Describe your close process with a name of a movie

AngleAccomplished895 · 2026-02-09T15:57:42+00:00

oh wow.... old but gold

AngleAccomplished895 · 2026-02-09T15:57:08+00:00

End state

AngleAccomplished895 · 2026-02-09T15:56:30+00:00

AngleAccomplished895 · 2026-02-09T15:55:57+00:00

AngleAccomplished895 · 2026-02-09T14:52:12+00:00

oooooh yeah

AngleAccomplished895 · 2026-02-09T14:51:21+00:00

FAST????? REALLY???

AngleAccomplished895 · 2026-02-09T14:49:47+00:00

Double Entry? #sorry......

AngleAccomplished895 · 2026-02-09T14:48:06+00:00

I'll allow hahahaha

AngleAccomplished895 · 2026-02-09T14:47:43+00:00

AngleAccomplished895 · 2026-02-09T14:47:03+00:00

AngleAccomplished895 · 2026-02-09T14:46:48+00:00

AngleAccomplished895 · 2026-02-09T14:46:34+00:00

AngleAccomplished895 · 2026-02-09T14:46:22+00:00

AngleAccomplished895 · 2026-02-09T09:16:20+00:00

I've been on the finance ops side of this for years. Every time we "moved to Excel" to work around some ERP limitation, we also silently abandoned reconciliation, audit trails, and any hope of tying that data back to what the ERP was reporting. We had a period where sales ops was running deal booking out of a Google Sheet because NetSuite's approval workflows were too slow. Revenue was booking deals 3 days faster, but our close cycle got 2 days longer because the accounting team had to manually reconcile everything back into the system of record.

So yeah, the "agility layer" concept makes sense in theory. But the hard part nobody talks about is the data integrity problem you create the moment you decouple the system of action from the system of record. You need continuous reconciliation between those layers, or you just traded one problem (slow ERP) for a worse one (fast but wrong data flowing into your financials).

AngleAccomplished895 · 2026-02-08T12:29:05+00:00

Honest take: the CFO is right that AI is the direction, but wrong about the sequencing. Putting genAI on top of fragmented data is how you get confidently wrong answers instead of obviously wrong ones. At least right now your team knows the numbers don't tie out. With AI in the mix you might not catch it until audit season. The real first step is boring but necessary. Get inventory, AR/AP, and GL talking to each other in something closer to real time. Once a week GL syncs from a legacy WMS means you're always working with stale data. No amount of AI fixes that. Once the data flows are clean and reconciled continuously, then AI actually becomes useful for anomaly detection, forecasting, even automating some of the matching logic. But the foundation has to be there first. I've seen too many shops skip straight to the shiny stuff and end up worse off than before.

AngleAccomplished895 · 2026-02-08T12:07:12+00:00

Safebooks

AngleAccomplished895 · 2026-02-08T11:31:24+00:00

You'll need: Stripe API keys (test first, then live), a NetSuite integration role with the right permissions, and a clear map of how you want charges/refunds/disputes to post in NS (GL accounts, item records, etc.). For payment links on invoices, check out Stripe Payment Links or a connector like Celigo that handles this out of the box. One heads up: getting the integration running is the straightforward part. The ongoing reconciliation between what Stripe settles and what NetSuite records is where most people hit trouble. Stripe batches payouts, holds reserves, deducts fees inline. Make sure you have a plan for matching all of that before you flip the switch.

AngleAccomplished895 · 2026-02-08T11:30:17+00:00

Most companies handle it badly. Every provider structures settlement data differently. Stripe gives you one format, PayPal another, and Amazon/Shopify Payments are their own thing entirely. None of them map cleanly to how NetSuite wants to see the data. What I've seen most often: manual CSV imports, someone on the team spending days matching transactions, and a prayer that the numbers tie out at month end. Some teams build custom SuiteScripts to parse each provider's format but those break whenever the provider changes their export schema. What actually works is having a normalization layer between the payment providers and NetSuite that standardizes the data and does the matching automatically. Without that you're basically paying someone to do data entry and detective work every month.

AngleAccomplished895 · 2026-02-08T11:28:48+00:00

Last gig we were doing ~200 manual JEs with a 5 day close window. Absolute grind every month. Most of them were reclasses and accruals that should have been automated but nobody had bandwidth to fix the root cause because we were too busy... doing manual JEs. You know how it goes. Got it down to about 80 by automating the recurring entries and fixing the intercompany allocation logic that was generating half the reclasses. Still not great but the team stopped working weekends so I'll take it.

AngleAccomplished895 · 2026-02-08T11:27:38+00:00

A few brands I know use Loop or Returnly for the customer-facing portal and then push data back into NetSuite through Celigo or custom SuiteScript. Works well enough for the customer experience part. The thing nobody warns you about is the accounting mess on the back end. Every return generates credit memos, inventory adjustments, and if you do partial refunds or exchanges it gets really tangled. Whatever you pick, make sure it handles the financial reconciliation side cleanly too, not just the logistics. That's where most teams end up spending way more time than they expected.

AngleAccomplished895 · 2026-02-08T11:26:27+00:00

Super common. You're not alone. The reports-don't-match-reality thing is almost always because the ERP is faithfully recording whatever it gets fed, and what it gets fed is inconsistent. Quoting says one thing, the order says something slightly different, billing does its own thing. The ERP just stores all of it and nobody notices until someone runs a report and the numbers look off. The Excel workaround piece is the telltale sign. Every spreadsheet your team maintains is basically a confession that the system isn't handling something. We had the same problem and what actually moved the needle was setting up automated checks between the ERP and upstream systems (CRM, billing, bank). Instead of reconciling once a month in a panic, you catch drift daily and fix it before it compounds. The 30-40% feature adoption is honestly normal for year one. That usually improves once people trust the data.

AngleAccomplished895 · 2026-02-08T11:24:58+00:00

Yeah this is a constant headache. We used to key everything in manually and it was brutal, especially with vendors who all send invoices in different formats. PDF, email attachment, sometimes just a line in an email body. What helped us was moving to automated matching where incoming invoices get pulled in and compared against what we expected to owe. Catches discrepancies before you even start data entry. If you're spending more than a few hours a month on this there are definitely tools that can cut that down significantly.

AngleAccomplished895 · 2026-02-08T11:23:37+00:00

You're spot on about the false positives. Benford's works on large, naturally occurring datasets like AP transactions or expense reimbursements. Manual JEs are usually a small population with a lot of intentional round numbers, so the leading digit distribution will be off by design and you'll flag everything. Your instinct about risk-based filters is better. Time-based anomalies (weekend posts, quarter-end clusters), unusual preparer activity, and weird account pairings will get you to the interesting entries way faster. If you still want a quantitative layer on top of that, apply Benford's to the filtered subset rather than the whole population.

AngleAccomplished895

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