You have a CSV of eighteen months of transactions and a vague sense that something in there is leaking. You open a chat box and type “analyze my spending”, and you get back a paragraph that could have been written about anyone. That is not the AI failing. That is a question with no edges.<\/p>\n
A conversational tool grounded in your own data answers narrow questions well and broad ones badly. So the useful skill is knowing where the edges are. In PennySlice, there are eight areas a question can land in: transactions, accounts, categories, budgets, imports, receipts, predictions and what-if scenarios. Here is what each one actually answers, and what comes back.<\/p>\n
This is the area most people underuse, because they assume search is all it does. It is not search \u2014 it is filtering, summing and sorting in plain English.<\/p>\n
Things that land here:<\/p>\n
The answer shape is a figure plus the rows behind it. Twelve parking charges, $186. You can see the twelve. That matters more than it sounds, because the moment an AI gives you a number you cannot trace, you have to take it on faith \u2014 and financial numbers you cannot check are not worth much.<\/p>\n
Account-level questions are about distribution. Balances, transfers between your own accounts, which card carries which kind of spending.<\/p>\n
That last one is quietly useful. Subscriptions scattered across three cards are the ones nobody audits.<\/p>\n
Category questions are the standard “where is my money going” question, made answerable.<\/p>\n
The part that changes the answers here is sub-transaction categorization. One purchase can be split across several categories at the line-item level, so a warehouse-store run becomes groceries plus household plus clothing rather than a single lump filed under whichever label loses the least information. If you have ever looked at a category report and thought “most of that groceries number is not groceries”, that is the reason \u2014 and splitting a Costco receipt at the line level<\/a> is the fix.<\/p>\n Ask a category question against split data and the answer is a different number. Usually a more uncomfortable one.<\/p>\n Budget questions are about position in a period, which is the thing a monthly statement is worst at telling you.<\/p>\n The answer comes back as a figure and a pace \u2014 spent to date, days remaining, projected end-of-month. Penny Spotter also runs budget pace as a background detector, so the flag arrives before you thought to ask.<\/p>\n This is the area nobody expects to be conversational, and the one that removes most of the setup friction. When you drop a file in \u2014 CSV, Excel, OFX, PDF, a forwarded email, or a photo \u2014 the AI reads it and detects its structure. There is no template to pick and no column-mapping screen.<\/p>\n So the questions are about the file itself:<\/p>\n If your bank’s export is the part you are stuck on, the guides cover that separately \u2014 there are 104 bank export guides across 14 countries, including a walkthrough of exporting transactions from Canadian banks<\/a>. Import works wherever you are. Bank linking through Plaid is a separate capability, available in the US and Canada, on paid tiers.<\/p>\n A bank statement gives you a merchant and a total. A receipt gives you what was in the bag. Photograph one and OCR pulls the itemized lines, each categorized individually.<\/p>\nBudgets: pace, not just totals<\/h2>\n
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Imports: asking the AI to analyze my bank statements right after the file lands<\/h2>\n
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Receipts: line items a statement cannot show you<\/h2>\n