Topic starter
16/09/2026 9:21 am
TL;DR: Corporate lending is moving toward a two-layer model, with Comarch arguing that process and AI should stay separate so auditability and interpretation remain distinct. Lending governance now has to preserve human accountability while live inputs keep changing the evidence base.
Editorial analysis by NHI Mgmt Group, based on content published by Comarch: “Beyond Sequential Workflow: Redefining Corporate Lending Strategy with AI”.
Key questions
Q: Why do real-time data feeds change credit governance so much?
A: Because they turn lending from snapshot review into continuous interpretation.
Q: What fails when lending teams rely only on sequential workflow?
A: The workflow can still route work, but it cannot capture the parallel and iterative nature of corporate credit analysis.
Q: How should banks govern AI in corporate lending without losing auditability?
A: Banks should place AI inside a controlled workflow rather than outside it.
Practitioner guidance
- Define the process layer and AI layer separately Map which steps in corporate lending remain procedural, which become advisory, and which require human approval so governance stays explicit.
- Log decision provenance for every case Capture the inputs, timestamps, model output, and human override path behind each recommendation so the bank can explain outcomes later.
- Treat live data feeds as control inputs Govern open banking, e-invoicing, and ERP data as part of the lending decision chain, including validation, freshness, and lineage checks.
Bottom line: Corporate lending is moving toward a governance model that separates procedural control from AI-assisted interpretation, rather than treating AI as a replacement for the credit process.
Glossary terms: Decision Provenance, Two-Layer Architecture, Continuous Decisioning, Auditability
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This topic was modified 1 hour ago by NHI Mgmt Group
16/09/2026 9:21 am
Two-layer decisioning is becoming the governance pattern that matters. Corporate lending shows why process and intelligence should not be collapsed into one control plane. The process layer preserves accountability, while the AI layer adds interpretation and recommendation. That separation is likely to spread wherever organisations need machine-speed analysis without surrendering human ownership of the final decision. Practitioners should treat this as a governance design principle, not a banking exception.
A question worth separating out:
Q: Should credit teams treat AI as automation or decision support?
A: As decision support. In corporate lending, AI is better used to search, compare, flag inconsistencies, and draft a first justification than to replace the expert. The final credit call should remain with a human owner who can interpret context, weigh exceptions, and stand behind the decision.
👉 Read our full editorial: Corporate lending needs a two-layer model for AI governance
This post was modified 1 hour ago by NHI Mgmt Group