AI Agents

Context

Built for banking in the Commercial lending stack, this agent runs at the start of credit analysis—before any financial benchmarking, risk assessment, or KPI evaluation begins. Typical scenarios include new credit applications, annual reviews, and portfolio monitoring where accurate industry classification drives everything downstream: the right peer comparisons, appropriate risk thresholds, and correct early-warning triggers.

What it does

The agent reads the company's business description, revenue breakdown, and available operational data, then compares the actual activity profile against the official sector code (NACE, SIC, ISIC). It flags mismatches where the registered classification no longer reflects reality—a "warehousing" company that's actually commodity trading, a "manufacturer" that outsourced production and now licenses brands, a "consultancy" generating most revenue from proprietary software. When a mismatch is detected, the agent proposes the correct functional classification and explains the evidence behind the recommendation. Only after validation do downstream agents (KPI analysis, cash-flow modeling, peer benchmarking) receive a confirmed sector input.

Core AI functions

Business description parsing and activity extraction; revenue stream analysis to identify dominant business model; cross-referencing operational indicators against sector definitions; confidence scoring with evidence trails; mismatch flagging with plain-language explanations; and suggested reclassification with supporting rationale.

Problem solved

Companies are often classified based on what they registered years ago, not what they actually do today. This flows silently through every downstream analysis—wrong benchmarks, wrong risk thresholds, wrong peer comparisons. A commodity trader benchmarked as a logistics company looks artificially weak. A real estate holder benchmarked as a construction firm triggers false alarms. Analysts catch some of these manually, but inconsistently and late in the process.

Business impact

Credit analysis starts from accurate foundations. Benchmark comparisons become meaningful. Risk models use appropriate thresholds. Analysts spend time on judgment calls, not data hygiene. Portfolio monitoring triggers fire on real signals, not classification artifacts.

Integration and adjacent use cases

Integration complexity is light: ingest company registration data, business descriptions, and revenue breakdowns from your onboarding flow or credit workbench; write validated sector classification back to the case record—no changes to cores.

Common combinations in this stack include:

• Financial statement analyzer,

• KPI & ratio benchmarking agent,

• Cash-flow & repayment capacity (DSCR),

• Market & competitive intelligence agent, and

• Early warning / covenant monitoring agent

So all downstream analysis runs against a validated, accurate sector classification.