There's a question that every insurance CFO should be able to answer on any given day: how much cash is sitting unallocated right now, and why?
In practice, most can't answer it without kicking off a process that takes days or weeks. That's not because the information doesn't exist. It's because reconciling bank transactions against bordereaux records, premium schedules, and claims settlements is one of the most labor-intensive processes in insurance finance — and most teams are still running it the same way they did a decade ago.
We've worked inside these operations. We've seen the spreadsheets with seventeen tabs. We've seen the email chains where analysts try to trace a $47,000 discrepancy back through three months of broker correspondence. And we've seen what happens when the person who manages that process takes a two-week vacation and nobody else can pick up where they left off.
Bank reconciliation in insurance isn't inherently difficult. It's just never been given the right tools.
Bank reconciliation in other industries is relatively straightforward: match invoices to payments, flag the exceptions, move on. Insurance is different — and anyone who's actually done it understands why.
In a typical MGA or reinsurer, a single bank deposit might contain premiums from multiple programs, each reported through separate bordereaux from different brokers. The reference codes on the bank statement rarely match the policy numbers on the bordereaux exactly. Claims payments flow through different accounts with different settlement timing. And the underlying data lives in at least three disconnected systems that were never designed to talk to each other.
The result is a matching problem that's unique in its complexity. You're not just reconciling two data sources. You're reconciling bank statements against bordereaux files against policy records against broker communications — each with its own format, terminology, and level of detail.
Most off-the-shelf reconciliation tools fail here because they don't understand this complexity. They're built for clean, structured data with consistent identifiers. Insurance cash flow is none of those things.
When bank reconciliation runs monthly — or worse, quarterly — the problems compound in ways that aren't immediately visible.
Unallocated cash balances grow because nobody has time to investigate every mismatch in real time. Short payments from specific brokers become an accepted pattern instead of an escalation trigger. Settlement disputes take longer to resolve because the evidence is reconstructed after the fact instead of documented as it happens.
We've seen organizations where several million dollars sat in suspense accounts for months because the reconciliation backlog meant nobody could confirm which program the money belonged to. The cash was there. The information to allocate it was there. But the process to connect them simply couldn't keep pace.
And when audit season arrives — whether it's an internal review, a regulatory examination, or a reinsurance commutation — the scramble to produce clean records exposes every shortcut and gap that accumulated during the year. The audit trail lives in someone's inbox. The matching logic lives in someone's head. And if that someone has moved on, the institutional knowledge is gone.
Effective bank reconciliation automation in insurance isn't about applying generic matching rules to cleaner data. It's about building intelligence that understands the specific patterns of insurance cash flow.
That means a system that can ingest a bank statement alongside premium and claims bordereaux, map transactions to expected payments even when reference codes don't match exactly, and learn from the matching rules your team applies — so the exceptions it surfaces are the ones that genuinely need human judgment, not the ones it should have handled automatically.
When this works well, the shift is dramatic. Matching that consumed weeks compresses to hours. The accuracy moves above 97% because the system catches patterns manual review consistently misses — systematic underpayments from a particular broker, recurring timing discrepancies in claims settlements, duplicate entries that look slightly different depending on the source.
More importantly, reconciliation becomes continuous rather than periodic. Finance teams see cash position in real time. They can answer the CFO's question — "where does our cash stand today?" — without launching a project to find out.
Brisc's approach to bank reconciliation came directly from years of watching finance teams struggle with this problem. We didn't start with a reconciliation engine and add insurance knowledge later. We started with a deep understanding of how insurance cash flow actually works — bordereaux-driven, multi-source, format-inconsistent — and built automation around that reality.
The platform ingests bank statements alongside bordereaux data in any format, applies intelligent matching that improves over time, flags genuine exceptions with full context, and produces audit-ready documentation automatically. Teams deploy in weeks, not months, because it works with existing banking and accounting systems rather than replacing them.
The organizations running this today are processing reconciliation 10x faster while reducing labor costs by 59%. But the more meaningful metric is this: their finance teams are spending time on analysis, broker relationships, and strategic decision-making instead of matching line items in spreadsheets.
The technology to solve bank reconciliation in insurance exists. The question is whether your team's time is better spent running the old process or focusing on the work that actually needs a human mind.