Category: Control & Reconciliation

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.

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.