AI Agents

Context

Built for banking within the SME/Corporate underwriting — financial insights stack, this agent runs after spreading and mapping to test whether the borrower’s numbers move together in a coherent way. It’s used at origination and renewal so underwriters see how sales, margins, working capital, liquidity, and leverage interact—before the file advances to committee.

What it does

The agent analyzes multi-period statements to link causes and effects across metrics. It checks whether revenue growth aligns with gross profit and operating cash flow, whether inventory and receivables scale sensibly with sales, whether payables support working-capital needs without undue stretch, and whether leverage and liquidity respond realistically to performance. It highlights broken or unusual linkages—for example, rising sales with falling cash conversion, margin upticks alongside inventory bloat, DSOs lengthening faster than growth, or debt rising with no asset build—and returns exhibit-linked explanations so reviewers see what decoupled, when, and by how much.

Core AI functions

Time-aligned series construction; correlation and lag analysis across sales, COGS, margins, inventory/receivables/payables, cash conversion, liquidity, and leverage; scale-checks (per-unit and per-turn metrics); seasonality and mix controls; anomaly scoring for decoupling (e.g., DSO/DIH vs. growth, payables stretch vs. liquidity); contribution analysis to quantify which drivers broke the linkage; and reason-code generation with links back to the exact lines and periods supporting each finding.

Problem solved

Ratio tables show movements but not whether they make sense together. Analysts spend time reverse-engineering why cash conversion diverged from growth or why leverage rose without asset formation—creating rework and inconsistent narratives.

Business impact

Reviews are faster and more decisive: inconsistent stories are exposed early, explanations are evidence-based, and attention goes to the few relationships that matter. That reduces back-and-forth, sharpens risk judgment, and produces a more consistent credit story across deals.

Integration and adjacent use cases

Integration complexity is light–moderate: ingest standardized financials from spreading/mapping; write decoupling flags, reason codes, and exhibits to the underwriting workbench or credit pack—no core changes required.

Common combinations in this stack:

• Balance sheet extractor (source statements),

• Financial mapping validator (prevents ratio drift),

• KPI trend scanner (explains movements before linkage checks),

• KPI forecaster (projects trajectories with realistic dependencies),

• Market pulse aligner (tests patterns versus sector norms),

• Market intelligence report (adds external context), and

• Credit memo generator (auto-drafts the narrative with exhibits).