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The reconciliation system that I work on tries to match data from different system to make sure they agree. Like match the Visa transaction file against what our system has internally recorded in its ledger. There is an unique key to match both records so no math involved.
Can someone help me understand the premise of this article? I think the goal here is to map the internal books of a business to their bank account, but I have never seen the kind of "grouping" the article seems to assume as given. In which scenarios do these groupings happen? If there are several customers, there will be different invoices and therefore separate payments. Why would the bank just throw them together,…
If you buy something for $0.99 today, they won't bill you immediately. If you buy another $0.99 item tomorrow, you'll get a consolidated charge for $1.98. You would need to do something like this to link the $1.98 to your app/song purchases.
The reason for this is that credit card processing often costs a flat service charge + a percentage of the bill: Stripe is 30 cents + 2.9% right now. The flat portion dominates for small charges, so you'd want to combine them if at all possible. (Apple certainly gets a better rate...but also has a scale where small savings add up).
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Whether something is better than nothing depends on its error rate and the consequences of an error.
Fair, but also humans working manually don't have a 0% error rate
lol at ai for solving deterministic knapsacks. Just get yourself a solver. And if you wanna solve billion item sized problems hire an OR scientist to write a decomposed algo. Literally after 2 minutes of search: https://arxiv.org/pdf/2002.00352.pdf
You searching arxiv.org stood out to me. I wish I had better skills at searching academic papers for problems I'm trying to solve or that I'm just thinking about. I think just as there are some people that google better than others, I imagine a similar skill applies to academic papers. Anyone encounter this? How do I get better at it? I thought at first it was an accessibility problem, and perhaps it still is. In tha…
do NOT subscribe to any academic journals. not worth it for an individual. find a way to get access for free, such as through a local library or institution, or by other means. also note that often google scholar gives a link to a PDF over on the right, or in alternate versions of the article.
there are services like perplexity.ai that can search arxiv, pull articles, and feed them through an LLM for you -- it's pretty much what you want. some of the LLM chat interfaces let you upload PDFs too. none of this actually works that well yet but sometimes useful.
As somebody who worked as an accountant, I saw several times where automated reconciliation solutions devastated books with repeat mistakes and lacking audit logs. I saw interns do the same, too. An automated solution to reconcile statements based on LLM matches removes transparency on how your books are prepared and might create a false sense of trust in the preparation of your books. In case of an audit, people wil…
Sean here, author and EM of the recon team at Modern Treasury. I completely agree, we us AI to surface suggestions to users during manual review so there's full transparency and human confirmation at every step. Our automatic recon doesn't use LLMs for the reasons you outlined.
I'm head of processing development at PayProp[^1] where we've been automatically reconciling rental payments for two decades using the techniques described in the article - we just don't call it an AI or a LLM. Our tech saves letting agents huge amounts of time. We look at the data we have and if it's sufficient we can "automatically" reconcile it - i.e. suggest a match with 100% certainty that the user(s) can then c…
I'm confuses, reconcialiation is also used in Bayesian statistics. Is it the same context?
Similarly, matching invoices to purchase orders and authorising payments. This catches fraud and avoids paying for goods you didn't receive ... but it's another necessary evil rather than a value-adding differentiator for the business. So companies exist to take your PDF invoices and your ERP's "we recorded that we got x, y, z" and match them up and authorise payment for unexceptional invoices (we wanted 5, you said you sent 5, we said we got 5, let's pay you for 5).
Can anyone recommend a reconciliation application they've had a good experience with? My current work place rolled their own recon service. Is this the norm?
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Sean here, author and EM of the recon team at Modern Treasury. I completely agree, we us AI to surface suggestions to users during manual review so there's full transparency and human confirmation at every step. Our automatic recon doesn't use LLMs for the reasons you outlined.
If the AI is right 99% of the time, what are the odds a human detects the remaining 1% rather than just being complacent?
Even if in reality the AI misses _considerably more_ than that, there's a good argument to be made that such hints may steer the human from their initial, correct assumptions, or even reasoning.
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Fair, but also humans working manually don't have a 0% error rate
I thought the whole point of double entry bookkeeping is to make sure that the error rate is, in fact, precisely 0% and verifiably so.