Turn transaction matching into true exception review

Agent-prepared transaction matches, review-ready, with exceptions, and source-to-match traceability.

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Maxima integration diagram connected to NetSuite, Snowflake, Chase, Sage, Workday, and SAP.

From manual matching to exception review

Matching today

Matching with

Your team cleans and reshapes fragmented data before matching can begin

Rigid rules break on tolerances, timing differences, aggregations, and multi-way matches.

Matching starts when someone pulls the files, often at month end.

Unmatched transactions pile up for your team to investigate.

Matching today

Data from source systems arrives normalized and ready to match

Rules handle the obvious. Max handles the long tail with smart matches and flexible AI rules

Transactions match continuously as source data arrives.

Review exceptions, not transactions. Your team focuses on the few that require judgment.

Matching with

Transaction matching, built for real-world complexity

Bring every transaction into one workflow, at close volumes

Connect banks, ERPs, processors, subledgers, warehouses, and files for real-time feeds, ready to match

Transform source data before it hits the matching engine

Apply FX conversions, lookups, formulas, mappings, and validations to each source before matching begins

Match how transactions actually move

Handle exact matches, tolerances, shared references, offsets, and imperfect data with rules built for each scenario.

Let Max build the logic for complex matches

Point Max at your datasets and watch it build matching rules tailored to your data and process, without IT

Take exceptions through resolution

Clear unmatched items, carry reconciling items into account reconciliations, or create correcting journal entries without leaving the workflow.

Bring every transaction into one workflow, at close volumes

Diagram of finance and business apps feeding Maxima workflows for journal entries, reconciliations, flux analysis, transaction matching, and close command center.

Bring every transaction into one workflow, at close volumes

Connect banks, ERPs, processors, subledgers, warehouses, and files for real-time feeds, ready to match

Transform source data before it hits the matching engine

Maxima AI bank reconciliation workflow showing JP Morgan accounts matched at 99.8% and journal entries ready to approve or reject.

Transform source data before it hits the matching engine

Apply FX conversions, lookups, formulas, mappings, and validations to each source before matching begins

Maxima journal entry automation screen showing PTO accrual audit log, agent draft note, and source data for accrued compensation.

Match how transactions actually move

Handle exact matches, tolerances, shared references, offsets, and imperfect data with rules built for each scenario.

Let Max build the logic for complex matches

Let Max build the logic for complex matches

Point Max at your datasets and watch it build matching rules tailored to your data and process, without IT

Take exceptions through resolution

Clear unmatched items, carry reconciling items into account reconciliations, or create correcting journal entries without leaving the workflow.

Matching that scales with your accounting

High-volume matching

Continuously match millions of transactions across banks, processors, and subledgers.

Multi-way matching

Reconcile across three or more systems without forcing complex workflows into two-way rules.

Tolerance-based matching

Automatically clear differences within approved thresholds and route material exceptions for review.

Intelligent exception resolution

Max investigates what doesn’t tie, explains why, and recommends what to do next.

Matching shouldn’t be a black box

Deterministic by design

Same rules, same data, same result.

Traceable to source

Trace every match back to the underlying records.

Controlled changes

Approved logic and locked periods stay permissioned.

Complete audit trails

Every rule change, match, exception, and agent action is logged.

Audit trail table showing workflow updates and Max-prepared journal activity.

Built for accounting teams operating at scale

98%

match rates across a global multi-entity environment

15 hours → 15 mins

Brex transaction matching across three entities

$21M

In monthly transaction matched continuously

50%

reduction time in bank-GL matching

Frequently asked questions

What types of transaction matching can Maxima automate?

Maxima supports cash and bank reconciliations, payment processor matching, AP and AR, intercompany, GL-to-subledger, and system-to-system matching. Common workflows include bank-to-GL, Stripe payouts-to-GL, Coupa-to-NetSuite, and other high-volume reconciliations.

Can Maxima match transactions across more than two systems?

Yes. Maxima supports multi-way matching across three or more systems, including workflows spanning the GL, payment processors, banks, subledgers, and internal systems.

Do I need to build matching rules myself?

No. Accounting teams can configure rules directly, or point Max at their datasets to have it propose matching logic tailored to the data and reconciliation process. Your team reviews and approves the logic before it runs.

What happens if Max suggests the wrong matching logic?

Your team reviews and approves Max-created logic before it runs in production. If Max investigates an exception where the right answer isn’t clear, it surfaces its reasoning and supporting evidence for accountant review rather than silently clearing the item.

What happens to transactions that don’t match?

They remain visible as exceptions rather than being forced into a match. Your team can investigate and resolve them directly in the reconciliation, including matching, ignoring, carrying forward, or preparing a correcting journal entry.

Can Maxima handle messy or inconsistent source data?

Yes. Maxima can transform each source independently before matching, including FX conversions, lookups, formulas, mappings, sign changes, and validations. This lets teams match the underlying accounting activity even when systems structure it differently.

Does Maxima use AI to decide whether transactions match?

Approved matching logic executes deterministically: the same rules applied to the same data produce the same result. Max is used to build matching logic and investigate complex exceptions, rather than putting a probabilistic model in the core matching path.

Can Maxima create journal entries from unmatched transactions?

Yes. Accountants can create correcting journal entries directly from reconciliation exceptions and route them through the normal review and approval workflow, rather than rebuilding the adjustment separately.

How is Maxima different from transaction matching in NetSuite?

NetSuite matching is designed primarily for matching within the ERP. Maxima brings detailed transaction data together across banks, processors, subledgers, warehouses, and other systems, handling high-volume and complex matching outside the ERP without forcing that processing into NetSuite.

How is Maxima different from traditional transaction matching software?

Traditional matching tools rely heavily on predefined rules and leave teams to investigate what those rules cannot resolve. Maxima combines deterministic matching with source-level transformations and Max to build complex matching logic and investigate the long tail of exceptions.

Move closer to an audit-ready, continuous close

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