The European SME credit market is currently defined by a data divide between originating banks with private granular records and investors who must rely on lagging public disclosures. To resolve this information asymmetry, a proposed AI monitoring framework utilises distinct architectures for the Insider and Outsider perspectives, enabled by the European Single Access Point and governed by the EU AI Act. The Insider Monitor allows banks to convert dormant private data into real-time intelligence through automated financial statement analysis, transactional analysis, and agentic covenant monitoring. The Outsider Monitor provides investors with an engine of inference, triangulating alternative data such as news sentiment, web signals, and sector-level proxies to construct synthetic views of borrower health when private ledgers are inaccessible.

Our framework leverages advanced AI technologies for document analysis and autonomous agentic workflows that transition credit management from static periodic reviews to continuous surveillance. Because the EU AI Act classifies credit scoring as a high-risk application, these systems are designed around Explainable AI and mandatory human-in-the-loop oversight to ensure transparency and auditability. Motivated by recent academic research called CovenantAI the framework moves beyond binary views of covenant compliance by utilising a nuanced taxonomy of renegotiation to distinguish between strategic capital optimisation and genuine financial distress. Ultimately, the convergence of these AI architectures aims to create a more resilient and transparent European credit market by effectively opening the black box of SME risk and private debt.

Read the full article here: AI Architectures for Real-Time SME Credit Risk Monitoring in Europe