Bank Statement Analyst AI

Invoked when: bank statement data — open banking / PSD2 feed or PDF OCR — is available for the application

• –Reads all credit entries in the statement over the analysis window and classifies them by source type: regular salary credits (consistent amount, consistent timing, from a named employer), business income receipts (variable amounts, multiple counterparties, consistent with declared business type), rental income (regular fixed credits from consistent counterparties), and inward transfers that are not income (intra-account transfers from the borrower's own other accounts, family transfers, refunds, loan disbursements into the account).
• –Calculates net monthly income by excluding non-income credits from the gross credit total — because a statement that shows €300,000 in monthly credits but €120,000 of those are the instalment bouncing back, €50,000 is a family transfer, and €30,000 is a mutual fund redemption has a genuine income of approximately €100,000, not €300,000. The income calculation shows the breakdown: what was included, what was excluded, and why — so the credit officer can see and verify the classification.
• –Computes an average monthly net income across the analysis window and flags the income's consistency — a coefficient of variation above a configured threshold flags income as "variable" and presents the monthly series rather than a single average, so the credit officer can see whether income variability is seasonal (acceptable with context), trending downward (concerning), or erratic (higher risk). A single average that masks a declining trend is misleading; the agent presents the trend, not just the number.
• –Compares the calculated income figure against the declared income on the application and against any national tax return or VAT data available in the file. Where the bank statement income is materially lower than the declared income, it flags the discrepancy with the specific figures — not to conclude that the application is fraudulent, but because the underwriting DTI must be computed on the verified lower figure, not the declared higher figure.

Invoked simultaneously with income calculation — reads debit entries for obligation and bounce signals

• –Identifies all SEPA Direct Debit entries in the statement — the regular outgoing debits that represent instalment payments on existing loans — and checks each one for bounce or return entries in the 2–5 business day window following the debit date. A SEPA Direct Debit on the 5th followed by a SEPA return credit on the 7th is a bounce; both entries are required to confirm it. The agent does not flag a missing debit as a bounce — it requires both the attempted debit and the return credit to classify a bounce event.
• –Analyses the bounce pattern: a single bounce in 24 months on a single obligation with no recurrence is categorically different from three bounces in 12 months across the same obligation, which is different again from bounces across multiple obligations in the same month (suggesting a liquidity crunch in that specific month). The pattern matters more than the count — and the narrative explains the pattern, not just the number.
• –Identifies undisclosed obligations by cross-referencing SEPA Direct Debit entries against the obligations declared on the loan application. A regular SEPA Direct Debit to a bank that is not listed in the declared obligations section is an undisclosed liability that affects the DTI calculation — the agent flags it with the counterparty name and estimated monthly obligation amount, and the credit officer decides whether to request clarification before proceeding.
• –Estimates the total existing instalment obligation from the statement — combining all identified SEPA Direct Debit debits as the observed obligation baseline. This observed DTI check is more reliable than the declared DTI because it is based on actual outgoings rather than borrower self-declaration, and it includes obligations the borrower may have omitted from their application. Where the observed DTI exceeds the declared DTI by more than a configured tolerance, the discrepancy is flagged for the credit officer's attention.

Invoked after income calculation and bounce detection are complete — requires both to produce a meaningful cash flow score

• –Computes a cash flow health picture across five dimensions: income stability (how consistent is the income month-to-month), surplus adequacy (average monthly surplus after all observed obligations as a multiple of the proposed instalment — is there room in the cash flow for the new instalment?), balance buffer (average month-end balance — does the borrower maintain a meaningful balance or do they run the account to near-zero each month?), obligation discipline (bounce rate as a proportion of total SEPA Direct Debit events over the window), and balance trend (is the average month-end balance growing, stable, or declining over the analysis window?).
• –Produces a composite cash flow score across these five dimensions — not as a single number (which hides the dimension-level picture) but as a five-dimension assessment with a classification per dimension: Strong / Adequate / Marginal / Weak. A borrower with Strong income stability and Strong surplus but Weak obligation discipline presents a different risk profile from one with Moderate income and Adequate everything else — and the credit officer needs to see both profiles clearly, not a single aggregate score that obscures the difference.
• –Produces a plain-language cash flow narrative — a 4 to 6 sentence paragraph that tells the story of the borrower's financial health as revealed by their statement, written for the credit officer to read before making the credit decision. The narrative states what the numbers mean, not just what the numbers are: "The account shows consistent salary credits of approximately €X per month from [employer]. Existing instalment obligations of €Y per month leave a pre-proposed-instalment surplus of €Z. A single SEPA Direct Debit return in month 8 appears isolated rather than structural, coinciding with a period of reduced salary credits — possibly a salary delay rather than income disruption. Month-end balances have been gradually improving over the last 6 months."
• –Computes the DTI headroom for the proposed loan: the proposed instalment as a percentage of the verified average monthly net income, and the DTI including all observed existing obligations. States clearly whether the proposed loan fits within the credit policy DTI limit and, where it does not, by what margin — because an application that is 2% over the DTI limit requires a different response from one that is 15% over.