Tighter standards create a consistency problem
Central and Eastern European banks are operating in a more selective credit environment. According to the article, the July 2026 euro area Bank Lending Survey found a net 7% of banks tightened credit standards for enterprises in the second quarter, while standards also tightened for housing loans and consumer credit. Rejected applications rose across borrower groups, driven mainly by perceived economic risk and lower risk tolerance.
For lenders, that turns underwriting discipline into an operational challenge. Once risk appetite tightens, analysts must interpret revised policies, review more evidence, challenge assumptions, and document the case against stricter thresholds. If this remains manual, consistency becomes fragile.
- Similar borrowers may receive different outcomes because policy is interpreted differently across analysts or teams.
- Review times increase as more applications are escalated or returned for missing evidence.
- Rejection rates can rise beyond what the underlying risk environment actually requires.
This matters because loan demand from firms increased slightly in the second quarter of 2026, contrary to the sharper decline banks had expected. That leaves a narrow commercial window: demand exists, but tolerance for error is lower.
What AI-assisted underwriting should actually do
A credible AI-assisted underwriting model should support the analyst at specific points in the process rather than act as an autonomous decision-maker. The distinction is important: policy, approval authority, and exception judgement remain with the bank.
- Collect and structure borrower data from applications, financial statements, supporting documents, and internal systems.
- Identify missing information, inconsistencies, and policy exceptions before a case reaches an approver.
- Generate an initial risk summary grounded in approved data and linked source documents.
- Apply eligibility models and scorecards consistently while exposing the main factors shaping the result.
- Recommend the next workflow action, such as straight-through processing, manual review, or escalation.
This reduces repetitive work while preserving the separation between model output and credit decision. In the article's framing, the model prepares evidence and highlights patterns; the authorised employee remains responsible for approval, judgement, and exceptions.
The case for hybrid decisioning
A rigid choice between extremes does not work well in this environment. Purely rules-based decisioning is transparent but can become too rigid. Purely model-driven decisioning may capture richer patterns but can be difficult to govern. For CEE banks, the stronger model is hybrid: policy rules, scorecards, machine-learning models, and human judgement working together inside one governed workflow.
In that design, a deterministic rule can reject an application that breaches a hard policy or legal limit. A predictive model can estimate the likelihood of default. A generative AI assistant can summarise the borrower's financial position and point out contradictory evidence. The workflow then combines these outputs, assigns the correct review path, and maintains a defensible record of what influenced the final decision.
This is also the more defensible response to the EU regulatory environment. Creditworthiness systems can fall within the high-risk framework under the AI Act, which makes documentation, monitoring, governance, and human oversight central design requirements.
How ACP supports controlled AI-assisted underwriting
Axe Credit Portal is positioned as infrastructure for embedding AI into the credit workflow rather than attaching it as a disconnected assistant. ACP can combine deterministic rules and AI-driven models, orchestrate structured data and documents, and keep human validation inside the process.
- Explainable eligibility models that support consistent application of policy.
- Document intelligence that classifies files, extracts relevant values, and maps them into the process with confidence scores.
- Source traceability so analysts can follow extracted information back to the original document location.
- AI-generated credit write-ups built from configurable templates and workflow context.
- An interactive agent that lets analysts question, refine, and validate generated content before it enters the approval record.
The value proposition is deliberately not "AI decides the loan". It is that every analyst begins with a more complete, consistent, and reviewable evidence base.
Five controls banks should require
Scaling AI-assisted underwriting without controls would be a design error. These five requirements form the minimum governance foundation.
- Traceability Every material AI output should link back to the data, document, or rule that supports it.
- Explainability Decision models should reveal the key factors influencing eligibility or risk.
- Human authority Approval rights and exception handling should remain explicit, role-based, and visible in the workflow.
- Monitoring Banks should track model performance, overrides, drift, and outcome differences across segments.
- Fallback processes The workflow must remain usable if an AI model or an external service becomes unavailable.
KPIs for proving the value
CEE banks, especially mid-sized institutions, are unlikely to fund an open-ended AI programme. That means the business case needs to be tied to measurable operational and risk indicators rather than broad innovation claims.
The objective is not maximum automation. It is a better allocation of human attention: straightforward cases should move faster, while contradictory or complex cases should receive deeper review from the people whose judgement genuinely changes the outcome.
Precision is the competitive advantage
Credit standards are likely to remain sensitive to geopolitical, energy, and sector risk. Tightening has been particularly pronounced in the car industry and energy-intensive manufacturing. In that environment, blunt policy changes alone are not enough.
CEE banks need the ability to update rules, incorporate new signals, explain outcomes, and monitor how those changes affect both portfolio quality and customer treatment. AI-assisted underwriting helps only when it operates inside a controlled credit architecture.
The advantage, then, is precision. Not looser standards. Not autonomous lending. Better evidence gathering, more consistent analysis, explainable models, and accountable human decisions.