The COO of a $500M+ GWP Lloyd's hybrid MGA told me something on a call last month that I haven't stopped thinking about. Her team was launching a new high-volume cyber product — micro-policies, high frequency, modest premiums. The underwriting economics were sound. The distribution was lined up. But when her credit control lead ran the numbers on what it would cost to match each premium to the bank statement, the per-policy match cost was approaching the premium itself. The product wasn't uneconomical because the underwriting was wrong. It was uneconomical because the back office couldn't scale with it.
Brisc AI is an insurance-native AI platform that automates cash matching, bordereaux reconciliation, and premium allocation for MGAs and reinsurers. We built the Reconciliation Analyst because every MGA we talk to hits the same wall — the cash-ops function that worked at five programs breaks at fifteen, and breaks catastrophically at thirty.
The breakpoints are predictable. Here are the six we see most often, what they cost, and what to do about each.
Every MGA's cash-matching workflow starts the same way: a capable analyst, a well-organized spreadsheet, and a manageable volume of broker remittances. At one or two programs, this works. The analyst knows the brokers, knows the formats, knows the quirks. The spreadsheet holds.
By program five, formats have fragmented. Three brokers report premium net of brokerage; two report gross. One sends a PDF; another sends an Excel file with tabs that change names every quarter. The spreadsheet that worked at two programs now has seventeen tabs and a formula chain that nobody except its creator fully understands. McKinsey estimates that 30-40% of underwriter and operations time in insurance goes to administrative tasks — and format fragmentation is a large share of that drag.
The fix isn't hiring another analyst. It's a system that learns each broker's format once and retains the knowledge permanently, so the fifth program onboards with the institutional memory of the first four.
This one is invisible until a CFO asks a question nobody can answer quickly: "How much cash is sitting in the trust account right now that we can't allocate to a specific program?"
The answer, at most MGAs running manual matching, is an uncomfortably large number. Cash arrives from brokers with reference codes that don't match the bordereau exactly — a transposed digit, a truncated program name, a wire memo that says "WIRE TXN 8847291" when the corresponding bordereau entry says "Program A, Q2 2026." The cash is there. The allocation isn't. Finance can't close the position until someone manually traces the payment back through broker correspondence.
At one MGA we work with, a single $47,000 discrepancy took three months of broker correspondence to trace. The money was never missing — it was sitting in suspense, correctly received, incorrectly labeled, while an analyst rebuilt the chain of custody one email at a time.
The fix is pattern recognition that persists. Brisc's Reconciliation Analyst remembers that Broker X always truncates program names to eight characters, that Broker Y nets commission before remitting, and that Broker Z's wire desk drops the last two digits of the reference code every third month. The next time the same pattern appears, the match is automatic. Knowledge compounds instead of walking out the door.
This is the breakpoint that turns cash matching from a back-office nuisance into a go/no-go on an entire product line. When an MGA launches a high-volume, low-premium product — cyber SME, embedded insurance, parametric micro-policies — the per-policy match cost doesn't scale the way the underwriting economics assume.
A traditional credit control team matching eighty bordereaux per month across thirty-eight binder partners and six geographies can absorb the work when each policy carries meaningful premium. But when premium per policy drops to double digits and volume quadruples, the cost of matching one premium to one bank entry approaches the premium itself. The product is dead on arrival — not because the loss ratio is wrong, but because the operations can't keep up without hiring proportionally.
One $500M+ GWP MGA we spoke with runs a twenty-three-person credit control team — eighteen offshore through a BPO and five onshore. That team is the product of years of incremental hiring to keep up with program growth. For a PE-owned MGA, the board's scoring function is cost reduction, not new spend. The value of automation isn't enabling upside the board hasn't modeled — it's displacing a labour line the board already sees and scrutinizes.
The fix is capacity that scales with volume, not with headcount. Brisc's Reconciliation Analyst processes the same match at the same accuracy whether the volume is eighty bordereaux per month or eight hundred, because the compute cost of matching doesn't scale linearly with human effort.
Every MGA has one. The senior analyst who knows that Cedant A always reports premium net of brokerage in column F. That Cedant B's profit commission true-ups arrive in narrative form, two months late, every Q3. That Cedant C's Excel tabs changed names last September and nobody updated the template.
That analyst's knowledge is institutional memory — and it lives nowhere except their head. When they take vacation, matching slows. When they leave, it breaks. Industry data suggests 20-40% annual turnover in insurance back-office roles, and a 90-to-180-day ramp for a new hire to become productive in cash matching. That's three to six months of degraded accuracy every time someone walks out the door.
This is the knowledge-retention problem that Brisc was built to solve. The Reconciliation Analyst accumulates every broker quirk, every format exception, every cedant-specific rule into a permanent dictionary. The next operator to encounter the pattern doesn't relearn it — the system already knows. A team of four with a Reconciliation Analyst carries the institutional memory of a team of twelve that's been working together for a decade.
Broker reports change. Column definitions shift silently. A field that said "Gross Premium" last quarter now says "Premium (Net of IPT)" with no notification. The analyst who processes this month's file may not notice the change — the column header looks close enough — and the mismatch surfaces three months later during quarterly reconciliation, or worse, during an external audit.
Cross-jurisdiction tax variance makes this harder. For an MGA writing across the UK, EU, and US, the difference between UK Insurance Premium Tax, EU stamp duties, and US surplus lines taxes means the same premium number can be reported three different ways by three brokers in three countries for the same program. The COO of one Lloyd's hybrid MGA called cross-jurisdiction tax variance her number-one matching blocker — not because the math is hard, but because the formats encoding the math are inconsistent and keep changing.
The fix is a system that detects format drift at ingestion, flags the change, and adjusts the matching logic before the mismatch compounds. Brisc's Reconciliation Analyst compares each incoming file against the historical pattern for that broker and program. When a column definition shifts, the system surfaces the change before it becomes a reconciliation problem — not three months later.
This is the breakpoint that's hardest to see in real time and most expensive when it arrives. Most MGAs run reconciliation periodically — monthly or quarterly. Between cycles, small mismatches accumulate. A premium posted to the wrong program. A commission calculated on the wrong basis. A timing difference between when cash was received and when the bordereau was processed.
Each one looks minor in isolation. But when an auditor or a carrier requests a full reconciliation of a multi-year treaty, the team discovers that the "clean" records were reconstructed after the fact rather than accumulated as the work was done. One reinsurance audit we encountered revealed a 12% true profitability uplift that had been invisible because periodic reconciliation masked systematic under-reporting across three years of cedant data.
The fix is continuous reconciliation with a built-in audit trail. Every match the Reconciliation Analyst makes is logged with the source document, the extracted fields, the matching logic, and the confidence score. The audit trail isn't reconstructed at quarter-end — it exists from the moment the data arrives. When a CFO or auditor asks "show me the history of Program X's premium," the answer takes seconds, not weeks.
Every one of these breakpoints is a variation of the same structural problem: institutional knowledge that doesn't persist. Broker quirks live in analysts' heads. Format changes go undetected because pattern recognition is manual. Reconciliation gaps compound because the work is periodic rather than continuous.
Brisc's Reconciliation Analyst is built on the premise that the knowledge should compound inside the system, not inside the people. Every cedant quirk, every broker format exception, every matching pattern the system encounters becomes part of a permanent dictionary. Helix Underwriting Partners reports that Brisc removed 80% of manual labour from their submission processing — and the mechanism is the same for cash matching: retained knowledge eliminates re-learning.
The MGA that runs thirty programs on the same team that ran five isn't moving faster. It's moving with the accumulated context of every program it has ever onboarded, every broker format it has ever processed, and every exception it has ever resolved. The 59% labour cost reduction Brisc customers see isn't a speed gain — it's the elimination of re-work that should never have existed.
What is cash matching in insurance? Cash matching is the process of reconciling incoming cash — broker remittances, wire transfers, and bank statement entries — against the corresponding bordereaux, premium schedules, and program records to confirm that every dollar received is allocated to the correct program, period, and transaction. For MGAs with multiple binder partners and programs, this is one of the most labour-intensive functions in the back office.
Why does cash matching become harder as an MGA grows? Growth introduces format fragmentation (more brokers, more report styles), jurisdictional complexity (different tax treatments), and knowledge concentration (more rules living in fewer heads). The relationship between program count and matching difficulty is not linear — it compounds, because each new program introduces interactions with every existing format and process.
What is a trust account in suspense? A trust account in suspense holds cash that has been received but cannot yet be allocated to a specific program or transaction. This typically happens when the payment reference doesn't match the bordereaux record exactly. Unallocated cash distorts cash-position reporting and creates audit risk until the allocation is manually resolved.
How does AI handle broker format drift? An insurance-native AI system like Brisc's Reconciliation Analyst compares each incoming file against the historical pattern for that broker and program. When column definitions, headers, or calculation bases change, the system flags the drift at ingestion rather than letting the mismatch compound through a full reconciliation cycle. The system adjusts its matching logic based on the detected change.
What does "Excel archaeology" mean in insurance operations? Excel archaeology describes the manual process of tracing cash, premium, and commission data across multiple spreadsheets, bank statements, broker portals, and email threads to reconstruct a reconciliation. The term comes directly from insurance operations teams who describe the four-screens-open workflow of matching data that exists in incompatible formats across disconnected systems.
Can Brisc handle cross-jurisdiction tax variance? Yes. Cross-jurisdiction tax variance — UK Insurance Premium Tax, EU stamp duties, US surplus lines taxes — is one of the most common sources of reconciliation mismatch. The Reconciliation Analyst learns the tax treatment rules per broker, per program, and per jurisdiction, and applies them consistently. When a broker changes how they encode tax in their remittance, the system detects and surfaces the change.
How long does it take to deploy a Reconciliation Analyst? Typical deployment takes 2-6 weeks, depending on the number of programs and the complexity of existing broker formats. The system ingests historical files to build its initial broker-format dictionary, and accuracy improves with each subsequent cycle as the dictionary grows.
What's the ROI of automating cash matching? Brisc customers report a 59% reduction in labour costs associated with cash matching and reconciliation, driven primarily by the elimination of re-work and manual format translation. The ROI is highest for MGAs at the growth inflection — five to fifteen programs — where the cost of adding headcount to maintain accuracy becomes disproportionate to the value of the matches.
If your MGA is hitting any of these six breakpoints, you're not alone — and you don't need to hire your way out of it. See how Brisc's Reconciliation Analyst works with your data →