AI Agents

Context

Built for banking in the Commercial lending stack, this agent is used when preparing SME and corporate credit files to evidence that projected cash flows can service debt. Typical scenarios include new term facilities, renewals with revised structures, and transactions where DSCR and coverage tests determine tenor, covenants, and conditions.

What it does

The agent ingests historical financials and borrower projections, then calculates debt-service coverage and related capacity metrics under the proposed structure. It reconciles operating cash flow to statement line items, builds the debt service profile (amortization, interest, fees), and checks assumptions behind the forecast (growth, margins, capex, working-capital movements). It runs sensitivities (e.g., lower revenue, higher rates) and seasonality where relevant, highlights gaps and inconsistencies, and returns a clear view of coverage today and through the life of the loan, with links to the exact figures and assumptions used.

Core AI functions

Document and model parsing for financial statements and projections; normalization of line items to a standard cash-flow model; automatic debt-service schedule construction from terms and pricing; DSCR/ICR and fixed-charge coverage computation across base and downside cases; assumption validation (e.g., capex vs. depreciation, WC turns vs. history); variance checks between narrative and numbers; and reason-code generation explaining each exception (“interest schedule missing step-up,” “revenue CAGR exceeds history by XPP,” “negative WC swing not modelled”).

Problem solved

Manual DSCR analysis is time-consuming and error-prone. Analysts rebuild cash-flow bridges, debt schedules, and sensitivities by hand, and mismatches between projections and narrative lead to late rework before committee.

Business impact

Credit preparation moves faster and arrives better evidenced. Coverage is calculated consistently, weaknesses are surfaced early, and committees see transparent links from terms and assumptions to capacity—supporting better credit decisions, fewer last-minute conditions, and smoother approvals.

Integration and adjacent use cases

Integration is light–moderate: ingest financials and projections from portal/email/DMS or your credit workbench; read the proposed term sheet; write coverage outputs, exceptions, and sensitivity results back to the lending workflow—no core changes required.

Common combinations in this stack:

• Financial statement analyzer (standardized historicals and ratios),

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

• Contract & revenue stream verifier (to tie projections to agreements),

• Collateral & security package validator (to align LTV, encumbrances, and insurance),

• Legal entity & shareholding validator, and

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