Author: Simetrik editorial team

Every fintech CFO has the same answer when you ask about reconciliation: “We’re covered.”

The dashboards are green. The month-end close completes on schedule. The board deck shows transactions processed, revenue recognized, and cash positions balanced. Everything looks healthy.

And then someone finds $2 million in losses that was there the entire time.

This is what we at Simetrik call the Confidence Gap, the distance between what finance leaders *believe* their reconciliation covers and what it *actually* covers. It is the most expensive blind spot in modern financial operations, and it is growing wider as payment complexity accelerates.

The Anatomy of an Invisible Problem

The Confidence Gap does not announce itself. There is no failed reconciliation run, no red flag in the dashboard, no alert from your payment processor. The numbers check out at the level you are looking at. The problem is that you are looking at the wrong level.

Consider a real case: a sophisticated payment processor operating across three markets, processing billions in annual volume. They had reconciliation processes. They had a finance team. They had dashboards. And buried inside their data were three separate sources of margin leakage totaling $2 million. None of which were visible to their existing controls.

The first was a partner bank incorrectly withholding taxes on transactions. At the aggregate level, “taxes paid” checked out. At the transaction level, the wrong taxes were being applied to the wrong transactions. The second was a transaction code ambiguity,  multiple acquirers used the same code for refunds and chargebacks, causing missed dispute deadlines and absorbed losses. The third was a pattern of duplicate debits on refunds spanning multiple countries, invisible without cross-entity pattern detection.

These were not edge cases. They were systemic, ongoing losses hiding behind reconciliation processes that looked perfectly functional.

Three Forces That Widen the Gap

The Confidence Gap is not caused by negligence. It is a structural consequence of three forces that compound as fintechs scale.

Volume masks discrepancies

When you process millions of transactions, aggregate numbers look healthy even when errors are accumulating underneath. A $500 tax misapplication on one transaction is noise. That same error compounding across thousands of transactions over months becomes a seven-figure problem, and it never triggers an alert because the totals still balance within acceptable thresholds.

Complexity outpaces process

Every new payment method, every new market, every new banking partner adds reconciliation complexity. Your team bolts on new processes to handle the new flows, but those processes were designed for yesterday’s architecture. The result is a growing patchwork of manual reviews, spreadsheet cross-references, and scripts that work until they silently stop.

Reconciliation is treated as hygiene, not infrastructure

It gets investment last, attention last, and talent last. Your best engineers are building product features, not reconciliation pipelines. Your best analysts are preparing board decks, not investigating transaction-level anomalies. Reconciliation gets treated as a back-office checkbox until a regulator, an auditor, or a $2 million loss forces it into the spotlight.

The Regulatory Signal You Cannot Ignore

If the business case alone does not close the gap, the regulatory environment should.

In late 2024, the FDIC proposed a new recordkeeping rule for custodial accounts. This was a direct response to the Synapse Financial Technologies collapse, which left over 100,000 consumers locked out of their money and exposed an estimated $65 million to $95 million shortfall between bank records and actual customer balances. The proposed rule pushes banks partnering with fintechs toward transaction-level traceability of beneficial ownership. A standard that makes aggregate reconciliation insufficient by design.

While the rule’s timeline has shifted under the current administration, the direction is clear: regulators are moving toward requiring the granularity that most fintech reconciliation architectures were never built to provide. Whether the FDIC rule takes effect this year or not, the expectation of transaction-level auditability is becoming the baseline. Companies that get ahead of it will have a competitive advantage; companies that wait will be scrambling.

Five Warning Signs You Have a Confidence Gap

Not every company has a $2 million problem. But most scaling companies have at least two of these five warning signs  and if you recognize more than two, the gap is real.

Your reconciliation runs on a schedule, not on events

End-of-day or twice-a-week reconciliation creates windows where errors compound undetected. Event-triggered reconciliation catches a $500 error on day one. Batch reconciliation discovers a $500,000 problem six months later.

You reconcile in aggregate, not at the transaction level

The $470,000 tax issue described above was invisible at the aggregate level. “Taxes paid” checked out. “Correct taxes paid on correct transactions” did not. If your reconciliation summarizes before it compares, it is designed to miss the errors that matter.

Your reconciliation is siloed by partner or market

The duplicate-debit pattern spanned three countries and multiple acquirers. No single partner’s data showed an anomaly. Only a cross-entity view comparing behavior patterns across all acquirers simultaneously could surface it. If your reconciliation runs in parallel silos that never intersect, cross-entity patterns stay invisible.

Finance cannot configure a new reconciliation flow without engineering

 In the case study above, the company went from six weeks to two weeks to onboard a new payment method once finance could configure flows independently. When reconciliation depends on engineering backlogs, your speed to market is bottlenecked.

A regulatory audit would take you weeks, not hours

If producing a complete transaction-level audit trail is a project rather than a query, your infrastructure lacks the granularity you think it has. As regulators push toward real-time traceability, “we can get it to you by next week” is not going to be an acceptable answer.

Closing the Gap

The companies that close the Confidence Gap share three architectural characteristics;

Transaction-level reconciliation across all cash flows

Not summary batches, not aggregate totals, but every individual transaction matched from payment processor to core ledger, continuously. Simetrik automates this across all inbound and outbound payment flows, reconciling at the transaction level and flagging discrepancies within hours, not weeks. This is how the $470,000 tax phantom was found: by comparing individual transactions against banking records instead of trusting aggregate totals.

Cross-entity pattern detection

Discrepancies that span multiple partners, markets, or acquirers require a unified view of all settlement data. Simetrik’s platform processes 2.5 billion records daily across 50+ countries, enabling the kind of cross-entity anomaly detection that caught the duplicate-debit pattern across three markets. Manual processes and siloed reconciliation tools are structurally incapable of surfacing these patterns.

Finance-owned configuration

When finance teams can build and modify reconciliation flows without waiting for engineering, two things happen: new payment methods get reconciled from day one (instead of six weeks later), and the people closest to the business logic are the ones designing the controls. Simetrik’s no-code platform puts reconciliation configuration in the hands of finance and ops teams, reducing dependency on engineering.

Do you have gaps?

The Synapse collapse proved that a company can process billions in volume, serve millions of customers, and still lose track of tens of millions of dollars because of a visibility failure. Their reconciliation architecture was not designed for the complexity they had grown into.

The $2 million case study proves that even sophisticated processors with real reconciliation processes can have systemic margin leaks hiding in plain sight.

The question is not whether you have a Confidence Gap. The question is whether you will close it before it costs you more than you are comfortable discovering.

Are you ready to see what your current reconciliation is missing? Schedule a 30-minute personalized demo with Simetrik. We will use a secure sandbox environment with either dummy data or your actual payment data to show you live, automated discrepancy detection and the transaction-level audit trails that close the gap.

For years, your fintech has thrived on a simple formula: build a sleek front-end, acquire users rapidly, and let a traditional sponsor bank handle the heavy regulatory lifting behind the scenes. It’s a model that allows startups to move fast and break things without actually breaking federal banking laws.

As transaction volumes scale, those “training wheels” start to look like expensive roadblocks.

Thanks to the passage of the 2025 GENIUS Act and a wave of pro-innovation rulings under OCC Comptroller Jonathan V. Gould, the window for federal charters is wide open. Following the December 2025 conditional approvals for giants like Ripple, Circle, Paxos, and Fidelity, a flood of small-to-medium fintechs and digital asset firms are now eyeing the OCC National Trust Charter.

Securing an OCC Charter is the ultimate graduation for a scaling fintech. It cuts out the sponsor bank middleman and preempts a patchwork of 50 different state licenses. However, federal examiners do not grade on a curve. If your back office still runs on spreadsheets and manual Python scripts, your application is dead on arrival. An OCC charter is the gold standard of financial regulation. Earning one signals to Wall Street, enterprise partners, and retail consumers that your institution is heavily capitalized, strictly regulated, and built to survive economic downturns.

Here is what it takes to get your operations ready for the federal standard.

 The Reality Check: Startup Agility vs. Bank-Grade Compliance

Right now, your finance team probably relies on Excel, Google Sheets, or homegrown Python scripts to match transactions, catch invisible revenue leakage, and run the month-end close. When discrepancies occur, your team manually hunts them down across multiple processor portals.

This is the “good enough” trap. It works when you have 10,000 users, but it breaks when you scale.

The OCC demands “bank-grade safety and soundness.” This means examiners have zero tolerance for operational gaps, delayed settlement tracking, or manual reconciliation errors. You cannot manage billions in fiduciary assets, issue stablecoins, or run a federally regulated payments program using VLOOKUPs. When regulators come knocking, spreadsheets won’t hold up to scrutiny. A proven, auditable system gives you the documentation and traceability to face any regulatory review with confidence. 

The Three Operational Prerequisites for an OCC Charter

To pass an OCC exam, you must prove that your financial controls are automated, continuous, and foolproof. Here are the three operational pillars you must have in place:

Prerequisite 1: Transaction-Level Reconciliation

Traditional financial close tools are balance-level focused. They are not built to handle the complexity of matching 24/7 crypto trades, volatile network gas fees, and card network chargebacks against fiat ACH and wire rails.

• The Standard: You need continuous, automated, transaction-level reconciliation.
• How Simetrik Solves It: Simetrik aligns operations data for all product operations, such as payment processing, ATMs, and other digital products, with accounting records daily, tracking every single dollar from the payment processor to the core ledger. We automate discrepancy detection within 24 hours, preventing the 5 to 15 basis points of invisible margin leakage that usually slips past manual reviews.

Prerequisite 2: Continuous Accounting and ASC 606 Compliance

If your month-end close takes 8 to 12 days because your team is bogged down by manual journal entries and spreadsheet-based revenue recognition, regulators (and investors) will see a massive red flag.

• The Standard: A continuous, highly automated close process that is perpetually audit-ready.
• How Simetrik Solves It: Simetrik automates revenue recognition directly from transaction-level payment data, ensuring strict ASC 606 compliance. By systematically generating journal entries for revenues, fees, and provisions, we reduce manual journal entries by 80%+. The result? A 2-3 day close with zero posting errors.

Prerequisite 3: Automated, Audit-Ready Regulatory Reporting

Compiling FinCEN, BSA/AML, and SOC 2 reports manually takes weeks of engineering and compliance resources, leaving massive room for human error. Under the OCC, these reporting requirements become heavier and more frequent.

• The Standard: Complete transaction-level audit trails that can be generated on demand for federal examiners.
• How Simetrik Solves It: We transform compliance from a manual burden into automated assurance. Simetrik generates FinCEN, AML, and other required regulatory reports directly from your transaction data, maintaining SOC 2-ready controls while reducing your overall compliance costs by up to 60%.

Stop Acting Like a Tech Company, Start Operating Like a Bank

You need Unified Oversight. Executives must have real-time KPIs across all product operations, from cash positions across funding sources to proactive anomaly alerts, with the ability to drill down from a summary dashboard straight to the transaction level. When the OCC (or your board) asks a question about unit economics or funds flow integrity, you must be able to answer it with instant, verified data.Are you preparing your operations for the next stage of scale?

Schedule a 30-minute personalized demo with Simetrik.

We’ll use a secure sandbox environment with either dummy data or your actual payment data to show you live, automated discrepancy detection and the regulatory-grade audit trails required for federal compliance.

When companies scale quickly, the first cracks often appear in back-office processes. Reconciliation is usually the most visible and the most painful.

Teams start each morning inside spreadsheets, pulling CSVs from processors, painstakingly matching transactions, and hoping the numbers line up. Manual workarounds may be sufficient at a small scale, but they turn into structural risk as volumes and partners multiply.

For a fast-growing fintech, reconciliation at scale means comparing transaction-level records across a growing number of processors, banks, ledgers, and partners without losing control of exceptions.

The operating model is straightforward: centralize the source data, normalize it, apply deterministic matching rules, and send the remaining exceptions to analysts with the context they need to act.

This is a familiar story: multiple bank and processor accounts, dozens of payment stakeholders, hundreds of thousands of daily entries, and activity spread across many time zones all contribute to a level of complexity that is hard to rein in. 

At scale, reconciliation must work for everyone. Finance leadership needs speed and reliability, while operations wants transparency and control.

Both teams need one source of truth. This article explains the problems behind manual reconciliation and how a controlled, automation-first approach can turn daily firefighting into a durable operating model.

The cost of spreadsheet-driven reconciliation

Spreadsheets are flexible and fast at the start. At scale, however, they create hidden costs that compound daily.

Instead of one controlled view of transaction and financial data, spreadsheets provide snapshots that diverge as teams copy, filter, and rework them. It becomes difficult to pinpoint what is reconciled, what is pending, and where material risk is concentrated. Context lives in people’s heads and scattered tabs.

In April 2025, the FDIC announced a $150 million civil penalty and a restitution plan of at least $1.225 billion against Discover Bank. A parallel Federal Reserve action imposed another $100 million penalty on Discover Financial Services and DFS Services.

Software would not have resolved the underlying conduct on its own. The case shows why teams need source-level records, controlled classification logic, reviewable changes, and evidence they can reconstruct when a fee or rule is challenged.

The cost of spreadsheet-driven reconciliation is due to several factors:

• Duplication and rework that wastes hours and adds risk
• Limited visibility that stunts leadership’s ability to make decisions
• Lack of clear, immutable audit trails needed for compliance
• Lack of automation, with experienced analysts reduced to data movers

These are not simply inconveniences. They are control failures waiting to surface. And the higher the transaction volumes, the worse these failures will be.

Why volume breaks spreadsheets and how to fix it

The largest PSPs and marketplaces process millions of transactions a day. At that scale, two things matter most. First, you must reconcile at the transaction level, not only by balance. Second, you must do it without forcing humans to wait on a screen.

A scalable workflow ingests compatible data from processors, banks, and internal systems, then normalizes files and APIs into comparable structures. Configured rules handle repeatable matching while analysts work from an exception queue instead of reviewing the full dataset. 

A useful scale test compares the same control at ten thousand and ten million rows. Runtime should remain predictable, material open items should stay visible, and analysts should not have to wait on the full dataset or build intermediate spreadsheets to make the process work. 

A transaction-level reconciliation platform supports this operating model by connecting data preparation, matching, exception handling, and traceability. 

What modern reconciliation should deliver by default

A modern reconciliation platform should provide financial control from transaction record ingestion to traceable reporting, with visible rules, exceptions, owners, and evidence.

In practice, teams need one governed view of accounts, partners, processors, completed controls, pending exceptions, fees, and financial exposure. The dashboard should show both counts and values, because a small number of unresolved transactions can still be material.

A scalable model uses deterministic matching rules for control and AI to assist mapping, transformations, rule suggestions, and exception analysis. Finance and operations retain responsibility for the configuration, approvals, and material decisions. 

When considering a modern reconciliation platform, check for these critical features:

• Configurable matching. Prioritized rules, tolerances, and matching sequences that compare prepared transaction data while retaining the rule behind each result.
• Scalable processing. Processing designed for growing data volumes, source counts, matching sequences, and recurring controls.
• Granular access control. Configurable profiles that give reconcilers, engineers, finance leaders, and other stakeholders appropriate access and visibility. Actions and changes remain traceable within the supported workflow.
• Collaboration across teams. Configured exception and oversight workflows can bring the right teams into the investigation while keeping evidence and status tied to the underlying record.
• A traceable audit trail. Source lineage, rule versions, actions, notes, approvals, timestamps, and user attribution that allow a reviewer to reconstruct the result.
• Controlled reporting and downstream workflows. Dashboards, datasets, exports, and compatible distribution workflows built from reconciled data and documented controls.

Together, these capabilities reduce repetitive work and make control evidence easier to review. They also reduce the engineering dependency created when every new partner, field, or rule requires a custom spreadsheet or code change.

How Simetrik aligns with your operational reality

Simetrik is a financial reconciliation and control platform for teams managing high-volume operations. It combines governed data preparation, configured matching rules, exception workflows, and traceable outputs. AI assists mapping, transformations, rule suggestions, and analysis while the deterministic engine executes the control logic. 

Reconciled data can feed dashboards, reports, and compatible downstream workflows. The same governed output supports operations and finance without another round of spreadsheet assembly.

From data movers to investigators

By adopting an AI reconciliation platform, you allow analysts to take on a more impactful, strategic role. They can see discrepancies and exceptions in their dashboard, along with all the context they need to investigate and resolve the issue.

With Simetrik, exception views can include the fields needed for investigation: counterparty, flow type, currency, amount, references, prior attempts, and related items. Configured conditions can route ownership and notify support or payments teams when their input is needed.

Analysts spend their day resolving, not assembling. They close items, add root cause codes, and propose rule changes when patterns emerge. Managers see throughput, backlog age, and the value at risk by team and partner. Leadership gets a clear view of operational health and financial exposure, not a stack of spreadsheets.

Reporting that’s ready to share and act on

Many teams still struggle with reporting at the end of a period. Missing evidence, manual data collection, and incomplete source information delay review and force people to rebuild the same analysis.

Internal protocols and external reporting requirements add more preparation. Teams need documentation that reflects their own controls and can be traced back to the underlying records.

AI Copilot Datasets can help users create reusable queries or datasets from natural-language instructions, such as identifying which partners drive the most exceptions or which accounts close late. Teams review those outputs and use them in dashboards or reporting workflows configured for their own requirements.

Dashboards and reports can remain drillable to the underlying transactions. Finance gets the context it needs while operations avoids rebuilding the same pivot table at every period end.

Integrations that keep systems aligned

Fast-growing companies often implement accounting systems on a separate track from operations. Reconciliation then becomes a bridge between external movement and internal books. That bridge needs to be stable.

Through compatible integration methods, Simetrik can ingest financial and operational data from banks, processors, ledgers, data warehouses, and internal systems. Cleared positions or exception summaries can then move into supported downstream workflows.

If an ERP rollout is still underway, structured exports can bridge the current process while the team establishes more consistent data and traceability.

What the first 90 days look like

Teams often ask how to move from spreadsheets to a controlled operating model without disrupting the close. The following 90-day sequence is an evaluation framework, not a universal implementation promise.

The first month tests source readiness, matching coverage, and ownership. Later phases should prove that the workflow can expand without losing runtime visibility, traceability, or control.

Finding ROI: seeing fast outcomes on Simetrik

Use the first few reconciliation cycles to establish a baseline. Compare runtime, match coverage, unmatched value, backlog age, manual touches, and reporting effort before expanding the workflow.

The ROI case should be measured through outcomes such as:

• Faster cycle times. Measure the time from source arrival to a documented reconciliation result, including time spent resolving material exceptions.
• Smaller backlogs. Track the count, value, age, and owner of unresolved items instead of relying on rows copied between files.
• Increased capacity. Measure how much analyst time moves from collecting and matching data to investigating exceptions and fixing recurring causes.
• Improved visibility. Give leaders a view of financial exposure by value, age, partner, and flow, with traceability for exceptions and adjustments.
• Stronger control. Review whether actions, rule changes, approvals, and evidence can be reconstructed without assembling a separate audit file.

A credible business case connects these measures to the cost of engineering changes, manual effort, delayed investigation, and fragmented reporting.

Simetrik brings data preparation, deterministic matching, exception management, and traceability into one controlled workflow. To evaluate the approach with your own high-volume reconciliation, request a demo.

Payments increasingly move in near real time across banks, payment service providers, card networks, platforms, and internal systems. Each participant can record a different amount, timestamp, fee, or settlement status, making liquidity visibility harder to maintain.

This complexity greatly complicates the role of treasury and finance teams. Variance among currencies, settlement times, and data management protocols makes it hard to track liquidity and accurately apply cash at scale, often leading to millions of unaccounted-for dollars a year. 

How can the office of the CFO tackle liquidity and forecasting challenges when money moves faster than it ever has? Keep reading for the answer – and a look into the emerging world of AI reconciliation.

The short answer: reconcile transaction, settlement, bank, and internal records frequently enough to identify what has cleared, what is pending, and where exceptions remain. Reconciliation improves the data used for liquidity decisions, but it complements rather than replaces a treasury management system, cash forecasting, or funding execution.

What’s so hard about liquidity management?

Treasury and finance teams used to be able to rely on batch settlement data to understand their cash positions. With the proliferation of flexible payment methods and new payment services, complexity grew. 

The addition of real-time payment (RTP) networks brings even more nuance and risk to the market. These new payment rails give businesses faster ways to handle payroll, pay out merchants, and manage AR/AP – they also tend to push internal reconciliation and finance teams to the limit.

The challenges to managing liquidity effectively today include:

• Funds come and go many times throughout the day. Without a continuous tracking system, finance teams can’t be sure enough cash is available at any given point.
• Settlement times vary and don’t always coincide with transaction time stamps. Bank balances may be off by thousands of dollars, leading to poor funding decisions, unplanned fees, and unnecessary panic. 
• Batch payments don’t map back to individual transactions. This adds hours of manual reconciliation to ensure cash and fees are applied correctly.
• Discrepancies and errors slow reconciliation and make it hard to calculate cash on hand. 

Move too cautiously, and the opportunity cost is steep. Cash sits idle in accounts instead of being deployed to fund growth or reduce short-term borrowing. Move too aggressively, and you risk the opposite problem.

Underfunded RTP accounts, for example, can break funding agreements with sponsor banks – the very relationships that make instant payments possible. Without timely reconciliation, teams may not realize how quickly real-time payouts are draining balances until transactions start failing.

Cross-border settlements introduce another layer of complexity. Funds often pass through multiple correspondent banks and FX providers before reaching their destination. When timing or conversion delays aren’t captured immediately, treasury teams can mistake in-transit balances for available cash, exposing the business to liquidity gaps and currency risk.

Merchant payouts add risk, too, as marketplaces and retailers often pay merchants ahead of card network settlement. Without clear visibility into pending inflows, finance teams may overextend working capital, effectively funding payouts with money that hasn’t yet arrived.

To accurately monitor liquidity and maintain a clear line of sight into balances, AR, and AP across  your entire payments ecosystem, look no further than reconciliation. 

The role of reconciliation in understanding liquidity 

Reconciliation helps finance teams manage liquidity and cash flows by maintaining accuracy and financial integrity at every step of every transaction.

At the most granular level, the reconciliation process validates and matches transaction records across all parties. At a higher level, it ensures operational balances accurately reflect the reality of the company’s cash position.

Once reconciled, the data can be used to make all kinds of decisions about liquidity: whether to acquire more funding, what payment terms to offer or accept, how to allocate working capital, and many more. 

But while AI and automation have transformed other key enterprise workflows, too many companies have yet to invest in modern reconciliation solutions that can handle the volume and complexity associated with real-time, global payments today.

The problem with legacy financial platforms

Many finance teams still rely on manual entries, spreadsheets, and brittle systems that can’t paint a real-time picture of cash flows and liquidity. Even with a strong treasury management system (TMS), the gap between balances shown in banking systems or financial analytics tools vs the reality of minute-by-minute transactional data leads to substantial discrepancies and excess costs.

Legacy, manual solutions aren’t just slower, they suffer from poor interoperability – too many disparate data formats, currencies, and protocols muddy visibility and hinder finance teams’ ability to use reconciled data for critical decision-making around liquidity. 

Fragmented tech stacks also force engineering to spend time on costly integrations and workarounds, rather than working on initiatives that will eventually drive revenue. The bottom line? It’s expensive to stick to the status quo.

How transaction reconciliation improves liquidity visibility

AI-assisted reconciliation helps finance teams improve the accuracy and timeliness of data used for working-capital and funding decisions. Modern platforms apply deterministic matching rules at scale, surface exceptions, and produce traceable dashboards and reports for treasury and finance teams. 

These newer, AI-enhanced systems are designed to handle exactly the kind of high-volume, complicated transaction data that enterprise finance teams struggle with. Incoming and outgoing payment records can be validated and matched across PSPs, real-time payment rails, card networks, banks, and internal systems, with exceptions routed for review. 

Unlike legacy and manual solutions, AI reconciliation centralizes all of your transaction and finance operations data into a single source of truth, standardizing records and identifying gaps so operational balance views can be refreshed as new data arrives. This gives teams a more reliable view of cleared, pending, and unmatched activity that can inform prefunding decisions on real-time payment rails. 

Treasury and finance decision-makers can log into their TMS, business intelligence tools, ERP, or any other connected system and work from the same reconciled dataset, reducing uncertainty in liquidity analysis.

Strengthen liquidity with complete visibility and control on Simetrik

Simetrik is an AI-assisted reconciliation platform for complex finance operations where liquidity decisions depend on timely, traceable transaction data. 

The platform ingests transaction data from any source and system, standardizing it and applying configurable, deterministic matching logic. Reconciled outputs and exception data can then feed connected ERPs, treasury systems, and business intelligence tools.

This level of automation and efficiency lets your finance team spend less time on repetitive matching and more time reviewing exceptions and supporting forecasting. Depending on the workflows and data sources configured, teams can use Simetrik to:

• Compare operational balances with transaction and settlement data
• Include prepayments and credits in reconciled balance views
• Batch payouts and bulk payments from PSPs are automatically disambiguated
• Surface aging receivables for follow-up in connected workflows
• Fraud and risk models are improved with trusted, reconciled data
• Detect errors and route discrepancies for governed resolution
• Feed reconciled data into the TMS and ERP for liquidity analysis

Simetrik helps finance and treasury teams transform their operations while maintaining global governance and control. To learn more about how our AI reconciliation platform improves liquidity and drives profitable decision-making, explore our solutions or get in touch with us to schedule a demo.

If you work in card payments, you know the feeling. A network file lands at 3:07 a.m., a processor export arrives at 3:12 a.m., and by 9:00 a.m. the operations team is trying to explain why a handful of transactions do not line up. Meanwhile, compliance wants sub-merchant chargeback trends and engineering is preparing another rule change and redeploy.

Payments reconciliation is the process of comparing transaction records across processors, card networks, banks, and internal systems to confirm that amounts, references, fees, refunds, and settlements agree. Automation brings those records into one controlled workflow, applies matching rules, and routes exceptions for review.

This matters most in high-volume payment operations, where the same transaction can arrive with different identifiers, dates, formats, or statuses. A processor file may land overnight, but operations still needs to explain why a subset of records does not line up the next morning.

This guide explains how to automate payments reconciliation from data ingestion and normalization through matching, exception analysis, alerts, and traceable reporting. The working example compares processor records with network settlement data, but the same pattern applies when several processors need to be reconciled against one ledger or settlement view.

Why payments reconciliation breaks at scale

• Fragmented data: Networks, processors, gateways, banks, internal ledgers, ERPs, settlement files, and dispute feeds each have their own identifiers, formats, timing, and lifecycle.
• Frequent change: Networks revise specifications, partners add columns, merchants expand cross-border, and teams launch new payment products. A code-only workflow can struggle to keep pace.
• Engineering bottlenecks: When matching logic lives entirely in code, a small adjustment can require a ticket, a pull request, testing, a redeploy, and cross-team coordination.
• Limited visibility: Finance and operations may know the totals but still need to trace root causes, fee variances, or sub-merchant chargeback trends through SQL, email threads, and several systems.

Five building blocks of automated payments reconciliation

1. Ingest source data

Start with the records that describe each stage of the payment lifecycle: processor exports, network settlement packages, bank statements, dispute feeds, and internal ledger data. Preserve the source context and original structure so every output can be traced back to the record that produced it.

Simetrik can ingest financial and operational data through compatible methods such as SFTP, email, APIs, or secure access workflows.

When a PDF or flat file contains 30 tables, the tabular sections can remain distinct Sources while retaining the original structure for audit. The objective is a governed input layer without brittle preprocessing scripts or staging jobs.

2. Normalize records across sources

Different systems use different column names, date formats, signs, currencies, and reference fields. A processor may add Ref2 beside Ref1 or insert a new field without changing the underlying transaction. Normalization makes those records comparable before matching begins.

Source Unions map compatible partner files into a consolidated schema. If Visa changes a column name or a processor inserts a field, the team can update the mapping while preserving historical context.

Dozens or hundreds of compatible partner sources can feed a canonical view for comparison with an internal ledger or network settlement data. AI-assisted mapping can suggest relationships between columns, while the team reviews the configuration and its downstream dependencies.

Reconciliation should not break because someone added Ref2 next to Ref1. Normalization is an operational capability, not a one-off cleanup project.

3. Apply configurable matching rules

Start strict with amount, currency, multiple references, payment method, and an approved settlement-date tolerance such as T+1. Then add priority-based fallback logic for a secondary reference parsed from a longer string, known timing differences, minor rounding, or processor-specific behavior.

Some customer configurations use Rule Sets in the triple digits to cover the long tail. Each match remains tied to the Rule Set that produced it.

Simetrik applies configured rules deterministically: the same data and configuration produce the same control result. AI can assist with suggestions, but the team remains responsible for validating the logic.

Operations should not need an engineering ticket for every documented rule change. Moving that configuration out of a code-only workflow avoids a pull request and redeploy while preserving ownership, history, and review.

4. Analyze matches and exceptions

Each run should separate matched records from exceptions and make the remaining work easy to investigate. Useful views include:

• Match rate and unmatched value for the current run.
• Exceptions by processor, payment method, merchant, or geography.
• The rule set that matched each record.
• Fee, foreign exchange, and settlement variances.
• Chargeback activity linked to the underlying transaction.

Simetrik’s Operations Center can bring reconciliation results, anomalies, and pending items into one environment so teams can investigate and document exceptions with their supporting context.

Reconciliation is not only about making two files equal. Teams need to move from transaction-level evidence to executive summaries, then back to the exact row and Rule Set that explains a result.

5. Monitor controls and completeness

Configured alarms can flag an expected file that did not arrive, a row-volume change, a falling match rate, a chargeback spike, a fee variance, or a growing exception backlog. Thresholds should reflect materiality: three unreconciled transactions totaling $250,000 may deserve attention before 300 records totaling $12.60. Track amounts, not only counts.

Simetrik can monitor configured conditions, alert teams to signals that need attention, and bring related work into its unified oversight and alerts layer.

How to automate a processor-to-network reconciliation

A processor-to-network reconciliation usually follows six controlled steps.

Step 1: Collect the records

• Acquirer or processor transaction exports, delivered through the agreed workflow.
• Visa or Mastercard network settlement packages, kept distinct when their lifecycle requires it.
• Optional bank statement, refund, dispute, or chargeback data.

Set a clear owner and expected delivery cadence for each source. A reconciliation cannot be complete if one of its required inputs is missing.

Step 2: Normalize the source data

• Map processor fields into a common structure for dates, amounts, currencies, statuses, and multiple references such as Ref1, Ref2, and Ref3.
• Keep network records separate when their structure or lifecycle requires a different preparation path.

Step 3: Enrich the fields needed for matching

Add calculated or standardized fields only when they help compare the records. Examples include normalized timestamps, fee-per-transaction calculations, foreign exchange conversions, or a fallback reference extracted from a longer entry string. Document every transformation so it can be reviewed later.

Step 4: Define matching logic

• Strict rule: Match amount, currency, primary reference, payment method, and an approved settlement-date tolerance.
• Fallback rule: Use a secondary reference or a wider timing tolerance for known operational differences.
• Edge-case rule: Apply specific logic for gateway quirks, cross-border FX rounding, partial captures, refunds, or other understood lifecycle differences.

Run rule sets in priority order and retain the rule that matched each pair. That record makes later review much easier.

Step 5: Review exceptions

• Group unmatched records by cause, source, value, and age, then expose the exact failure reason, such as a missing reference, amount variance, or settlement drift beyond tolerance.
• Assign an owner, evidence, and next action to material exceptions.

Step 6: Act and distribute results

• Trigger configured alerts when a control crosses its threshold.
• Export or distribute daily summaries, settlement confirmations, fee breakdowns, and other reconciled data through compatible reporting workflows.
• Retain the original sources, transformations, rules, user actions, and timestamps needed to reconstruct the result.

How chargebacks fit into payments reconciliation

A chargeback should connect back to the original payment, the processor or network notification, any financial debit or fee, the settlement result, and the internal ledger.

When those records live in separate systems, teams can miss duplicate debits, unmatched fees, a recovery that never appears in settlement, or a chargeback deduction with no underlying transaction match.

• Validate that each dispute is linked to a valid transaction and the expected financial movement.
• Track both counts and amounts by sub-merchant, brand, country, BIN, processor, and the other operational dimensions that matter to the team. A small count can still represent a material financial exposure.

Simetrik can compare compatible sources, follow exceptions, and verify the financial impact of a result. Its claims and chargebacks workflow focuses on transaction-level control across that lifecycle.

Performance and scale: what enterprise buyers should test

A natural evaluation question is: what happens when a million transactions run through dozens of Rule Sets? The answer has to cover more than raw throughput. Buyers should test whether runtime stays predictable as volumes grow, sources change, and new edge cases are added.

Simetrik customer environments can reach hundreds of millions of records per day across multiple reconciliations, with execution measured in minutes. Matching is parallelized, transformations compile efficiently, and Source Unions reduce the rework created by one-off pipelines.

The practical test is whether teams can add Rule Sets for gateway quirks, settlement drift, FX rounding, partial captures, and refunds without pushing a nightly run into the next business day. Track daily volume, Rule Set count, end-to-end runtime, peak-load behavior, and the cost of adding each new exception pattern.

Metrics for automated payments reconciliation

A useful dashboard should show whether the process is complete, accurate, timely, and explainable. Track:

• Match rate and value coverage: The share and value of records matched by the configured rules.
• Unmatched count and value: The volume and financial materiality of the remaining exceptions.
• Exception aging: How long unmatched items remain unresolved.
• Time to resolution: The elapsed time from detection to a documented outcome.
• Fee and FX variance: Differences between expected and recorded deductions or conversions.
• Source completeness: Whether every required file or dataset arrived for the run.
• Manual intervention rate: The share of records that required judgment or adjustment.

Targets should reflect materiality, source quality, and the risks of the workflow. In mature implementations, teams using this approach have reported daily match rates above 99.95%, with the remaining exceptions explainable and trending down. Treat that as a benchmark to validate against your own data, not as a universal guarantee.

Build vs. buy: what to evaluate

An internal solution can fit a narrow, stable workflow with a strong technical owner. The honest comparison begins when sources, entities, rules, and exceptions keep changing. Evaluate time to adapt, engineering dependency, long-tail coverage, auditability, and opportunity cost, not only the first version of the matcher.

Long-tail coverage is where the economics often change. In the in-house programs observed by Simetrik, initial automation has reached roughly 60% coverage before edge cases began consuming disproportionate engineering time.

The relevant comparison is not only license cost versus infrastructure. It also includes sprint time, support, key-person risk, audit controls, and the opportunity cost of taking engineers away from customer-facing or risk work.

Neither option is automatically better. A platform becomes more compelling when change is frequent, exception logic expands, and operations needs direct control without giving up auditability. That is the point at which Simetrik’s configurable rules, Source Unions, controls, and transaction-level traceability address more than the matching step.

A practical pilot plan

A six-week pilot can provide a useful evaluation window when the sources, owners, and scope are ready. This is an outline for testing coverage, control, and runtime against a known baseline, not a universal implementation promise.

1. Week 0–1, connections: Set up compatible delivery for processor, network, and optional bank or chargeback data. Confirm owners and expected arrival times.
2. Week 1–2, normalization: Build the Source Union or equivalent canonical views. Add the three to five enrichment fields needed for fees, FX, timestamps, or parsed references.
3. Week 2–3, first reconciliation: Configure three to five prioritized Rule Sets and test them against historical data, including known exceptions.
4. Week 3–4, exceptions and dashboards: Create root-cause views, define owners and evidence, add chargeback or sub-merchant views where relevant, and configure alerts.
5. Week 4–5, iteration: Add long-tail Rule Sets for documented edge cases and validate the reports or exports the receiving teams require.
6. Week 6, review: Compare runtime, match rate, unmatched value, exception aging, and manual effort with the baseline before expanding to more processors, networks, or bank settlement workflows.

Make reconciliation predictable

Reconciliation will never be glamorous, but it should be predictable, explainable, and fast. A partner changing a column on Thursday should be a controlled mapping update, not a post-mortem.

Automated payments reconciliation works when complete source data, documented transformations, deterministic rules, visible exceptions, and an audit trail make every result reconstructable while human review remains where judgment is required.

Simetrik brings data preparation, deterministic matching, exception management, and traceability into one transaction-level reconciliation workflow. To test it with your own processor, network, bank, or chargeback data, request a demo.

By 2029, global payment revenues are expected to reach $2.4 trillion. That’s an increase of over 25% from today’s figures, spread across a fragmented landscape of banks, card schemes, clearinghouses, payment processors, and other payments stakeholders.

Real-time payment rails have added a new level of complexity and opportunity to the landscape. These networks make it possible to move money between participating institutions in seconds, often with 24/7 availability. Payments service providers (PSPs) are embracing instant payments to stay competitive, but the move doesn’t come without risk.

As instant payments scale, banks and PSPs face five connected challenges: sponsor bank and third-party dependencies, 24/7 liquidity, fragmented data and reconciliation, fraud exposure, and evolving compliance requirements. For PSPs scaling real-time payments for lending platforms, fintech startups, merchants, payroll providers, and digital wealth products, these challenges align with Federal Reserve guidance on fraud, liquidity, compliance, and third-party risk.

As customers demand more immediacy and convenience, PSPs must closely examine their technology investments and financial partners to optimize the benefits of real-time payments. As you plan for scale at your own organization, make reducing complexity a top priority for your finance team.  

The challenges of scaling instant payments

Real-time payments haven’t been around for long, but they’ve transformed the industry. The Unified Payments Interface (UPI) in India launched in 2016, the US-based Clearing House’s RTP Network launched in 2017, and Brazil’s Pix went live in 2020. Alongside counterparts in more than 70 countries, these networks now process trillions of dollars in real-time payment volume each year.

These rapidly adopted payment rails enable PSPs to offer faster, more convenient ways for merchants to accept customer payments. From a user perspective it’s easy to make instant payments on these platforms, but behind the scenes there are many moving parts that often eat into margins and cause regulatory confusion.

Let’s dive into each of them.

The sponsor bank relationship: a delicate balancing act 

Finding the right sponsor bank is key to your long-term success as a US PSP. To select the best partner, ensure agreements are structured to optimize outcomes on both sides. 

First, consider which regions you operate in now or plan to in the future. For PSPs scaling cross-border payments, partnering with banks that already connect to multiple RTP networks can reduce integration complexity and make expansion easier. Talk through how quickly you can enable payments via networks like the EU’s SEPA Instant Credit Transfer (SCT Inst), India’s UPI, and Singapore’s FAST, even if you’re not currently in those regions. 

Next, examine the bank’s technology. Make sure they have robust infrastructure and integrations in place to support a large volume of real-time transactions on your platform without disruption. Ask about their adoption of shared standards like Swift GPI and ISO 20022 XML V9 to understand the reconciliation roadblocks you may encounter. Check for strong risk management and liquidity guardrails that keep money moving even if volumes spike unexpectedly.

Then discuss the bank’s available funding models, including the pros and cons of each and how much flexibility you’ll have to move among them. Banks and credit unions typically offer one or more of the following modes:

• Pre-funding agreements – PSPs deposit a certain amount at the sponsor bank to cover transactions up to that balance. This is a simple, low-risk option for the bank but requires you to tie up a significant amount of working capital and may pause transactions if the account falls below its threshold. However, variations on these models offer more flexibility, like just-in-time top-ups or hybrid pre-funding/credit models that keep transactions moving if volumes surpass the agreed threshold.
• Intraday sweeps – The sponsor bank monitors RTP volume and sweeps funds from PSP accounts throughout the day. This option reduces the need for large prefunding deposits but requires constant reconciliation and clear visibility into liquidity to be effective. If you’re still using legacy finance systems or struggling to reconcile payments at the transaction level, this model might not be feasible.
• Credit lines – The sponsor bank extends credit to PSPs, funding RTP activity to a certain limit and interest rate. Batch settlements occur via ACH daily, weekly, or monthly, complicating the reconciliation process but freeing up significant capital for the PSP. 

Selecting the right sponsor banks and funding models is important, but in reality even the best partnerships can’t solve all of the big-picture challenges around reconciliation, liquidity management, and revenue leakage in today’s instant payment ecosystem.

The reconciliation challenge

Reconciliation, or the process of validating and matching transactions to ensure financial integrity, becomes nearly impossible at the volume and complexity described above. The sheer number of partnerships, regulatory bodies, consortiums, and technologies needed to support instant payments across traditional and emerging systems calls for an entirely new approach to reconciling and managing transaction data.   

Even at the enterprise level, many finance teams are still using spreadsheets and semi-manual processes to check each transaction against bulk settlements, fee deductions, and other activity along the payment journey. This gap between sending and receiving real-time payments and clearly tracking their business impact leads to major issues: revenue leakage, excess spending, and painfully drawn-out audits and close cycles. 

To reconcile instant payments accurately and cost-effectively, PSPs must have a clear view of the entire RTP value chain in one place. But because these networks are fairly new and run by disparate government and hybrid public/private entities, there is still limited interoperability between them. 

The effect of real-time payments on liquidity

PSPs that don’t address these challenges will find that instant payments hinder their liquidity and financial health. Lack of visibility at the transaction level leads to poor working capital management and blind decisions around financing, while losses due to reconciliation errors, fraud, and missed collections directly eat into available cash. 

Paired with the right partners and technology, however, real-time payments boost your financial resilience and help you retain customers in a crowded market. Modern reconciliation platforms automate your finance team’s most error-prone, inefficient workflows, making it possible to process and reconcile millions of real-time transactions each day.  

Reconciliation also makes it easier to optimize your agreements with sponsor banks. Keeping a healthy balance in the accounts connected to RTP rails is key for any pre-funding, sweeps, or hybrid model, and having full visibility into the details of past transactions helps predict future demand so you always have appropriate funding in place.

Once reconciliation isn’t a blocker, instant payments become a strength. Instead of worrying about a sudden lack of working capital, you can focus on providing cutting-edge services to your customers. 

Simetrik: AI reconciliation for the instant payment era

Simetrik is a comprehensive transaction reconciliation platform that unifies data across every system involved in real-time payments, automates multi-way matching, and exposes exceptions before they become a problem.

PSPs rely on Simetrik to achieve new levels of scale and auditability across four distinct areas:

• Cost control and fee management – Expose, validate, and optimize all of the fees associated with RTP rails.
• Cash position and liquidity – Accelerate settlement, reduce working capital drag, and improve forecasting and visibility.
• Regulatory reporting – Reduce risk and automate reporting across SOX, PCI, GLBA, and other obligations.
• Cross-border and crypto transactions – Tame FX, network variance, and on/off-ramp complexity with reliable, scalable reconciliation.

By 2028, real-time payments will account for over a quarter of global electronic payments. To learn how Simetrik streamlines RTP complexity and turns this fast-evolving market into a strategic profit lever, request a demo of our platform.

Acquirers play a pivotal role in the modern financial ecosystem, facilitating card payments and disbursing the right amount to merchants with every purchase. But each transaction is a potential point of failure that’s subject to risk from errors, disputes, and revenue leakage.

How can acquirers reduce manual reconciliation? By standardizing scheme files, settlement records, bank statements, and internal transaction data, then applying reusable matching and fee-validation rules. Preconfigured workflows shorten setup time while keeping exceptions and rule changes visible to finance teams.

To handle high transaction volumes and maintain your reputation in a fragmented payments market, you must reliably automate reconciliation and reporting—starting with activity related to the card networks that make up a major portion of your transactions.

Simetrik’s preconfigured Visa and Mastercard solutions make this possible without exorbitant costs or a heavy engineering lift. It’s designed to help you set up best-practice reconciliation workflows with no code, guided by an intelligent setup assistant and a dedicated onboarding team, and maintain full visibility into the inner workings of your rulesets and logic as they evolve.

We’ve connected nuanced capabilities and financial controls to enable six key use cases, from authorization to dispute management, in a way that’s easy to deploy and adopt.

Why should acquirers automate reconciliation?

Monitoring and reconciling card network transactions is critical to your success as an acquirer. Visa and Mastercard not only make up a large portion of total transactions, they also each have their own set of requirements for acquirers, including specific audit frameworks and scheme fees that must be tracked meticulously.

Simetrik automates reconciliation down to the transaction level, matching records across internal and external systems and generating traceable outputs for merchants, auditors, and other stakeholders. Reporting workflows can be configured to support relevant Visa and Mastercard data requirements. Instead of managing spreadsheets and scrambling to organize data for network reports like the QMR (Mastercard Quarterly Member Report) and GOC (Visa Global Operating Certificate), you can focus on quickly remediating errors and disputes.

Once data is reconciled and verified, it can be used by your team and your merchants to make better operational decisions, from forecasting to fee strategies.

5 reasons to automate Visa and Mastercard reconciliation:

• Improved cash-flow visibility
• Better forecasting inputs
• Efficient fee management
• Faster dispute resolution
• Stronger risk management

6 key functionalities you can enable on Simetrik

Simetrik connects acquirer-supplied scheme files, bank statements, and records from internal systems like your ERP, automating six key reconciliation use cases for Visa and Mastercard transactions:

• Funding – Validate programmed versus received bank receipts, investigating partials or delays to ensure compliance and timely execution.
• Authorization – Ensure control and reduce fraud by reconciling authorized requests and responses with network data and matching clearing files to issuer records.
• Clearing – Validate scheme fees, interchange fees, and network adjustments against acquirer records to prevent overbilling and missed charges.
• Disputes – Confirm funds debited from issuers, reconcile pre-arbitration and arbitration cases, and help merchants manage evidence.
• Settlement – Calculate daily net settlement and validate receipts against bank statements to improve settlement and funding visibility.
• Agenda – Split domestic and international agendas, with or without deferment, validating expected financial movements to prevent loss from delayed or missing settlements.

Minimize risk with fast, guided deployment

Simetrik leverages a dedicated onboarding team alongside AI-enhanced setup flows to help you quickly improve reconciliation and reporting. The platform reduces repetitive manual handling while keeping users in control of final outputs through governed workflows and visible reconciliation logic.

With Simetrik, you don’t have to worry about managing a tough implementation alone. Our Visa and Mastercard solutions can be delivered in weeks, not months, with help from a dedicated onboarding team and an intelligent, guided setup assistant.

Simetrik customers can also choose from prebuilt, customizable dashboards that highlight the metrics acquirers care about the most—out-of-the-box KPIs with drill-down capability and AI-driven recommendations. 

Get started with a personalized demo

Want to see Simetrik’s card network solutions in action? Request a personalized demo to explore modular workflows for authorization, clearing, settlement, fees, and disputes.

Learn more about our Visa and Mastercard solutions.

When you think about resilient finance operations, what comes to mind? 

Accurate numbers, limited risk exposure, data-driven decisions, and well-controlled costs—all of these are made possible through the process of reconciliation.

Reconciliation maturity describes how consistently a finance operations team can connect data, apply repeatable matching rules, investigate exceptions, and produce traceable outputs at scale. This five-stage model helps teams identify their current level and the controls needed to advance.

Today, however, the reconciliation process is still plagued by outdated technology and manual number-crunching. It’s tedious, rife with errors (as seen in recent public cases where mistakes have cost companies millions), and unscalable in a market where complexity seems to grow by the minute. 

It’s time to adapt. In this guide, we’ll take a look at a model for reconciliation maturity that will guide you toward fully automated, AI-enabled reconciliation at every level. From siloed, costly operations to expertly orchestrated workflows with clear ROI, each advancement reduces cost and drives efficiency across the organization.

5 factors making reconciliation painful:

1. Massive transaction volumes involving many fintech, payments, and banking partners
2. Highly complex, nonstandardized remittance data in varied formats
3. Stringent, inconsistent regulatory requirements across many regions and industries
4. A fast-growing market that regularly requires new partnerships, integrations, and workflows 
5. The en masse adoption of AI that requires real-time, trusted data to operate

Why reconciliation maturity matters now

Until recently, the standard response to reconciliation complexity was to add headcount, boost engineering spend, or accept a baseline level of revenue leakage, audit fees, and costly errors. 

But the modern financial ecosystem has reached an inflection point that makes this path impossible to stay on. The last decade has seen sweeping changes in global regulations, an explosion of payment companies and offerings, open banking standards, and an AI boom that reset the bar for real-time data access and deep interoperability.

Legacy reconciliation solutions can’t meet the demands of today’s market, always one step behind and far too unreliable for the speed and volume of modern money movement. Miss out now, and you’ll face increasingly prohibitive costs as you try to keep up with competitors who’ve adopted  scalable, AI-ready reconciliation platforms.

The great enabler: the AI reconciliation platform

To reach full reconciliation maturity, companies need technology that can handle enterprise complexity. While it’s possible to make it past the first stage of reconciliation maturity using point solutions or home-grown scripts, you won’t get much further without a unified platform for all of your reconciliation data, controls, and workflows.

AI reconciliation platforms help you integrate internal and external transaction data sources, connect internal systems like your ERP or core banking system, and deploy sophisticated governance and automation in a way that doesn’t plunge you into technical debt as regulations and business needs change.

The best reconciliation platforms do more than just reconcile multi-way transactions. They combine deterministic matching rules with AI-assisted configuration and analysis across three distinct levels:

• Transaction level – Matching individual transactions to create a reliable source of truth for all downstream processes, detecting anomalies and discrepancies in real time.
• Financial level – Using reconciled data to manage cash flow and control costs, supporting strategic decisions, and reporting accurately to internal and external auditors.
• Accounting level – Aligning transaction and financial data with journal entries to ensure compliant, accurate reporting and accelerate close cycles.

In the next section, we’ll show you how adopting an AI reconciliation platform is essential to increasing the maturity of your finance operations. There are many paths to success—keep reading to explore ways to advance that fit your unique use case, resources, and goals.

What can an AI reconciliation platform do?

• Process massive transaction volumes with low latency at T+1 speed 
• Standardize and match multi-platform, highly varied transaction data
• Support controls and reporting for varied regulatory requirements across regions and industries 
• Keep TCO manageable with no-code, scalable deployment and iteration
• Support changing requirements and higher volumes with controlled workflow updates

An actionable model for reconciliation maturity

Fully mature, AI-optimized reconciliation drives value beyond just reporting the numbers. It gives you trusted, real-time financial data to use in any downstream application you can dream up. 

The Simetrik reconciliation maturity model consists of five stages, starting with completely manual finance operations and ending with transformative, continuously improving reconciliation automation. From instant customer refunds and nuanced loyalty programs to advanced, predictive FP&A modeling, getting to Stage 5 will put you at a huge competitive advantage.

Stage 1: Slow & siloed 

At the first stage, reconciliation is almost entirely manual. Minimal data integration, no scalability, and a lack of confidence in the numbers hurts the business.

• Data management: Spreadsheets and macros are the finance team’s primary tools, and siloed transaction data must be exported, validated, and entered correctly into backend systems. 
• Development: Any attempt at integration or automation is reliant on engineering, so progress is slow and costly. There’s no path to leveraging AI or using transaction data in decisioning models. 
• Exception handling:  Finance teams manually check for discrepancies and fraud to a limited degree of success. When found, outreach and remediation can take weeks.
• Financial oversight: Little insight into current operational balances makes decision-making difficult. Transaction data often doesn’t match bank statements, bank balances, revenue forecasts, and other indicators of financial health.
• Accounting integrity: Transactional data is not aligned with operational balances or ledgers, slowing the financial close and often resulting in reporting errors. 
• Risk: Risk is at a critical level, with many unexpected, unexplained losses and siloed internal knowledge. If a key employee leaves, they take the understanding of reconciliation logic with them.
• Compliance: Without audit trails, data must be compiled manually. Audits are costly and drawn out, and compliance gaps can lead to fees and reputational harm.

Stage 2: Limited automation

Benefits start to materialize at stage two, but reconciliation still isn’t scalable. With many data sources not yet integrated, AI adoption is not yet possible.

• Data management: Some integrations are in place, but transaction data remains fragmented.  Manual data exports and spreadsheets are still common. 
• Development: Automations are homegrown or piecemeal and rely on constantly changing logic and data sources. Engineering costs to add new integrations are high.
• Exception handling: Anomaly detection and transaction matching is automated for some sources, lightening the burden of exception management. Remediation is still entirely manual.
• Financial oversight: Increased visibility into transaction data helps with financial modeling and performance tracking, but isn’t enough to inform major decisions or drive AI innovation. 
• Accounting integrity: Transactional data is still not aligned with general and sub-ledgers. Close cycles are long, and reporting is error-prone.  
• Risk: Risk is still high, but the number of unexplainable losses starts to drop. Siloed internal knowledge is still a problem, with many black boxes across teams and information held by a select few team members.
• Compliance: Rev rec and reporting for standards like ASC 606 and IFRS 15 is manual and lacks complete audit trails. Internal audits can take months due to visibility gaps.

Stage 3: Full transactional coverage 

At this stage, transaction-level reconciliation is fully automated, scalable, and prepared for financial reporting and downstream innovation. Accounting-level reconciliation remains manual.  

• Data management: All transaction records are integrated, standardized, and automatically reconciled. Finance teams have real-time visibility into granular transaction details and discrepancies.
• Development: No-code tools and AI agents accelerate time to value and alleviate reliance on engineering as new workflows are needed. Anyone with permission can adapt the rulesets and business logic that power automated reconciliation and reporting. 
• Exception handling: With the day-to-day reconciliation fully automated, team members can focus on exception handling. As discrepancies, chargebacks, disputes, and potential fraud are detected in real-time transaction streams, alerts tell the FinOps team it’s time for remediation.  
•  Financial oversight: Transaction data is occasionally reconciled against operational balances in the ERP, but records quickly outdated. Finance operations and accounting teams must manually compare ERP data with transactions for a true picture of the company’s finances. 
• Accounting integrity: Accounting-level reconciliation is still manual. Transaction data doesn’t always match the GL and subledgers. The financial close takes days longer than it should due to manual data collection and reporting.
• Risk: Risk is greatly reduced due to transactional visibility, with losses easier to explain and quickly resolve. Knowledge of transactional reconciliation logic is accessible across the company—key employees can leave with zero disruption to the business.
• Compliance: Detailed transaction-level audit logs exist, but the audit process is still slowed by outdated reporting mechanisms. Documents are created manually or using scripts, without being connected to updated source data.

Stage 4: Complete accounting control 

Transaction data is automatically reconciled against accounting balances. Accounting teams have dynamic, dedicated dashboards for the metrics they care about. Financial oversight is strong, but room for innovation remains.

• Data management: ERPs and other internal accounting systems are integrated alongside transaction data sources, all on a single reconciliation platform. Accounting teams can see always-updated, reconciled journal entries in the GL and sub-ledgers.
• Development: Users can deploy no-code workflows and update accounting rulesets to correctly transform data and sync it with the ERP. AI agents simplify the process of automation without diminishing control. 
• Exception handling: The accounting team receives real-time alerts for discrepancies between transactional data and accounting balances. Exceptions are caught and remediated before the end of the close period.
• Financial oversight: Data is synced at least daily with the ERP and prepped for downstream reporting and AI analysis. Finance leaders can make fast decisions without digging into transaction details. 
• Accounting integrity: Accounting leadership can see calculated balances, auto-reconciled journal entries, and progress against the close at a glance. The financial close is reduced by an average of five days. 
• Risk: Operational loss and revenue leakage happens rarely, with complete explainability. Finance operations, accounting, product teams, and leadership all have visibility into reconciliation logic without needing to understand code. 
• Compliance: Transaction- and accounting-level audit logs accelerate compliance efforts and minimize cost. Reporting is automatically tailored to relevant regulations and internal compliance requirements.

Stage 5: End-to-end AI automation

The final stage is an ongoing journey of iteration, scale, and increased ROI. New use cases are continuously deployed with zero code. AI-assisted workflows can help teams identify patterns and suggest improvements as the business and ecosystem evolve, while deterministic rules continue to execute reconciliation controls.

• Data management: Relevant internal and external data sources can be unified on one reconciliation platform. Standardized integrations and controlled configuration help teams add partners, respond to new requirements, and expand into new markets.  
• Development: Scalable, no-code automation makes adding a payment partner, adapting to new laws, and expanding into new markets painless. Massive changes to the financial ecosystem no longer pose a threat.
• Exception handling: Agentic AI powers every step of risk detection and exception management. Users manage sophisticated automations that quickly remediate issues like chargebacks, disputes, and refunds.
• Financial oversight: AI-ready, reconciled data powers a multitude of innovative financial modeling, forecasting, and performance tracking. New use cases are easy to implement as requirements change.
• Accounting integrity: The books always align with transactional data. AI-assisted workflows support a more automated and auditable financial close, with reporting and documentation configured for the organization’s accounting policies and workflows. 
• Risk: Risk from reconciliation errors is reduced through exception visibility, predictive analysis, and governed no-code configuration. 
• Compliance: AI-assisted analysis and governed logic support reporting across internal control and compliance workflows.

What’s next on the path to maturity?

Now that we’ve laid out a vision for full reconciliation maturity, it’s time to get to work. Plot yourself on the model, outline next steps, and prioritize the use cases that are most likely to move you to the next stage. 

At Simetrik, we’ve helped finance operations teams across top financial services, retailers, and marketplaces move from costly manual transaction matching and reporting to AI-enabled, fully automated reconciliation. 

Get in touch with our team to request a demo.

In September 2024, the FDIC proposed a rule to improve recordkeeping for custodial (also called FBO, or “for benefit of”) accounts, where fintechs and non-bank entities pool customer funds in FDIC-insured banks. The goal is to ensure that banks can accurately identify individual fund owners and their balances, even when intermediaries track transactions.

The rule, aimed at enhancing depositor protection and increasing public confidence in insured deposits, could become law any day. When it happens, unprepared banks will have to scramble to put new technology and workflows in place to maintain compliance. 

The catalyst: the 2024 Synapse bankruptcy 

The proposed FBO rule is largely a response to the 2024 bankruptcy of Synapse Financial Technologies, a middleware provider that allowed businesses to integrate banking services into their own applications. 

After a subsidiary of Synapse began offering cash management accounts to their partners’ end users, the company filed for bankruptcy protection. One of their partner banks, Evolve, froze access to Synapse accounts to the tune of over $200 million, stating lack of access to an essential system of record. 

The result was chaotic. End customers couldn’t access their funds, but to release them the FDIC needed transaction and ledger records from Synapse. Between access issues and inadequate recordkeeping, there was no way to recoup losses using FDIC insurance. With $96 million missing and over 100,000 customers affected, the saga still isn’t fully resolved.

What are custodial, or FBO, accounts?

Custodial accounts are bank accounts held by one party (the “custodian”) on behalf of another (the “beneficial owner”). In fintech-bank partnerships, these accounts typically hold pooled customer funds under the fintech’s name or a third party’s, with individual user balances tracked outside of the core banking system by the third party.

This arrangement creates a visibility gap for the bank. It doesn’t inherently know who the end users are or how much each is owed, making things complicated for FDIC insurance determinations when something goes awry. 

Why is custodial account recordkeeping so complicated? 

Over the past decade, a huge influx of fintech companies have entered the market. These entities aren’t allowed to provide the full spectrum of financial products, so they rely on partners to enable them. 

In the case of custodial accounts, these partners are banks (referred to in the rule as Insured Depository Institutions, or IDIs) who already are licensed and insured to provide accounts insured by the FDIC. 

These partnerships create a complex ecosystem of intermediaries and fintech partners that each enable their own customer base to open accounts with the custodian bank. For most banks, it’s nearly impossible to keep track of who manages whose accounts, transaction details, and daily balances across all of the different systems.

For example, while individual customers may initiate millions of transactions each day on the fintech side, the details aren’t necessarily preserved by various members in the ecosystem. Some will initiate bulk movements that aggregate individual transactions into a single amount, making them hard to disambiguate later. 

Any company growth, new partnerships, or regulatory changes just increase this complexity, risking the loss of visibility and traceability of critical financial movements. Catastrophic events like the Synapse bankruptcy don’t happen every month—but losing millions to leakage, inefficient operations, and audit fees is far more common. 

What are the FDIC’s proposed changes to FBO accounts?

The FDIC’s proposed rule, Part 375, outlines new requirements for maintaining and reconciling records for FBO accounts. IDIs holding custodial deposit accounts with transactional features would be required to:

• Meet new recordkeeping requirements, including maintaining records of custodial account details like the beneficiary, owner, and the balance attributed to each end user in a standardized format.
• Be subject to an annual validation by an independent person or entity to assess and verify that third parties are maintaining accurate and complete records consistent with the proposal’s requirements.
• Implement internal controls to ensure that balances of custodial deposit accounts are accurate and reconciled daily.
• Complete an annual certification of compliance and an annual report of compliance.

Banks would be allowed to partner with a trusted third-party to meet these requirements, as long as certain conditions are satisfied. They must have direct, continuous, and unrestricted access to the records maintained by the third party, as well as have continuity plans and internal controls in place.

How do the FDIC’s proposed changes affect PSPs and fintechs?

For banks to meet these requirements, their fintech and intermediary partners must be able to provide daily transaction-level data on money moving to and from custodial accounts. These individual records must be reconciled against FBO balances, taking into account complications like rolling reserves and delayed settlements. 

To facilitate this, PSPs and other fintechs should invest in scalable, real-time transaction tracking and reconciliation technology that can sync data across all systems at least once per day.

How Simetrik helps you prepare for the new FDIC rule

Simetrik is an enterprise reconciliation platform that helps banks maintain and govern this improved method of recordkeeping that will soon be required by the FDIC. 

Here’s how it works:

• Banks can integrate data from all of their fintech and middleware providers in one place, standardizing it to meet the specific requirements outlined in the proposed rule.
• Transactions are monitored and reconciled daily, catching discrepancies and potential fraud instantly so they can be handled before reporting to the FDIC.
• Bulk money in/money out records are automatically disambiguated to keep end user balances accurate.
• Simetrik dashboards inform stakeholders of individual beneficiary activity and balances, total daily net movement, daily cumulative balances, and more.
• Simetrik users can search and explore a subset of custodial accounts, like a specific partner.
• Reporting is automatically prepared for appropriate third-party auditors and shared in the correct format. 

A similar process applies to fintech and middleware providers who choose to follow suit by adopting Simetrik. Transaction data from their platforms is monitored and reconciled daily with bank balances. This reconciled data is always accurate, searchable, and ready to use in customer-facing products or to meet additional reporting requirements. 

Scaling with no code automation

Unlike legacy or homegrown solutions you may have used in the past, Simetrik doesn’t require expensive integrations or hours of engineering work to set up each partner. Unify your custodial account transaction data and configure advanced reconciliation logic faster with flexible, no-code building blocks—so you’ll be ready when the proposed rule becomes enacted law.

Don’t get caught unprepared

The FDIC hasn’t yet announced when this rule will go into effect, but it may happen soon. While you still have time, adopt a unified reconciliation platform that will make the whole process painless and successful. 

To learn more about Simetrik, request a demo here.