Close Month-End
in 3 Days,
Not 12
Your PM system is terrible at reporting, and when payers quietly change their denial patterns, it takes months to catch. Our automation pulls data across your systems, spots anomalies like sudden spikes in denials for specific diagnosis codes, and surfaces trends before they drain revenue.
month-end close
recovered Year 1
accuracy
auto-reconciled
Month End Becomes a Reconciliation
Instead of a Decision
Your numbers live in a dozen systems that disagree with each other. Month end turns into a reconciliation exercise instead of a decision, and by the time the report is trustworthy the quarter has already moved on.
The Numbers Never Quite Agree
Bank, remittance, accounts receivable and general ledger each describe the same money differently. Month end becomes an exercise in finding where they diverged rather than deciding what to do next.
Provider-Level Adjustments Break the Tie-Out
The PLB segment sits outside any individual claim, so the remittance total and the deposit legitimately differ. Reconcile without accounting for it and your team spends days chasing a variance that was always explained.
Unapplied Cash Hides in Plain Sight
Payments that post without being reassociated to a deposit look like collections and behave like a problem. The longer they sit, the harder they are to trace back to a source file.
Variances Age Because Nobody Owns Them
A small unexplained difference with no name against it is the item still sitting there at year end. Amount matters far less than age and ownership.
The Close Takes Days You Do Not Have
When assembling the evidence takes longer than reviewing it, the report is trustworthy only after the decision it should have informed has already been made.
Sign-Off Without Traceability Is Just Trust
A controller certifying a close needs each reported figure to walk back to the transaction it came from. A reconciliation you cannot reproduce next month is not a control.
Revenue Reporting Automation
Built by RCM Experts
We built this reporting because we spent 28+ years assembling it manually every month. The metrics here are the ones we actually needed.
Reconciles every dollar, every source.
Pulls data across EHR, clearinghouse, bank, and PM systems. 87% of claims auto-reconciled, exceptions routed with full context.
Daily, weekly, monthly, all automated.
AR aging, cash posting, denial trends, and payer scorecards generated on schedule, no analyst hours required.
Close your books in 3 days, not 12.
AI agents reconcile overnight so month-end starts with clean data. No scramble, no spreadsheet archaeology.
Cleaner books, faster close.
Days in AR down 30%. Close cycle cut 75%.
AI catches revenue leaks fast.
Track variance patterns by payer, service line, and contract. AI surfaces underpayments, denial spikes, and posting anomalies before they hit the P&L.
Variance · Cardiology net revenue down 14% · Payer mix shiftRevenue Reporting Automation
Healthcare Finance Teams Trust
Reporting was the part of the month we dreaded, so we rebuilt it. Leaders get numbers they can trust inside the first quarter.
4-Hospital Community Health System, Midwest
A 4-hospital community health system with $340M in net patient revenue. Their finance team spent 12+ days every month matching lockbox deposits to posted payments, chasing missing remits, and manually resolving ERA exceptions, with no visibility into underpayment trends until months later.
time reduction
identified in Year 1
(down from 12 days)
to denial prevention
"We assembled these numbers by hand every month for years. The reporting surfaces what a revenue cycle leader actually asks for, not vanity metrics."
From 12 days to 3
to close the books.
Finance teams burn more than half their close cycle on reconciliation, and 1 to 3% of net revenue leaks away in undetected underpayments. Industry-average month-end close runs 12 days, while automatedreconciliation closes in 3. We didn't build revenue reporting from a vendor pitch deck. We've run revenue cycle operations from the front desk to VP, and we've watched the same payer-mix shifts, posting variances, and AR aging surprises ambush leadership month after month. Automatedreconciliation cuts the close cycle 75% and surfaces leaks before they hit the P&L, and that's what we tune for.
RCM Expertise First
Our founder has 28+ years in healthcare operations. We understand claim scrubbing workflows, payer behavior, and denial patterns before writing a single line of automation code.
"Build-a-Bear" Customization
Pre-built components for 1,800+ payers, major EHRs, and LCD/NCD rule engines. Assembled to your workflows. You own the source code, not a black-box SaaS subscription.
to Go-Live
6 to 8 Week Implementation
Not 6 to 12 months. We customize proven claim scrubbing frameworks to your workflows rather than building from scratch. Your revenue cycle cannot wait a year.
No Integration Required
AI agents work like your best employees. They log in via RDP/VPN, not API. No Epic IT projects, no Cerner change orders, no 6-month IT queues holding you back.
1,800+ Payers Pre-Mapped
Every major commercial payer, all Medicare MACs, Medicaid, and clearinghouses. We've already built payer rule libraries for them, we just configure for your accounts.
Proven ROI in 90 Days
Cost per touch drops from $18+/hr (manual) to approximately $0.06/touch (automated). Most clients see positive ROI within 90 days. We'll prove it before you commit.
Ready to Close Your Books
in Days, Not Weeks?
See how Innobot's revenue reporting and reconciliation automation can work in your specific environment. No pressure, no generic demo, just a real conversation about your close cycle and where revenue is leaking.
Common questions about automated revenue reporting
What exactly does revenue reconciliation automation do?
Our AI agents pull reports from your patient accounting system, access lockboxes to verify EFT deposits, match payments to remittance advices, identify discrepancies, and flag exceptions for human review. They process ERA/EOB payment posting with 99.98% accuracy and push reconciled data directly into your GL, replacing the manual matching work that consumes your finance team's first two weeks of every month.
How does this work with our existing EHR and practice management system?
No integration required. Our AI agents access your systems the same way a remote employee would, via RDP or VPN with standard user credentials. There's no API work, no Epic IT project, no Cerner change order. Works with Epic, Cerner, Meditech, NextGen, and 15+ other platforms. Your IT team creates a user account, and we handle the rest. All PHI stays within your environment.
What revenue cycle reports are available out of the box?
We deploy real-time KPI dashboards in Power BI tracking charges, payments, contractual adjustments, denials, and AR aging, broken out by payer, provider, service line, and facility. Our AI layer identifies anomalies like payer downcoding patterns, sudden denial spikes by diagnosis code, or remittance discrepancies that your PM system's standard reports would miss entirely.
How do you handle reconciliation across hundreds of different payers?
We have pre-built connections to 1,800+ payer portals including Availity, Optum, Medicare MACs, state Medicaid portals, and all major commercial payers. Our AI agents pull ERAs, check lockbox files, and cross-reference expected payments against actual remittances, automatically flagging underpayments, missing remits, and fee schedule variances across your entire payer mix.
What ROI can we expect from revenue reporting and reconciliation automation?
Cost per transaction drops from $18+/hour (manual) to approximately $0.06 per touch. One client recovered $17M in underpayments within 120 days through automated fee schedule comparison. A 4-hospital health system we work with reduced their month-end close from 12 days to 3 days and identified $2.3M in Year 1 underpayments that had been systematically missed. Most clients reach positive ROI within 90 days of go-live.
How long does implementation take?
6-8 weeks from kickoff to go-live. Weeks 1-2 are discovery (mapping your lockbox, ERA, and GL workflows). Weeks 3-4 we build the reconciliation logic and configure your Power BI dashboards. Weeks 5-6 we develop and test the AI agents. Weeks 7-8 we run incremental payment batches to validate accuracy before full production. We move this fast because we've already built these frameworks for 1,800+ payers, we're customizing proven automation, not starting from scratch.
How is this different from the reporting my PM system already provides?
PM system reports are backward-looking snapshots, they show you what happened after the fact, at a summary level, within a single system. Our automation does something fundamentally different: it cross-references data across your PM system, lockbox files, ERA remittances, and payer portals in real time. It catches underpayments by comparing actual reimbursements against your contracted fee schedules. It surfaces denial pattern shifts before they become write-offs. Your PM system tells you what was posted. We tell you what was missed.