Reconciliation is hard. Most people assume the difficulty is volume. That part is largely solved. The hard part is what comes after.
Millions of transactions, multiple ledgers, the sheer mass of data. With modern infrastructure and AI, ingesting a hundred million records and matching the obvious ones is no longer a meaningful technical barrier.
The hard part is what comes after.
The anatomy of the real problem
A reconciliation pipeline has to absorb data from systems built by different teams, for different purposes, at different points in time. The processor pushes a file in one format. The bank pushes another. The internal ledger reflects a third reality. Connectors that were never designed to speak to each other are forced into a daily handshake.
The data shifts format. Fields get renamed. A counterparty’s API silently drops a column. Something that worked for eighteen months starts breaking on a Tuesday because an upstream vendor pushed a release nobody told you about.
Third parties introduce inconsistency you don’t own and cannot fully control. A timing difference here. A chargeback definition there. A fee structure that changed two weeks ago.
And there is always an infinite tail of edge cases that only show up in production after you were certain you had covered every scenario.
This is why static rule sets cap out. You can write logic for the patterns you have seen. You cannot write logic for the ones you haven’t. Every new edge case is a new ticket, a new rule, a new piece of code maintained by an engineer who probably wishes they were working on something else. That backlog never shrinks. It compounds.
This is also why AI is the right architecture for the job. Not as a buzzword. As the only approach that handles this kind of variability at a cost structure that makes economic sense. The technology window is real. Reconciliation is finally solvable in a way it was not five years ago.
The cost most teams don’t see until it’s too late
The pain is measurable.
PYMNTS Intelligence research with Mastercard found that 44% of U.S. businesses consider data reconciliation their most pressing accounts receivable concern, nearly twice the rate of any other problem cited. Separately, 59% link poor cash flow and forecasting to manual AR processes, and 24% still run their workflows on spreadsheets.
EY estimates finance personnel spend 75% of their time on routine compliance work, which includes data collection, cleansing, and reconciliations. Only 25% goes to analysis or planning.
For financial institutions specifically, PYMNTS Intelligence puts the annual loss from operational inefficiencies in reconciliation at $98.5 million per institution, most of it driven by manual workflows and siloed systems.
And the resolution side is even slower. Smartstream research cited by Finextra found that more than 70% of cross-border payment exceptions still take longer than five days to resolve. Not five hours. Five days.
Automation is not the finish line. It’s the start.
Here is where most implementations get it wrong. They automate the data processing layer and declare victory. The matching engine works. The dashboard looks clean. The pilot is signed off.
Then production starts.
After every reconciliation cycle there is a remainder. A subset of transactions that didn’t match, didn’t reconcile, or threw an exception. These are not edge cases of the data. They are exceptions of the world. A processor settled late. A bank rejected a payout for a reason that wasn’t in the original spec. A partner sent a duplicate file. Something broke upstream, and someone, somewhere, needs to resolve it.
That is the last mile. It is where the most value is either recovered or quietly written off.
The proof is visible across the market. There are companies running Blackline, Simetrik, and Trintech, serious and well-engineered software, and still maintaining teams of analysts whose full-time job is to work the exception queue. They open Excel. They send emails. They get on calls with their processor’s account manager to figure out why a settlement file is short by four thousand dollars. The software automated data aggregation. Nobody automated the resolution.
This is not a feature gap. It is a category-defining gap. Automating transaction matching gets you to the starting line. Automating discrepancy resolution is where the actual value lives, because that is where the time is spent, where the money leaks, and where the real risk is created.
Reconciliation is a coordination problem
Strip away the spreadsheets and the dashboards and look at what the work actually is. A reconciliation team spends most of its time coordinating. With internal systems. With a counterparty’s operations team. With a processor’s support queue. With a bank’s settlement desk. With a partner that needs to send a corrected file.
The exceptions don’t resolve themselves. They get resolved when humans coordinate across organizational boundaries. The “data problem” framing that has dominated the recon software market for two decades is the wrong abstraction. Reconciliation is a coordination problem with a data layer underneath it.
That reframe changes what the right product looks like. If reconciliation is a coordination problem, the product cannot stop at the matching engine. It has to extend into the resolution workflow itself. It has to investigate the discrepancy, identify the counterparty, propose a fix, and increasingly, execute the resolution on behalf of the team.
How Rexi is built
We started Rexi with a different posture than the legacy vendors. We are not selling software that a customer’s team uses to investigate exceptions. We are selling an outcome: your reconciliation closes, fully, on a daily cadence. We own that result.
That means we take responsibility for the last mile. The discrepancy investigation. The coordination with third parties. The reconciled close at the end of every cycle. The customer’s team gets their time back. They do not get a better dashboard for the same Excel work.
Yes, we use AI. We use it heavily, across matching, classification, anomaly detection, and resolution. But the buyer does not need to care about that. The buyer cares that the flows match, that the exceptions are worked, and that the team is no longer the bottleneck. AI is how we deliver the outcome. It is not the outcome itself.
We act as a partner, not a vendor. The standard we hold ourselves to is that your books close on time, fully reconciled, every day. If they don’t, that’s our problem to solve, not yours.
Where this is going
The current product is good enough to ship and create real value today. The endgame is more interesting.
The natural architecture for resolving discrepancies is an agentic one. Autonomous agents that investigate exceptions, coordinate with the relevant third parties, and execute the resolution. The protocol layer for this is starting to take shape. MCP, and similar emerging standards, make it possible for agents on different sides of a transaction to communicate, exchange context, and converge on a resolution without the human escalation chain in between.
When a discrepancy hits, an agent on our side reaches an agent on the processor’s side. They exchange the relevant context. They identify the root cause. They propose and execute the resolution. The customer’s finance team sees a notification: this is what happened, this is what we did, here is the audit trail. No ticket. No call. No spreadsheet.
That is when reconciliation stops being a finance function and becomes back-office infrastructure. Present, reliable, and largely unseen. The way payment rails are today.
That is the future we are building toward. The current product is the wedge.
If this is your day-to-day
If you are running reconciliation today, at a bank, a fintech, an insurer, a marketplace, and the last mile is still consuming your team’s time, we would like to show you a different version of the work.
Book a discovery call and try our problem-solver agent. The fastest way to understand what changes is to see your own exceptions get resolved.