Back in the 80s, if you had a good accountant, they knew your situation. Not because they looked it up every year. Because they remembered. They knew your Bunnings receipts were always tools. They knew your phone was 80% work. They knew that the Coles receipt was personal because you told them once, three years ago, and they never forgot.
That is what a trusted professional relationship looks like. They play your greatest hits without you having to press play every single time.
Today, Oscar learned to do the same thing.
We shipped Oscar Memory — a per-user merchant learning engine that sits behind every scan you do. From today, Oscar tracks how you classify each merchant over time. The more consistent your pattern, the more confident his suggestion becomes. And if you want to skip the learning phase entirely, you can pin a merchant as Always Work or Always Personal. Oscar will never question it again.
Here is what it looks like in practice:
That Woolworths entry did not start at 94% confident. Oscar got there by watching. Every time you saved a Woolworths receipt as D5C, he updated his understanding. By the twelfth time, he barely needs to think about it.
Most receipt apps treat every scan like the first time they have ever met you. You scan a Bunnings receipt. The app asks you to classify it. You pick D3. You scan another Bunnings receipt three weeks later. The app asks you again. You pick D3 again. The app has learned absolutely nothing.
That is not how a good accountant works. A good accountant remembers. After your first appointment, they know you are a tradie. After your second, they know Bunnings is always tools. After a year, they barely need to ask you anything because they already know your business better than most.
Oscar now works the same way. He builds a picture of how you actually run your business, one receipt at a time, and that picture gets sharper every week.
"The longer you use SnapClaim, the less work you have to do. That is the whole point. Oscar should be getting smarter about your situation, not starting from zero every single scan."
Paul Mueller, SnapClaimHere is the thing that sparked all of this. A cafe owner buys milk at Coles for their business every single day. It is a cost of sales, D5C, absolutely deductible. A hairdresser buys a bottle of water from the same Coles for clients waiting in the salon. Also D5. But someone else buying the same thing is just doing a personal grocery shop.
Coles is genuinely ambiguous. Oscar cannot know which one you are from a single scan. But after a few weeks of watching how you classify it, he starts to understand. And if you want to skip the learning period entirely and just tell him, one tap on Always Work or Always Personal and he is locked in forever.
What if you buy milk for the cafe on Monday and do your personal grocery shop on Wednesday? No problem. Oscar uses weighted history, not a binary switch. One personal purchase does not erase twelve business ones. The history stays. You just override it for that individual receipt, and Oscar notes the exception without forgetting the pattern.
Merchant memory is the foundation. Once Oscar knows your patterns at the merchant level, we can start building on top of that. Category memory. Seasonal patterns. Spending clusters. A quarterly summary that Oscar generates himself because he already knows what you spend and where.
The end goal is simple. Tax time should not feel like paperwork. It should feel like pressing play on a mixtape Oscar already knows by heart.
Oscar Memory is live today for all SnapClaim users. Free plan users get a 14-day trial automatically. No action required — just keep scanning and Oscar will start learning your business from your very next receipt.
We almost called this feature Smart Classification or Adaptive Learning or some other bland tech name that means nothing. Then someone said it was like asking your 80s stereo to play your greatest hits — and once it knows your favourites, it never stops playing them. Oscar's Greatest Hits it is.
Oscar approves. He has opinions about most things.
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