Zedly AI
Data Visualization
Most people only see credit card spending as a long list of transactions. Scrolling through pages of charges, trying to spot patterns or problem areas, is tedious and rarely leads to action.
A simple chart changes everything. It instantly shows where your money goes and where you can cut back. You do not need to wait for monthly PDF statements or copy data by hand.
The fastest path: export transactions as a CSV, feed it to an AI analyzer, and get categorized charts in seconds.
This is the tip most people miss. Banks and credit card issuers spend resources building visual dashboards, but those dashboards group transactions by generic categories like "Shopping" or "Travel." That tells you very little about your actual spending patterns.
The better approach: download your raw transaction data as a CSV file.
Many issuers let you export custom date ranges: last 30 days, 90 days, year-to-date, or any timeframe you choose. Export formats often include CSV, Excel, QBO/QFX, and sometimes OFX.
Chase, American Express, Capital One, Citi, and most major issuers support CSV exports. The option is usually near the search or filter controls on your transaction history page.
A CSV gives you structured data, but you still need to make sense of it. Spreadsheet pivot tables and manual formulas work, yet they demand time and expertise most people do not have. AI removes that barrier entirely.
When you feed a credit card CSV to an AI analyzer, the system reads every row, identifies the relevant columns, and builds charts or summaries on demand. You describe what you want in plain English ("show my top spending categories this quarter") and get a result in seconds. No formulas, no conditional formatting, no VLOOKUP chains.
The biggest advantage is intelligent category grouping. Banks assign each transaction a merchant category code (MCC), but those codes are broad and often misleading. A coffee shop and a furniture store can both land under "Retail." AI looks at the actual merchant name and groups transactions into categories that reflect your real spending: dining, groceries, subscriptions, travel, utilities, and so on.
AI also handles merchant normalization automatically. The same retailer might appear as "AMZN," "Amazon.com," "AMAZON MARKETPLACE," and "AMZ*Digital" across different statements. Instead of treating those as four separate vendors, the system consolidates them into a single entry so your totals and charts are accurate.
Beyond grouping, AI catches patterns that are hard to spot in a list of transactions:
All of this happens without sending your data to a model that trains on it. Zedly processes files in an isolated environment with strict retention controls, so your financial information stays private. The same approach works for bank statement analysis if you want to combine checking account data with your credit card transactions.
Once you have a CSV, you can generate a chart in seconds. Here is what that looks like:
This chart immediately reveals:
No formulas. No pivot tables. Just upload the CSV and describe what you want to see. For merchant-level anomaly drivers and spend cube analysis on larger datasets, see AI spend anomaly detection from Excel.
Choose a date range that matches what you want to analyze:
If you want to compare year-over-year, download the same period from the previous year as a separate file.
Upload your exported CSV file into your analysis tool. In Zedly, you can drag and drop the file onto your workspace.
A quick note on privacy: credit card transactions contain sensitive financial data. Zedly processes files in an isolated environment with strict retention controls. Your data is never used to train AI models, and you control when files are deleted.
Describe what you want to see in plain English. Here are five prompts you can copy and paste:
"Graph my top 10 merchants by total spend."
"Group transactions by merchant and show a bar chart."
"Show monthly spend trend for the last 12 months."
"Show top categories by total spend (and list which merchants are in each)."
"Flag subscriptions/recurring charges and graph them."
The system reads your CSV, identifies the relevant columns, and generates the chart. You can iterate by asking follow-up questions like "now show just the last 30 days" or "exclude refunds."
Once you have the basics working, try these visualizations that tend to drive real spending changes:
Each of these gives you a different lens on the same data. The monthly trend shows trajectory; the merchant drill-down shows habits.
Here is something that solves a real frustration: by storing your credit card statements in your private vault, you can fetch them later for reference without digging through email or re-downloading from your bank's portal.
Every year, tax season arrives and you need to find that one statement from March, or pull together all your business expenses from Q3. The typical workflow is painful:
With a private vault, you upload statements once and they stay organized and searchable. When tax time comes, your data is already there. Ask questions like "show all business expenses over $500 from last year" and get answers instantly, with the source statements linked for verification.
You can also save the full analysis session for later retrieval. Name it something like "Card spend for 2024" and come back to it months later with all your charts, prompts, and results intact. No need to re-upload files or re-run queries.
The vault also means you are not relying on your bank's retention policies. Some issuers only keep 18 months of history online. If you need older data, you are out of luck unless you saved it yourself.
Real-world credit card exports are not always clean. Here are the most common problems and how to handle them:
Some exports include both pending and posted versions of the same transaction. Ask the system to "remove duplicate transactions" or "keep only posted transactions" before charting.
Refunds show as negative amounts. If you want net spend, the system will automatically subtract refunds. If you want gross spend only, ask to "exclude refunds and credits."
The same merchant might appear under several variations ("AMZN," "Amazon.com," "AMAZON MARKETPLACE"). As noted in the AI section above, an AI analyzer consolidates these automatically. If you are using a spreadsheet instead, ask the system to "normalize merchant names" before charting.
If your export includes multiple cards, filter by card number or account, or ask to "show spending by card" to compare them.
Posting date vs transaction date can differ by a day or two. For monthly charts, this rarely matters. For daily analysis, specify which date column to use.
You can graph spending from transaction data. Debt requires balance data, which some issuers include in exports. Otherwise, use statement balance snapshots and enter them manually to track debt over time.
Use a statement converter to extract transactions from PDF to CSV first. Then upload the CSV and generate charts. Many banks also offer CSV exports directly from their online portal if you check the Activity or Transactions section.
With Zedly, your files are processed in an isolated environment with strict retention controls. The system shows you exactly which source file was used for each result, so you can verify the output. Your data is never used for model training. See our privacy policy for details.
Upload your CSV and let AI categorize transactions, normalize merchants, and generate charts in seconds. No spreadsheet formulas, no manual data entry.
Upload Your CSV and Generate a Chart
If you only have PDFs, convert your statements to CSV first.