The 5% You Write Off Is Where the Money Lives
A 95% match rate sounds strong until you size the 5% left over. On a large book, that residual is where the expensive errors hide.
CEO, Brisc AI
Sanjay writes about insurance back-office operations and what AI actually changes about them.
A 95% match rate sounds strong until you size the 5% left over. On a large book, that residual is where the expensive errors hide.
Seven questions to ask before buying reinsurance bordereaux automation, from messy cedant files to accuracy at day ninety and true cost.
Brokers and MGAs on Morning Data's NOVUS still match cash by hand. Where Brisc fits alongside NOVUS: evidenced matching that keeps funding current.
Vendors quote per reconciliation but define the unit differently, so the same book can cost four times more. How to compare proposals fairly.
When submission intake becomes infrastructure, underwriters stop rekeying broker data and start reviewing it. Here is what changes for an MGA.
Monthly reconciliation understates your cash position, hides premium leakage and leaves regulatory gaps between cycles. What CFOs need to know.
ACORD ADEPT is fixing structured data exchange in reinsurance. Most cedants still send PDFs and Excel, and that is where the back-office work lives.
Back-office turnover costs far more than a recruiting fee. Ramp errors, rediscovery and lost knowledge compound with every departure.
A $47K reconciliation error never costs $47K. The investigation, correction cascade, lost trust and pricing distortion that follow cost far more.
The fear is that AI creates a second system of record. Why a matching layer is not a second ledger, and your PAS stays the source of truth.
Adding reconciliation staff, onshore or offshore, stops working as the book grows. Turnover, ramp time and handovers erase the gains.
What an insurance AI Analyst actually learns from corrections: rules, formats and institutional memory that compound, and what it never learns from.
When insurance talks about AI, it means underwriting. The back office that executes those decisions needs a different discipline. Here is what changes.
A reconciliation error does not stay in finance. It becomes a broker dispute, then a retention problem. The five-stage loop, and how to break it at entry.
Claim approved is not claim received. Where money stalls between carrier, TPA, and claimant, and what a proving layer over the payment chain changes.
A week-by-week walkthrough of deploying Brisc's Reconciliation Analyst: from ingestion and baseline through steady state at 97%+ accuracy by day 90.
CASS 5, SSAP 6, Lloyd's delegated authority: the regulator examines the reconciliation itself. Why unmatched cash is the finding, and how lineage fixes it.
When per-policy match cost approaches the premium itself, Cyber SME, embedded, and parametric lines stop scaling. Here's why — and how Brisc AI fixes it.
Cross-jurisdiction tax variance — UK IPT, EU stamp duties, US surplus lines — is the #1 reason insurance cash matching breaks. Here's why & how to fix it.
Every reconciliation demo looks perfect. Most pilots stall within six weeks. Here are 8 questions CFOs should ask before buying insurance reconciliation AI
Six SICS cession-accounting tasks depend on institutional memory, not speed. Here's why an AI Reconciliation Analyst — not more headcount — is the fix.
Automating MGA premium cash application means connecting your bank feed, bordereaux & remittances to matching logic that understands insurance. Here's how.
When your best SICS operator leaves, the cedant knowledge leaves too. Here's what that costs — and how reinsurers stop losing it.
Cedants send PDFs. SICS needs structured data. The operators bridging the gap cost more than your license & take the dictionary with them when they leave.
Bank reconciliation in insurance means matching bank deposits against premium bordereaux and broker remittances — a three-way match that generic tools miss
A step-by-step walkthrough of what the agentic cash ops workflow actually looks like in practice, inside real insurance operations. Learn more.
Most reinsurers can’t see their true matched cash position in real time. Here’s how automated reconciliation impacts float, capital efficiency & operations
A practical guide to evaluating bordereaux reconciliation software — from cash matching and audit trails to format drift and deployment timelines.
6 cash-matching breakpoints that stall MGA growth — format drift, suspense traps, high-volume products — & how Brisc AI eliminates the rework permanently.
A behind-the-scenes look at manual insurance cash matching, why it drains experienced teams, and how Brisc AI can make reconciliation scalable.
A look inside insurance Cash Ops — the hidden reconciliation workflows, cash matching problems, and operational costs slowing MGA growth. Learn more.
Manual bordereaux processing in SICS and legacy reinsurance platforms creates hidden risk, institutional knowledge loss, and costly reconciliation delays.
Clean bordereaux doesn't mean your cash position is accurate. Learn where financial risk hides in DA programs & how real-time reconciliation closes the gap
Learn how to evaluate bordereaux reconciliation tech. Key criteria include state tracking, premium-to-cash matching, exception handling & auditability.
See why claims bordereaux ingestion fails at scale—and how claims-aware validation improves data trust, financial integrity, and operational control.
Bordereaux processing is broken. We've seen it from inside insurance operations. Here's what actually changes when you automate it the right way.
Insurance bank reconciliation is uniquely complex — and solvable. An insider's perspective on why most teams are still stuck and what changes everything.
From intake to underwriting, see how Brisc AI processes broker submissions—extracting, validating, and delivering quote-ready data in minutes.
Build an AI-ready underwriting desk by shifting from data entry to data advantage. See how structured intake data improves pricing, capacity, and insight.
MGAs grow GWP by reviewing more submissions, quoting faster, and improving early-stage underwriting. Learn the operational levers that drive premium growth
A data-driven look at how manual intake drives adverse risk selection in a softening market & why Agentic AI is the operational imperative for MGA leaders.
Discover how AI agents are transforming underwriting by eliminating manual intake, structuring data, and freeing underwriters to focus on risk assessment.
Discover how AI-powered claims automation helps insurers cut costs, speed settlements, and improve accuracy across the entire claims lifecycle.
What explainable AI means in insurance operations: every value traced to its source document, every rule named, every exception shown.
Discover why insurance-native AI outperforms generic solutions, with faster deployment, higher accuracy, and built-in compliance insurers can trust.
Claims triage breaks when notices arrive faster than anyone can read them. What changes when every document is read in full at intake.
Insurers seeing the biggest AI gains aren’t replacing teams — they’re empowering them. Learn how Human-in-the-loop AI is driving better outcomes.
Solve underwriting submission bottlenecks. Learn how Brisc's AI agent automates intake and triage & speeds up quoting, seamlessly improving workflows.
Learn how to unlock the value of unstructured insurance data with AI. Automate, extract insights, and act faster—without replacing your core systems.
Vertical AI outperforms general AI in regulated industries like insurance, with domain-specific accuracy and built-in compliance. See why vertical AI wins.
Quote to bind slips when submissions back up. Four places AI lifts the ratio, starting at intake, and what each one changes for underwriters.