You look at the total and it’s $4,380. Last month it was $3,720. Nothing obvious happened \u2014 no vacation, no new car, no medical bill. You didn’t feel like you spent $660 more. And now you’re scrolling a transaction list looking for a purchase big enough to explain it, and there isn’t one.<\/p>\n
There usually isn’t. A $660 swing is rarely a $660 purchase. It’s more often three categories moving $150 to $250 each in the same direction, plus a couple that quietly went the other way and hid part of the difference from you.<\/p>\n
A month-over-month total is an aggregate of dozens of independent behaviours. Groceries, fuel, subscriptions, one annual insurance renewal, a birthday, a dentist. Each moves for its own reason. Summing them produces a figure that is real but has no cause attached to it, which is why staring at it doesn’t resolve anything.<\/p>\n
The pie chart has the same problem in a prettier format. A pie chart shows composition for one month. Groceries at 22% tells you groceries are a big slice \u2014 it does not tell you whether 22% is normal for you, because you have no baseline in the frame. Two pie charts side by side are marginally better and still hard to read, because the slices resize relative to a total that itself changed. A category can grow in dollars and shrink as a percentage in the same month.<\/p>\n
What you actually need is a per-category delta in dollars, ranked, with the negatives included.<\/p>\n
If you’re doing this by hand, the shape of the work is the same regardless of tool.<\/p>\n
Pull both months as a single file.<\/strong> Export a date range that covers this month and the previous one from each account you use. Most banks let you set a custom range and download CSV; the mechanics differ per institution, and if you’re in Canada the export steps for RBC, TD, Scotiabank, BMO, CIBC and National Bank<\/a> are documented step by step.<\/p>\n Get every account in, not just the busy one.<\/strong> A comparison built on your checking account alone will miss the credit card where the change actually happened. Partial data produces a confident wrong answer, which is worse than no answer.<\/p>\n Sum by category and month, then subtract.<\/strong> Pivot table, one row per category, one column per month, a third column for the difference. Sort that difference column descending.<\/p>\n Read the bottom of the list too.<\/strong> The categories that went down are doing real work. If dining rose $310 and travel fell $180, your total only moved $130 and the dining change is invisible in the headline figure. This is the single most common reason people conclude “nothing changed” when something did.<\/p>\n What you get is a short list \u2014 usually two to four categories carrying nearly all of the movement, and a long tail of \u00b1$20 noise you can ignore.<\/p>\n Having found your movers, resist treating last month as truth. Last month is one sample. If your groceries run $520, $610, $480, $590 across four months, then a $610 month isn’t an anomaly \u2014 it’s the top of your normal range. Compared against a $480 month it looks like a 27% spike.<\/p>\n A better test: does this month fall outside the range the same category has occupied over the last several months? That’s what spending anomaly detection means in practice \u2014 not “bigger than last time” but “outside this category’s own established pattern.” A category with a stable history flags on a small deviation. A category that’s always volatile needs a much bigger move before it means anything.<\/p>\n Three things separate a real change from noise:<\/p>\n One structural problem breaks all of the above: a lot of transactions aren’t one category. A $214 Costco charge lands as “groceries,” and inside it are $130 of food, $50 of clothing and $34 of household supplies. When that charge is $290 the following month, your groceries line moves $76 and you have no idea whether you bought more food or more sheets.<\/p>\n Merchant-level categorization makes big-box and warehouse spending structurally unreadable \u2014 which matters because that’s often exactly where household spending concentrates. The fix is splitting at the line level rather than the transaction level, so a single trip resolves into the three or four categories it actually was<\/a>. PennySlice does this automatically, and it also runs on photographed receipts, so a paper receipt becomes itemized lines categorized individually<\/a>.<\/p>\n The manual version works. It also takes an hour, and you have to remember to do it, which means you’ll do it once.<\/p>\n PennySlice runs the comparison as a conversation. Drop in a CSV, Excel, OFX or PDF export \u2014 the AI reads the structure, so there’s no template to choose and no columns to map \u2014 or link a bank through Plaid on a paid tier, where your login is entered at your own bank and comes back as a revocable read-only token. Then ask what changed in your spending compared to last month, and get the ranked per-category delta with real figures from your own data. Comparing months is one of the eight areas the chat covers<\/a>.<\/p>\n You also don’t have to remember to ask. Penny Spotter runs seven detectors in the background, three of which target exactly this question: category anomalies, where spending breaks your own established pattern; recurring cost creep, where subscriptions and bills quietly rose; and budget pace, which flags a category on track to exceed its budget before the month closes rather than after.<\/p>\n That last distinction is the point of doing any of this. Finding the category that moved on the 3rd of the following month is a post-mortem. Finding it on the 12th, while the month is still running, is information you can still use.<\/p>\n Export this month and last month from every account you use, load them in one file, and ask what moved. The list will be shorter than you expect \u2014 and one of the names on it will be a category you’d have sworn was flat.<\/p>\n PennySlice provides spending information, not financial advice.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Why did my spending go up this month? The total never says. Here’s how to isolate the one or two categories that actually moved, using your own export.<\/p>\n","protected":false},"author":2,"featured_media":45,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ps_meta_title":"Why Did My Spending Go Up This Month?","footnotes":""},"categories":[2],"tags":[],"class_list":["post-46","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-pennyslice"],"_links":{"self":[{"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/posts\/46","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/comments?post=46"}],"version-history":[{"count":0,"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/posts\/46\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/media\/45"}],"wp:attachment":[{"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/media?parent=46"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/categories?post=46"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pennyslice.ai\/blog\/wp-json\/wp\/v2\/tags?post=46"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}One month over one month is a weak baseline<\/h2>\n
\n
Where the categories themselves lie to you<\/h2>\n
Asking instead of building the pivot table<\/h2>\n
Start with two months and one file<\/h2>\n