How to Use:
Analyzing Any Account with Account X-Ray Analytics
X-Ray Analytics gives you a forensic breakdown of any account's spending behavior — top categories, peak spending days, and year-over-year change. Here's how to read it and what to act on.
What You'll Learn
- How to navigate the X-Ray dashboard for any account and read each panel
- What the 'spending heat map' reveals about your behavioral patterns
- How to use the month-over-month comparison to catch expense drift before it compounds
What It Does
Your bank statement is a list of numbers. Account X-Ray Analytics is an interpretation of what those numbers mean. It answers questions your statement never could: Am I spending more on food this year than last? Which days of the month am I most likely to overspend? Is my 'discretionary' spending actually discretionary? This guide shows you how to get those answers in under 10 minutes.
Who This Guide Is For
You've imported or logged at least 60 days of transactions for an account and want to understand the behavioral patterns inside that data.
Open X-Ray for a Specific Account
Navigate to Accounts → [Account Name] → X-Ray Analytics. Alternatively, from the main Insights screen, tap 'Account X-Ray' and select your account. The X-Ray dashboard loads with your selected account's full data. At the top you'll see the analysis time range (default: last 90 days) and a summary row: total inflow, total outflow, net position, and your top spending category.
Start with your primary spending account — the one your UPI transactions and debit card are linked to. This will have the richest transaction data and produce the most useful insights.
Read Your Category Breakdown
Scroll to the Category Breakdown panel. This shows your spending distributed across all categories as both a percentage chart and a ranked list with absolute amounts. The list is sorted by total spend (highest first). Look for two things: any category that surprises you (more than you expected), and the gap between your top category and your second. A large gap often indicates a hidden concentration of spending you weren't aware of.
Tap any category in the list to see all individual transactions within it, sorted by amount. This is often where you discover the specific merchants or purchases driving a category higher than expected.
Read the Spending Heat Map
The Spending Heat Map shows your daily transaction activity across the selected period as a calendar grid where each day is colored by spending intensity (lighter = low spend, darker = high spend). Look for patterns: Are there consistent high-spend days around specific dates (like the weekend after salary arrives)? Do the last 5 days of the month consistently show high intensity? These behavioral signatures are the engine's most actionable output.
A common pattern in Indian salary cycles: heavy spending in days 1–7 (post-salary confidence), low spending in days 8–22, and a second spike in days 27–30 (month-end scarcity spending on 'last chances'). If you see this pattern, the fix is a week 1 spending limit, not a month-end strategy.
Use the Month-Over-Month Comparison
Tap 'Compare Months' at the top of the X-Ray screen. Select any two months to compare. The panel shows: total spend in each month, category-by-category delta (which categories went up or down and by how much), and a net change summary. This comparison is most valuable when you suspect spending in a category has crept up — the delta view makes invisible drift immediately visible.
Run a month-over-month comparison between the month you started using Fin OS and your most recent month. Users consistently find 2–4 categories where spending has declined — this is the data that confirms the app is working.
Read Your Merchant Intelligence
Scroll to the Merchant Intelligence panel. This shows your top 10 merchants by total spend, the number of transactions per merchant, and average transaction size. Large total spend with high transaction count = habitual spending (daily coffee, frequent food delivery). Large total spend with low transaction count = occasional large purchases (electronics, travel). These two patterns require different corrective strategies.
Your top merchant by transaction count is almost always your highest behavioral risk — because the habit is so ingrained you've stopped noticing it. Calculate the annual cost of your top merchant. The result is usually genuinely surprising.
Pro Tip
After your first X-Ray session, write down one sentence about what surprised you most in the data. Share it with no one — just write it for yourself. This reflection step converts data observation into behavioral awareness, which is the necessary precursor to change. Users who do this consistently see faster improvement in their spending patterns than users who review the same data passively.
The minimum for meaningful patterns is 60 days. The heat map and behavioral patterns become significantly more reliable at 90 days. Annual comparisons require at least 13 months of data. If you've just imported 6 months of CSV history, your X-Ray analytics will be immediately rich — which is one of the strongest reasons to do the import early.
Yes. Credit card X-Ray is often more revealing than savings account X-Ray because credit card spending tends to be more diverse and behavioral — it covers discretionary purchases rather than fixed bills. If you regularly carry a credit card balance, your X-Ray will also show you the interest charges as a distinct line in your outflow, making the real cost of the balance visible.
Some transactions couldn't be auto-categorized and were assigned to 'Uncategorized.' Tap the category to see the individual transactions. For each one, you can manually assign the correct category — this also trains the AI Pattern Recognition engine to categorize that merchant correctly in future imports.
Ready to try it?
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