We model a timestamp which functionally is "when does this activity go on the books"? We compute clearing both based on this "effective_at" and a "system time" (there are some business functions with known periodicity and it makes for a useful debugging tool).
Long-clearing activity makes for some interesting business cases and long-standing float obviously can lead to some detection noise. For example, Brazil has uniquely long settlement times.
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While Stripe does love to write, I think our core premise here was that being able to reason mathematically about independent distributed systems was a unique application of a ledger. Likewise, we think the data quality offering is a unique extension since it gives us tools to make improvements to our systems over time.
Long-clearing activity makes for some interesting business cases and long-standing float obviously can lead to some detection noise. For example, Brazil has uniquely long settlement times.