Bank Statement Analysis
AI bank statement analysis that understands context.
Kaaj goes beyond OCR. Our bank statement analysis reads messy PDFs from any bank, classifies revenue, detects transfers and MCA proceeds, tracks NSFs, and adapts to industry context because what counts as revenue depends on the business. In production it is 99.7% accurate at a median of 48.8 seconds per statement.
Book a demoMeasured on real lender statements in production
Methodology: Measured on live lender statements in production (September 2026). Accuracy reconciles Kaaj's parsed dollar totals against the totals printed on each statement; statements flagged for a large discrepancy were manually reviewed by the Kaaj team (most were false positives from the check itself), and 99.7% were parsed without a material error. Median dollar error was $0.00. Latency is the median time to parse one statement end to end.
Context changes classification
The same transaction means different things depending on industry. Zelle might be legitimate revenue for a trucking company but unusual for a restaurant. Kaaj applies industry-aware logic to every line.
Revenue classification
Stripe, Square, ACH, wire: automatically identified, classified, and trended across months.
MCA & loan detection
Funder names, recurring debits, stacking patterns flagged using research-backed data, not static keyword lists.
Transfer identification
Internal transfers, sweeps, owner draws separated from revenue so you see the real picture.
NSF & risk tracking
Non-sufficient funds, overdrafts, and negative balance days: every risk signal tracked and counted.
Cashflow trends
Monthly revenue patterns, average daily balance, and deposit consistency visualized and trended.
Industry-aware logic
Classification adapts to the borrower's industry and business model: trucking, restaurant, retail.
Built for messy statements from any bank
Real deals arrive as broker-forwarded PDFs, phone scans, and multi-account statements from dozens of banks. Kaaj reads them without templates and returns the same cash-flow view every time.
Scanned and low-quality PDFs
Phone scans, rotated pages, and faxed statements are read without templates, then checked against the statement's own balances.
Any bank, one schema
Layouts from national banks, community banks, credit unions, and fintech accounts are normalized into the same revenue, balance, and NSF metrics.
Multi-account and multi-month
3, 6, or 12 months across several accounts are combined, with inter-account transfers removed so revenue is not double counted.
Deposits vs. transfers vs. proceeds
Operating revenue is separated from owner and internal transfers, loan and MCA proceeds, and refunds before any metric is calculated.
Recurring payments and stacking
Recurring MCA remittances, loan payments, and payroll are detected by pattern and funder, surfacing existing debt service and stacking.
Self-checking output
Parsed totals are reconciled to the statement; anything that does not reconcile is routed to an underwriter instead of passing silently.
Kaaj vs. extraction-only tools vs. manual review
Extraction tools turn statements into rows. Underwriters still need those rows turned into lender-grade cash flow. Here is where each approach stops.
| Kaaj | Extraction-only OCR / API | Manual spreading | |
|---|---|---|---|
| Time per statement | Median 48.8 seconds | Varies by vendor (extraction only) | Minutes to hours, by statement length |
| Accuracy check | Reconciled to statement totals (99.7%) | Varies; usually field-level OCR confidence | Depends on the analyst |
| Revenue vs. transfers vs. proceeds | Classified with industry context | Generic categories or none | Manual judgment |
| MCA stacking and recurring debt | Detected automatically | Not typically | Manual pattern spotting |
| Output | Source-linked findings in a credit memo | Transactions / spreadsheet export | Spreadsheet spread |
| High volume | Same workflow for one deal or a full pipeline | Yes, for extraction | Limited by headcount |
Bank statement analysis FAQ
How accurate is Kaaj's AI bank statement analysis?
99.7%. Measured on live lender statements in production (September 2026), Kaaj's parsed dollar totals reconciled with the totals printed on the statement for 99.7% of statements after review, and the median dollar error was $0.00.
How long does Kaaj take to analyze a bank statement?
A median of 48.8 seconds per statement in production. A multi-month, multi-bank package is analyzed in minutes, far faster than a manual spread.
Can Kaaj read messy, scanned, or multi-bank PDF statements?
Yes. Kaaj reads digital and scanned PDFs from any bank without templates, including broker-forwarded and multi-account statements, and normalizes them into the same cash-flow metrics.
How does Kaaj separate revenue from transfers and loan proceeds?
Every deposit is classified as operating revenue, internal or owner transfer, loan or MCA proceeds, or refund, using the borrower's industry as context. Recurring debits such as MCA remittances, loan payments, and payroll are flagged so existing debt service is visible.
Does Kaaj work for high-volume processing as well as single deals?
Yes. The same workflow runs a single urgent deal or a lender's full pipeline, at a median 48.8 seconds per statement.
What does Kaaj produce from bank statements?
Monthly revenue and deposit trends, average daily balance, NSF and overdraft counts, negative balance days, MCA and loan positions, and source-linked findings that flow into a decision-ready credit memo.