Solution
For chief credit officers, underwriting teams and lending operations at banks, credit unions and non-bank lenders: every file tested against the full rulebook, with cited evidence behind each result.
The problem
The policy is written down, yet a hundred pages of borrower documents leave room to miss things — so outcomes vary by reviewer.
Silently waived thresholds surface months later — in quality control, in audit, or in the portfolio.
A revised threshold travels by memo and retraining. Some files are still being decided on the old rules.
The product, not a promise
How it works
Load the complete borrower file — application, financials, credit reports, supporting documents.
Pull the data points your policy actually tests: income, leverage, collateral, history, entity facts.
Run every credit policy rule, eligibility criterion, and product constraint against the file.
Pass moves forward; failures and exceptions queue for review with the triggering evidence attached.
A credit officer approves, declines, or grants the exception — with the full rule trace in front of them.
Who it's for
Credit analyst
Chief credit officer
Risk & compliance
Credit policy is written down; applying it is where the inconsistency creeps in. Two analysts read the same file against the same rulebook and reach different conclusions, because a hundred pages of borrower documents leave room to miss things. This solution makes the policy check mechanical, so the judgment can be human.
Botminds extracts the decision-relevant data points from the complete borrower file — income and cash flow from financial statements, obligations from credit reports, collateral and entity details from supporting documents — and tests them against your credit policy, eligibility rules, and product constraints. Each rule returns a clear result: pass, fail, or exception, with the evidence that produced it cited to the source page in the file.
Nothing is silently waived. A file that fails a debt-service threshold or falls outside a product’s constraints is routed to a credit officer with the triggering rule, the extracted values, and the source documents in one view. Policy exceptions become deliberate, documented decisions instead of things discovered later in QC. And because the same rule set runs identically on every file, decisioning is consistent across analysts, branches, and volume spikes.
Eligibility decisions get examined — by internal audit, by investors, by regulators. Every check the platform runs is logged: which policy version, which rules fired, what evidence supported each result, who reviewed it, and what they decided. Fair and consistent application of policy stops being an assertion and becomes a queryable record. The platform never auto-declines or auto-approves on its own; every decision is human-approved, made faster because the rule work arrives already done and already cited.
Objections, answered
No. It returns pass, fail, or exception for each rule, with the evidence cited to the source page. A credit officer approves, declines, or grants the exception, and that sign-off is logged. Nothing is auto-approved or auto-declined.
Your thresholds, eligibility criteria and product constraints are expressed as explicit rules against the extracted fields. The rule set is versioned, so you always know which edition of the policy decided which file, and updating a rule takes effect on the next file processed.
Every check is logged: which policy version ran, which rules fired, what evidence supported each result, who reviewed it, and what they decided. Fair and consistent application of policy becomes a queryable record rather than an assertion.
The rule engine starts on your real files alongside your current process. Most teams compare its rule results against analyst decisions on recent files first, then move the live queue once the results line up.
Watch every policy rule run against it live — pass, fail and exception, each cited to the page that triggered it.
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