Loan prep tool
Income stability checker
Enter the last 6 months and check whether the income story looks stable enough to defend before you upload real statements.
Start with the free manual tool. If you want the real document view after that, analyze a statement PDF.
Free tool
Measure income volatility before the real review starts
Enter the last 6 months and get a fast stability read. The tool stays intentionally narrow. The next layer, if you need it, is statement-backed deposit detection and cleanup.
Enter the last 6 months
Designed for self-employed, commission, and mixed-income borrowers.
Volatility is 7.4% from low month to high month. Median monthly income is $5,425.
Simple 6-month average.
Often more useful when income jumps.
Weakest visible month.
Strongest visible month.
The sequence looks reasonably consistent on a manual pass. The paid statement workflow becomes better when you want payroll detection, transfer cleanup, and merged multi-account history instead of manual numbers.
Natural next step
Replace the manual pattern read with the real statement pattern
The manual checker is useful for a quick pattern read. Uploading actual statements is useful when you need payroll vs transfer cleanup, deposit pattern detection, and a number you can defend with the file.
What it gives you
Fast enough for a first pass
Each tool is intentionally narrow. The job here is a clean estimate, not a fake replacement for a full statement analysis.
Built for variable income
Uses average, median, and volatility together so one big month does not hide the real picture.
Shows the weak points
Surfaces weak months quickly, which is usually what causes the underwriting conversation.
Natural upgrade path
Strong fit with the paid statement workflow because the next question is always whether the deposits are actually clean.
Where it fits
This tool is about one question: how noisy does the income story look?
Freelancers and consultants
A clean way to see whether the last 6 months look stable enough before you package statements for a lender or landlord.
Commission and bonus earners
Useful when income is real but uneven and you want a first-pass read on volatility.
Mortgage and rent prep
Helpful when someone is about to judge your consistency from statements you have not uploaded yet.
Internal ops teams
Fast enough for an intake assistant doing early triage before a deeper review.
Deeper context
How to interpret income stability
The result is most useful when it helps you judge whether the income story looks easy to believe, borderline, or likely to trigger follow-up questions.
Stable does not mean perfectly flat
Many legitimate income patterns move month to month. What usually matters is whether the range stays understandable and whether weak months still leave the story intact.
Median often matters more than one big month
A spike can make the average look stronger than the real pattern. That is why stability tools should care about dispersion, not just totals.
Dead months change the conversation fast
A missing or sharply reduced month is often what pushes a file from easy to explain into something a reviewer will want to examine closely.
Deeper context
Why statement-level review changes the picture
Manual month totals are a good start. The real statement matters because it explains what those totals are actually made of.
Payroll and transfers are not the same thing
A monthly total can look stable while the underlying inflows still include internal transfers, reimbursements, or noise that should not be treated as real income.
Deposit timing can matter as much as amount
Regular patterns usually look stronger than random timing, even when the totals are similar. Statement data helps expose whether the pattern feels coherent.
The file has to be defensible, not just numerically decent
When someone else is reviewing the statements, clarity and cleanliness matter. The analyzer helps move the story from rough claim to document-backed pattern.
Supporting guides
Read the article version if you want more context
The tool gives you the quick read. These posts explain the thresholds, use cases, and document expectations behind the result.
FAQ