Solution
For credit analysts, risk teams, and lending operations: multi-period borrower risk analysis with every finding cited — the same depth on every file, from the largest exposure to the long tail.
The problem
Large exposures get thorough analysis; everything else gets what time allows. The riskiest thing in a credit file is the pattern nobody had time to see.
Performance lives across statements, returns, bank statements, and schedules. Reconciling reported income against banking activity is manual work, so it rarely happens.
A risk number with no explanation can't be challenged, calibrated, or defended to committee. Analysts end up re-deriving the analysis before they trust it.
The product, not a promise
How it works
Take in the borrower's financial statements, tax returns, bank statements, and debt schedules.
Extract performance data into consistent, source-linked metrics across periods.
Detect anomalies, trend breaks, and inconsistencies between what different documents claim.
Assess repayment risk against your credit policy, with the drivers made explicit.
Deliver a risk picture an analyst can interrogate — every finding clickable back to its source.
Who it's for
Credit analyst
Chief credit officer
Risk & audit
The riskiest thing in a credit file is the pattern nobody had time to see. Borrower performance data arrives buried in statements, returns, and schedules; assembling it into a view that reveals trends and anomalies is manual work, so it happens thoroughly for large exposures and thinly everywhere else. Borrower Risk Analysis applies the same analytical depth to every file.
Working from the financial data extracted across the borrower’s documents, the agents build a consistent multi-period picture of performance: revenue trajectory, margin movement, leverage, liquidity, and cash flow against obligations. On top of that structure, the analysis hunts for what a rushed manual review misses — anomalies within a period, breaks in trend between periods, and inconsistencies across documents, such as reported income that fails to reconcile with banking activity or a debt schedule that disagrees with the balance sheet.
Repayment risk is then assessed against your credit policy, with the drivers spelled out rather than compressed into an opaque score. An analyst sees which specific factors moved the assessment — and clicks through from any figure to the exact page of the source document it came from. The result is decision-ready intelligence for credit, risk, and operations teams: accurate, repeatable, and consistent whether it runs on one borrower or across the portfolio.
Risk conclusions drive pricing, structure, and approval — decisions a lender must be able to reconstruct and defend later. Every finding in this analysis carries its lineage: the source citation, the computation, the policy rule applied. Every conclusion goes to a human for approval before it shapes a decision, and every step lands in the audit trail. Guesswork is eliminated by making every claim checkable against the borrower’s own documents.
Objections, answered
Every finding travels with its source citation, its computation, and the policy rule applied — checkable against the borrower's own documents in one click. Nothing shapes a decision without a named analyst's approval.
The assessment runs against your credit policy, with drivers spelled out per your criteria. Rules are configured once and applied identically to every borrower.
The lineage of every conclusion: extracted values, source pages, computations, policy rules, reviewer, and approval — recorded as the analysis happened, on every file.
The analysis works from documents the platform already reads — statements, returns, bank statements, schedules. Setup is encoding your policy criteria: configuration, not a model-building project.
Watch a full multi-period risk analysis assemble — every finding clickable to its source — live in the demo.
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