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The hidden data problem behind payment reconciliation
Payment reconciliation problems in high-volume environments are seldom caused by process failures. The underlying issue is finance data architecture. Event-level transaction detail, including gateway fees, FX spreads, and reversal costs, gets compressed before it reaches your accounting records, leaving Finance to reconstruct what the numbers mean rather than explain them. Finance-grade data with full lineage changes that.
6 min readJul 15, 2026)
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Five questions your ERP vendor doesn’t want you to ask about AI
When you're choosing a finance ERP, the AI sitting on top matters far less than the data underneath it. Five questions test this foundation in any evaluation: real-time transaction-level data, output you can trace to source, continuous processing, embedded controls, and the same inputs producing the same outputs every time. Beware vague answers that reveal a gap.
5 min readJun 24, 2026)
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The reconciliation trap: why your month-end close gets longer as you scale
Reconciliation effort scales with transaction volume because most finance architectures process data in aggregate – grouping transactions into totals and posting summaries to the ledger. Every mismatch then has to be traced back by hand, and growth multiplies these mismatches. The month-end close runs long for the same reason: the work was deferred, not done as data arrived.
7 min readJul 22, 2026
Latest insights
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Insights library
The reconciliation trap: why your month-end close gets longer as you scale
The hidden data problem behind payment reconciliation
Five questions your ERP vendor doesn’t want you to ask about AI
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AI-enabled vs AI-native: why the distinction matters for your next finance ERP decision
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Audit-ready outputs: why ‘plausible’ isn’t good enough for finance AI
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Real-time finance data: how to stop your finance AI working with one eye closed
