AI Agents

Context

Built for banking within the SME/Corporate underwriting — financial insights stack, this agent runs after spreading/mapping to explain what changed in the borrower’s performance and why. It’s used at origination and renewal so KPI movements are interpreted—with internal and external context—before the file advances to committee.

What it does

The agent analyzes multi-period statements and computed KPIs, detects material shifts, and interprets drivers so reviewers see an explained movement, not just a changed number. It ties ratio and metric changes to concrete causes—inventory build, receivable stretch, margin mix, non-recurring items—and, where available, contrasts those movements with sector signals and peer baselines. Outputs are concise, exhibit-linked explanations per KPI (e.g., “gross margin ↓120 bps due to mix + input cost rise; recovery lag vs. sector”), ready to drop into the credit pack.

Core AI functions

Time-series construction from standardized statements; change-point and trend detection on key KPIs (growth, margins, WC turns, liquidity, leverage, coverage, cash conversion); variance decomposition to quantify driver contributions (price/volume/mix, WC components); seasonality and base-effect adjustments; one-off detection; optional overlay of external references (peer/sector indices) for context; and reason-code generation with links back to the exact lines and periods that support each explanation.

Problem solved

KPI tables lack context and push analysts to reverse-engineer drivers—leading to uneven narratives, avoidable rework, and slower reviews. External conditions are referenced inconsistently, making movements hard to judge.

Business impact

Transparent financial insights with business-context alerts help reviewers focus on what matters, reduce back-and-forth, and create more consistent credit stories. Teams move faster from numbers to judgment, with less variance across analysts.

Integration and adjacent use cases

Integration complexity is light–moderate: ingest standardized financials from your spreading/mapping step; write KPI movements, driver attributions, and exhibit links into the underwriting workbench or credit pack—no core changes required.

Common combinations in this stack:

• Interdependency checker (to link balance movements to sales/liquidity behavior),

• KPI forecaster (to project trajectories),

• Market pulse aligner (to benchmark against sector peers),

• Market intelligence report (to enrich credit analysis), and

• Credit memo generator (to auto-draft the narrative with exhibits),

• Balance sheet extractor (upstream standardization),

• Financial mapping validator (prevents ratio drift).