The stakes for AI and digital lending have never been higher for banks operating across Central and Eastern Europe, South‑Eastern Europe and the Baltic states. Stage 2 exposures are elevated and concentrated in commercial real estate and SME portfolios, credit demand is strong but supply constrained, and macro scenarios for 2026 range from continued growth to tariff‑induced contraction. At the same time, customers expect frictionless digital experiences while regulators sharpen their focus on model risk, data quality and cyber resilience. Without disciplined governance and strategic foresight, AI adoption risks becoming a patchwork of pilots that fail to scale.
This article argues that AI is most valuable when embedded in a coherent strategy that spans the entire credit lifecycle and is anchored by robust governance and scenario planning. We explore the key building blocks of governance, examine plausible futures for AI‑enabled lending in the region and illustrate how Axe Finance’s Axe Credit Portal (ACP) provides a flexible platform to navigate uncertainty.
The case for disciplined strategy and governance
The regulatory environment is evolving rapidly. Recent risk assessments highlight that cyber and ICT threats are among the most prominent operational risks and that distributed denial‑of‑service incidents dominate reported cases. The Digital Operational Resilience Act (DORA) harmonises incident reporting and resilience requirements across EU member states. In parallel, supervisory authorities are scrutinising model risk management, data lineage and the explainability of AI‑driven decisions. Models must be validated, monitored and auditable across jurisdictions.
Lenders also face divergent macro scenarios. Industry outlooks identify persistent inflation, potential tariff shocks and diverging consumer sentiment as key uncertainties. Baseline scenarios foresee GDP growth slowing and interest rates drifting lower. Regional surveys show that about two‑thirds of cross‑border banking groups plan to expand selectively in CEE/SEE/Baltics, even as credit standards tighten. Such complexity demands a governance framework that enables rapid adaptation without sacrificing control.
Governance as a catalyst for speed
Governance should be seen as an enabler of speed, not a brake. Clear policies allow teams to move quickly because they know which data sources are permitted, how models must be validated and when human oversight is required. Lending surveys emphasise that credit supply is expected to improve moderately as banks become more willing to finance corporate and SME segments. Accelerating this supply requires confidence that automated decisions are sound.
Four governance building blocks are critical:
- Model risk management: Banks need standards for model validation, challenger testing and performance monitoring. Recent lending surveys note that banks are already engaging in more intensive monitoring and analysis. AI models must be documented, their inputs and outputs tracked, and thresholds established for intervention.
- Data governance: Data lineage and quality checks ensure that information used for lending decisions is reliable. In a region where registries differ by country and data quality varies, establishing ownership and harmonising definitions is essential. Real‑time risk assessment work emphasises the importance of data aggregation and speed, not just missing information.
- Operational controls: Platforms should maintain audit trails, version policies and enforce consistent decision logic across channels. Research linking technology modernisation with strategic cost transformation underscores that operational controls make modernisation safe.
- Credit quality monitoring: Early warning triggers and intervention playbooks align with regional NPL tracking. The Vienna Initiative’s NPL Monitor underscores how macro factors influence credit quality across CESEE and the need for timely remediation.
Strategic use of AI across the credit lifecycle
AI’s strategic value lies in its ability to enhance each step of the credit lifecycle. Predictive analytics and behavioural scoring expand access to credit by drawing insight from partial data. Early warning models detect emerging distress before defaults occur. Generative tools automatically summarise financials and produce credit narratives, enabling staff to focus on exceptions. Multi‑agent architectures could eventually automate continuous KYC and suspicious‑activity reporting, though adoption remains nascent.
AI must, however, be deployed responsibly. Models should be explainable and free from bias. They should complement, not replace, human expertise. Analysts warn that integration and regulatory challenges can stall adoption, recommending a focus on high‑impact, low‑risk use cases. A disciplined approach ensures that AI adoption enhances operational efficiency and risk management rather than exposing the bank to new vulnerabilities.
Three plausible futures for AI‑enabled lending
Scenario planning helps banks prepare for different regulatory and technological outcomes. We outline three plausible futures and the implications for strategy:
Scenario A: Accelerated convergence
In this scenario, digital infrastructure across Europe becomes more harmonised. The push for pan‑European retail payment solutions and improved resilience translates into interoperable digital identity schemes and unified data standards. Lending journeys become standardised across borders, enabling end‑to‑end automation for retail and SME products. Early warning becomes a default capability rather than a specialist function, and AI‑enabled monitoring helps banks intervene proactively. Credit quality monitoring remains important for benchmarking and portfolio steering.
Scenario B: Uneven modernisation
Here, adoption of digital lending varies widely by country and segment. Surveys show that only about 28 % of lenders offer fully digital SME journeys. Some markets leap ahead, while others lag due to local regulatory, cultural or economic constraints. Banks must operate multiple workflows, risking operational complexity and higher costs. Mitigation requires standardising core components – data definitions, decision logic, monitoring and audit trails – even as customer experiences differ by market. Modular platforms like ACP support this approach by enabling incremental adoption.
Scenario C: Cautious adoption and tighter constraints
In this scenario, incidents or heightened privacy concerns slow down automation. Regulators tighten oversight and banks respond by adding manual review layers and limiting AI use. Surveys demonstrate how risk perceptions drive tightening even when actual changes are modest. Even under cautious adoption, platform modernisation pays off: it reduces manual rework, ensures consistent policy enforcement and enhances auditability. Robust early warning remains essential because early intervention reduces losses regardless of automation speed.
Axe Credit Portal in strategic perspective
The Axe Credit Portal exemplifies how a modular, AI‑enabled lending platform supports multiple futures. ACP automates the full credit lifecycle – from origination through scoring, underwriting, servicing, collateral and limit management – and offers configurable AI capabilities across each stage. It includes natural‑language intake, document intelligence, automated financial spreading, peer comparison, sentiment analysis and early warning, all of which accelerate decision‑making and monitoring. Explainable models and automated audit trails align with the governance principles outlined above.
ACP’s design anticipates uneven modernisation. It supports multiple languages, currencies and regulatory requirements, allowing cross‑border groups like OTP Group to deploy a shared core while tailoring front‑ends for local markets. API‑driven integration enables banks to adopt modules gradually and integrate with legacy cores. Should convergence accelerate, the same platform can scale quickly without major rework. Should caution prevail, the platform still improves documentation, monitoring and governance.
Success stories in the region illustrate these benefits. Implementations across OTP’s subsidiaries have delivered shorter decision cycles, improved consistency and stronger audit trails. While exact metrics remain confidential, the qualitative outcomes – reduced manual effort, improved governance and faster time to “yes” – confirm that disciplined AI adoption delivers tangible value. ACP demonstrates that AI need not be a moonshot; it can be embedded in everyday processes and governed effectively.
A no‑regret strategy for CEE lenders
Regardless of which scenario unfolds, certain investments will remain valuable:
- Unify data and definitions: Harmonise data across products and countries to reduce model and reporting risk and enable scalable AI deployment. Research underscores the need for modern data capabilities to support generative AI.
- Modernise workflows with a platform: Deploy an end‑to‑end lending platform that automates routine tasks, enforces policy and produces audit trails. Surveys of digital SME adoption show that many lenders still rely on manual processes; modernisation is a pressing need.
- Deploy AI where it changes outcomes: Focus on use cases that improve credit decisioning, affordability checks and monitoring. Explainability and model governance are non‑negotiable. Surveys emphasising tightened standards imply that AI must support prudent underwriting, not just speed.
- Strengthen early warning and remediation: Build machine‑learning pipelines that detect emerging risks and trigger intervention playbooks. As the Vienna Initiative’s NPL Monitor shows, early action limits losses across credit cycles.
Conclusion
AI and digital lending are strategic imperatives for banks across the CEE/SEE/Baltics region, but they must be approached with discipline. A well‑designed governance framework, combined with scenario planning and modular platforms, allows banks to harness AI’s potential while managing risk. The coming years will test resilience as macro scenarios diverge and regulatory expectations rise. Lenders that invest in unified data, automated workflows, explainable AI and early warning will be ready to adapt – whether the future brings accelerated convergence, uneven modernisation or cautious adoption. In this landscape, platforms like Axe Credit Portal provide both the flexibility and the governance required to turn AI into a lasting strategic advantage.
References
- European Banking Authority: Risk Assessment Report – Autumn 2025
- European Investment Bank: CESEE Banking Lending Survey 2025 H1
- Raiffeisen Research: CEE Banking Sector Report 2024
- Deloitte: Banking Industry Outlook 2026
- Deloitte: Agentic AI in Banking
- CEE, SEE & Baltics Summit 2025 – Key Takeaways
- European Central Bank: Euro area bank lending survey Q3 2025
- Vienna Initiative: NPL Monitor H2 2024
- Axe Finance: AI‑based lending solution