Bank Statement Analysis
AI bank statement analyzer for lenders.
Kaaj is a bank statement analyzer built for underwriting, not just OCR. It 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.
How to analyze a bank statement with Kaaj
- STEP 1
Send the statements
Upload, forward by email, or post through the API. Digital or scanned PDFs from any bank, 3, 6, or 12 months, across several accounts.
- STEP 2
Every transaction is classified
Deposits are split into operating revenue, owner and internal transfers, loan and MCA proceeds, and refunds, using the borrower's industry as context.
- STEP 3
Totals are reconciled and checked
Parsed totals are checked against the balances printed on each statement, and each document is checked for tampering. Anything that does not reconcile goes to an underwriter.
- STEP 4
Review lender-ready output
Monthly revenue, average daily balance, NSFs, negative days, and MCA positions, each linked to its source line, in the credit memo and your CRM or LOS.
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 |
What lenders say
How lenders use AI bank statement analysis
Five lenders and brokers explain how they use AI bank statement analysis in underwriting: reading true cash flow and inflows/outflows, checking statements for tampering, spreading bank and financial statements in minutes instead of hours, and getting to a pre-qualification in about five minutes.
In this video
- Capital Solutions Providers: the true cash flow
- Eagle Crusher: hours → minutes
- The Funding Booth: no more scrolling statements
- Finance Pros Inc: balances and borrowing
- Hub Funding Solutions: pre-qualified in five minutes
Bank statement analysis FAQ
What is a bank statement analyzer?
Software that reads a business's bank statements and turns them into the cash-flow picture a lender needs: true revenue, transfers, loan and MCA proceeds, average daily balance, NSFs, and existing debt payments. Kaaj is a bank statement analyzer built for lenders; it reconciles its results to the statement totals and runs at a median 48.8 seconds per statement.
How is a bank statement analyzer different from a PDF-to-Excel converter?
A converter turns a statement into rows of transactions, and someone still has to classify them and build the analysis. An analyzer classifies each transaction, separates revenue from transfers and financing, calculates lending metrics such as average daily balance and NSF counts, detects MCA stacking, and flags statements that do not reconcile or show signs of tampering.
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.