AI Agents

Context

Built for banking in the Commercial lending stack, this agent is used when preparing new or amended facilities that rely on collateral and legal security. Typical scenarios include ABL and term loans secured by receivables, inventory, equipment, property, deposits, shares, or guarantees—at origination, renewal, or when collateral is substituted or re-valued.

What it does

The agent reads the collateral pack—pledge/mortgage deeds, security agreements, guarantees, lien and registry searches, appraisals/valuations, asset schedules, and insurance certificates—and validates that the security is identifiable, enforceable, and policy-conformant. It confirms asset identity and ownership (serial numbers/VINs/legal descriptions), checks for existing encumbrances and priority, reconciles valuation date, method, and currency, and verifies that insurance is in-force with the correct insured/loss-payee wording and coverage. It applies eligibility rules and haircuts by collateral type, computes LTV and concentration, and flags gaps such as expired filings, missing appendices, inconsistent descriptions, or coverage shortfalls. Each finding includes a concise rationale with a link to the exact clause, page, or table used, so reviewers can verify quickly.

Core AI functions

Document classification for security/pledge, mortgage, guarantee, appraisal, registry extract, and insurance forms; clause and field extraction for parties, assets, limits, filing numbers, and expiry dates; normalization of identifiers (VIN/IMEI/serials), legal descriptions, and amounts/dates; cross-document consistency checks (security agreement ↔ registry search ↔ appraisal ↔ insurance); rule evaluation against collateral policy (eligibility, haircuts, concentration, filing and insurance standards); encumbrance/priority detection from registry text; expiry tracking and reason-code generation to explain every pass/flag outcome.

Problem solved

Manual collateral verification is slow and error-prone. Missed liens, expired insurance, inconsistent valuations, or incomplete filings lead to last-minute conditions precedent, rework, or unsecured exposure.

Business impact

Exposure is safer and closings are faster. Conditions are identified early, LTV and concentration are evidenced, and rework drops because exceptions are specific and traceable. Committees and auditors get a clear line from each conclusion to its source, supporting better credit decisions and lower operational risk.

Integration and adjacent use cases

Integration is light–moderate: ingest documents from DMS or deal rooms and read-only registry outputs; write validations, LTV/haircut calculations, and exceptions into the lending workflow or credit workbench—no core changes required.

Common combinations in this stack:

• Financial statement analyzer (standardized historicals and ratios),

• Business plan & use-of-funds validator (purpose/KPI coherence),

• Legal entity & shareholding validator (borrower/owner alignment),

• Contract & revenue stream verifier (tie projected cash flows to agreements),

• Cash-flow & repayment capacity (DSCR) (serviceability against confirmed collateral and cash flows), and

• Commercial lending completeness agent (to confirm the file is committee-ready).