Artificial intelligence is no longer a future discussion for banks across Central and Eastern Europe. It is becoming part of day-to-day lending operations.
What has changed over the past 18 months is not simply the maturity of AI technology. It is the pressure on banks to improve efficiency, accelerate decision-making, strengthen risk oversight, and modernise customer experience at the same time.
Across the CEE region, many institutions are dealing with a combination of slower economic growth, margin pressure, higher operational costs, increasing regulatory expectations, legacy infrastructure challenges, and growing competition from digital lenders and fintechs.
For executives leading lending transformation initiatives, the question is no longer whether AI should be adopted.
The real question is: how can banks operationalise AI in lending while maintaining governance, risk discipline, and profitability?
AI in banking is moving from experimentation to operations
Many banks globally have already tested AI capabilities in underwriting, customer onboarding, fraud detection, and collections.
However, according to McKinsey & Company, banks are under rising pressure to secure productivity gains from AI as revenue growth moderates and technology-led competition intensifies.
The banks creating the strongest outcomes are focusing less on isolated AI pilots and more on operational integration. This shift is becoming increasingly visible across CEE markets.
Unified lending ecosystems
Rather than deploying standalone AI tools, banks are looking for unified lending ecosystems capable of supporting origination, scoring, decisioning, portfolio monitoring, collections, provisioning, and regulatory reporting.
Fragmentation still blocks scale
Deloitte frames the move from testing AI to deploying it smoothly into operations as the real scaling challenge.
This is especially relevant in Central and Eastern Europe, where many banking groups still operate with multiple legacy systems, siloed risk and collections environments, manual approval processes, disconnected customer data, and cross border operational structures.
In practice, these inefficiencies slow decision making and reduce visibility across the credit lifecycle.
What banking executives in CEE are asking right now
1. Can AI improve lending efficiency without increasing risk?
This is one of the most important concerns among C level executives. The answer depends entirely on how AI is implemented.
Banks seeing measurable results are not replacing governance with automation. They are strengthening governance through automation.
Where AI is being used
According to the McKinsey Global Banking Annual Review, productivity improvement is becoming a strategic priority as global banking revenue growth slows and operational pressure increases.
- Reduce manual underwriting tasks
- Accelerate loan processing
- Improve application quality checks
- Detect inconsistencies in borrower documentation
- Automate repetitive operational workflows
- Improve consistency in credit decisions
This is particularly valuable for CEE banks managing large SME portfolios where operational efficiency and turnaround time directly affect competitiveness.
The objective is not to remove human decision making. The objective is to allow credit teams to focus on higher value analysis instead of repetitive operational work.
2. How do banks use AI while remaining compliant?
This concern is becoming even more important across Europe due to evolving regulatory frameworks and the introduction of the EU AI Act.
Executives are increasingly cautious about black box AI models that cannot explain how lending decisions are made. For banks, explainability is no longer optional.
Control
- Full auditability
- Decision traceability
- Human oversight
Policy
- Configurable approval policies
- Regulatory reporting
- Transparent scoring logic
Architecture
This is especially important for banks operating across multiple jurisdictions in the CEE region.
According to the PwC Global Banking Risk Study 2025, governance and operational resilience remain among the top priorities for banking executives implementing AI capabilities.
Axe Finance has positioned its Axe Credit Portal (ACP) around this operational model by combining AI powered automation with configurable workflows, explainable decisioning, and end to end credit lifecycle management.
3. Why is AI becoming important for portfolio monitoring?
One of the biggest shifts happening in lending is the move from reactive portfolio management toward predictive portfolio monitoring. Traditional monitoring models often identify risk too late.
AI allows banks to detect early behavioural patterns that may indicate deterioration before delinquency becomes visible.
McKinsey Risk & Resilience highlights the role of risk data, digitisation, and analytics engines in faster credit decision-making and stronger monitoring.
- Exposure concentration
- Payment behaviour anomalies
- Covenant breaches
- Collateral deterioration
- Early delinquency indicators
- Sector based portfolio stress
This is particularly relevant across CEE markets where economic volatility, inflation pressure, and sector concentration risks continue affecting SME and commercial lending portfolios.
Executives are no longer asking whether AI can support portfolio management. They are asking how quickly it can be integrated into operational decision making.
4. Why collections and provisioning are becoming strategic priorities
Collections and provisioning are rapidly becoming major focus areas for AI adoption in lending.
Many banks still operate collections separately from origination and risk systems, limiting visibility across the full customer lifecycle.
Where inefficiency appears
- Delayed intervention
- Weak prioritisation strategies
- Limited recovery forecasting
- Inconsistent customer segmentation
- Reduced provisioning accuracy
Where AI helps
- Recovery prioritisation
- Dynamic segmentation
- Collection strategy recommendations
- Portfolio forecasting
- Provisioning calculations
- Early warning identification
ACP Collection & Provisioning enables lenders to improve debt recovery management, automate operational workflows, and strengthen portfolio visibility across the collections lifecycle.
Similarly, ACP Loan Collectors supports more structured and efficient collection management processes through workflow automation and intelligent operational monitoring.
For many institutions across Central and Eastern Europe, these capabilities are becoming increasingly important as cost of risk remains under pressure.
The future of AI in lending across CEE
The banks that will lead lending transformation in the CEE region over the next few years are unlikely to be the ones deploying the largest number of AI initiatives.
They will be the institutions applying AI pragmatically to improve operational performance, strengthen governance, and simplify lending processes.
According to BCG, meaningful returns come when banks move beyond experimentation and scale proven use cases with traceability, audit trails, and human-in-the-loop controls.
That shift is already visible across lending.
- Accelerate decision making
- Improve operational efficiency
- Strengthen portfolio monitoring
- Enhance collections performance
- Maintain regulatory compliance
- Deliver more consistent customer experiences
For executives across Central and Eastern Europe, the challenge is no longer understanding the potential of AI. The challenge is building lending operations capable of scaling it effectively.
That requires more than isolated automation projects. It requires integrated lending ecosystems designed around efficiency, governance, portfolio visibility, and long term operational resilience.
That transition is already underway across the region.
References
- McKinsey & Company, Global Banking Annual Review 2025
- Deloitte, Scaling Artificial Intelligence: Navigating the Horizons of Innovation
- European Commission, EU AI Act regulatory framework
- PwC, Global Banking Risk Study 2025
- BCG, A Faster Path to Scaling GenAI in Banking Compliance
- Axe Finance, ACP AI-powered Lending
- Axe Finance, ACP Collection & Provisioning