights, and strategic growth.
For firms looking to scale, this operational bottleneck isn’t sustainable.
The shift: AI as the force multiplier
At DwellFi, we saw an opportunity to completely rethink reconciliation, not with basic automation, but with AI-driven agents that work alongside fund admins to remove inefficiencies.
That’s why we built Finley, our Fund Accounting Operations Agent.
What Finley actually does (and why it’s different)
Finley is more than an automation script, it’s an intelligent agent purpose built specifically for private markets. It understands the structure, hierarchy, and nuances of capital flows, so the outputs are tailored to how fund administrators actually work.
Here’s what Finley delivers:
- Auto-ingests all reconciliation inputs: bank statements, GL exports, custodial files, trial balances, and system-generated reports.
- Understands private-markets context: capital calls, distributions, management fees, waterfalls, leverage activity, and cash movements.
- Runs the matching logic automatically: line-item matching, variance detection, tolerance checks, aging rules, and multi-period roll-forwards.
- Uses your rules, not generic ones: Finley incorporates each firm’s reconciliation rulebook, exception criteria, naming conventions, and custom tolerances.
- Learns from your workflows: it adapts as teams resolve exceptions, improving accuracy every cycle.
- Surfaces clean, explainable outputs: exception reports, reconciled tables and recommended actions, all delivered in your preferred accounting templates.
- Can run on a schedule or on demand: daily, hourly, end-of-day, or tied to custodian file availability.
- Provides continuous monitoring: alerts when new discrepancies appear or when data feeds drift from expected patterns.
- Fits into existing processes: no need to overhaul accounting systems or workflows; Finley works alongside teams and systems already in place.
Finley isn’t another dashboard asking fund admins to change how they work. It’s an operational partner that makes reconciliation faster, clearer, and dramatically more accurate.
A simple example: where a manual team may spend hours tracing a single unresolved cash movement across systems, Finley identifies, matches, and explains the variance in seconds, complete with an audit trail.
And the impact is clear:
- Up to 80% reduction in reconciliation time
- Fewer manual errors and exception loops
- Faster month-end closes and NAV cycles
- Teams freed to focus on analysis, not data chasing
- Operations that finally move at the speed of investment decisions
Firms embracing AI-driven reconciliation aren’t just fixing inefficiencies, they’re future-proofing their operating model.
At DwellFi, we strongly believe that AI doesn’t replace humans, it amplifies them. The question is whether firms will adopt it fast enough to stay ahead.
If reconciliation is still a bottleneck, experience what enterprise-grade AI accuracy looks like in practice.
Book a demo to see Finley in action.