Your Cash Reconciliation Problem Isn’t a Capacity Problem

Every month, finance teams spend hours—sometimes days—matching bank transactions to invoices, bills, and charges in the GL. It's one of the most repetitive, time-consuming parts of the month-end close, and it's almost entirely manual at most companies. 

A transaction comes in. Someone looks it up. They find the invoice (or don't). They post the entry. Repeat 300 times.

If that's still how your team closes, it's not a capacity problem. It's an architecture problem.

Rillet's Cash Reconciliation Engine does this for you. By the time you open the reconciliation screen, most of the work is already done. You review the exceptions, not the routine.

The Root Cause: Your Bank Feed Has No Context

Cash reconciliation is painful because most accounting systems treat every incoming bank transaction as a blank slate, and your bank feed drops hundreds (sometimes thousands) of transactions into the accounting system. 

Your GL has no idea whether that $12,400 ACH is from a Stripe payout, a wire from a customer you invoiced 30 days ago, or a vendor reimbursement your AP team processed last week. Someone on the finance team has to figure out which bank entries map to which invoices, which bills, which expense charges, and which just need a new journal entry. 

Do it wrong, and your cash position is off. Do it manually, and your close slows to a crawl. The fix isn't more headcount. It's a system that brings context to the transaction the moment it lands.

How Rillet's Cash Reconciliation Engine Works

Rillet's already auto-matches 95% of cash transactions. The goal of the Cash Reconciliation Engine is to make that number as close to 100 as possible, while making the remaining exceptions fast to resolve. 

Our engine uses a four-layered matching system designed to resolve the vast majority of transactions automatically, without requiring manual review. It runs every transaction through a layered matching system, each catching what it can and passing the rest down, starting with certainty and ending with informed judgment. 

Not every finance team wants the same level of hands-off behavior, so auto-match is configurable. You choose how aggressively the system auto-matches. 

Layer 1: Integration Matches

If the transaction originates from a connected integration, like Stripe or Ramp, Rillet already knows exactly what it corresponds to. Stripe sends invoice metadata. Ramp provides expense IDs. These transactions match automatically with no human input required. For most customers, this layer alone handles the majority of transaction volume.

Layer 2: Deterministic Matching

For bank feed transactions without integration context, Rillet runs a structured matching algorithm against your open invoices, bills, and other entities in the system. It checks whether the amounts align, whether the invoice or expense number appears in the transaction description, and whether the date proximity makes sense. When all signals check out, it becomes a high-confidence match.

Layer 3: Reconciliation Rules

Recurring charges like rent, utilities, and bank fees don’t have associated bills, and they shouldn't need one. You can define a merchant name or description pattern once, and Rillet routes those transactions to the correct GL account automatically from there on out. You can build a rule directly from the matching screen, at the moment of the match, without leaving the workflow. Every rule you create makes the queue smaller next month.

Layer 4: AI-Powered Suggestions

When the prior three layers don’t resolve a transaction, Rillet's matching engine uses similarity algorithms or Aura AI to provide confidence-weighted suggestions. These suggestions are based on how your organization has categorized similar transactions in the past. Suggestions carry a confidence indicator so your team knows how much to trust them before approving. What surfaces in the review queue is the 5% that genuinely needs a human, not the other 95. 

High-confidence matches are ready to accept in a single click. If something needs adjustment before matching, like splitting an amount or changing a GL account, an inline edit keeps the workflow moving without leaving the screen. Depending on your team's risk tolerance and audit requirements, you can configure it to never auto-match, to auto-match only by rules, or to auto-match all high-confidence transactions. 

Why This is Only Possible on Rillet’s Real-Time Architecture

Cash reconciliation sits on the critical path of month-end close. Until your bank accounts are reconciled, you can't finalize the balance sheet. Until the balance sheet is finalized, you can't produce the financial statements your board, investors, or auditors need. 

Teams that do this manually are compressing enormous work into the first five business days of every month. Errors introduced at that stage, whether it’s a transaction miscoded, a payment applied to the wrong invoice, or a transfer mistaken for revenue, create cascading corrections that push close timelines further and create audit exposure.

Rillet’s continuous reconciliation, as in matching transactions throughout the month rather than in a post-period scramble, is what makes fast, clean closes possible. When the bank is already reconciled before the period ends, month-end becomes a confirmation rather than a crisis.

Our Cash Reconciliation Engine is native to the integrations that matter, like Stripe, Ramp, Brex, Plaid for bank feeds, and 100+ others. These integrations are built by people who understand how the GL actually works, so Aura AI isn't guessing against stale data. It's acting on a live GL that reflects the current state of your books. 

Every other ERP has bolted AI onto a legacy batch-processing system. It's not a better QuickBooks. It's not a lighter-weight NetSuite. Rillet rebuilt the entire ERP from scratch with real-time architecture as the foundation. 

That's not a feature distinction. It's a structural one.

Brock Beyer, Controller at Jump, put it this way after replacing QuickBooks:

"Most of the items that took so long (e.g., bank matching, Ramp T&E, etc.) are done in real-time throughout the month because of Rillet. It's allowed me to handle more work than I ever thought possible."

‍Jump now auto-reconciles 99% of thousands of Stripe transactions each month, and 95% of its total monthly transactions are auto-coded. Their controller now singlehandedly manages the volume that used to require a team of four.

It's not magic. That's just what continuous reconciliation looks like. By the time month-end arrives, the bank is already reconciled. Close isn't a process you run. It's the state your books are in. If you want to see how Cash Reconciliation works on your own transaction volume, book a demo.