At the same time, a quiet revolution is unfolding. Artificial intelligence (AI) and digital innovation are no longer distant buzzwords—they are shaping how loans are approved, risks are assessed, and customers are served. Instead of waiting days for an application to be processed, clients can increasingly get a credit decision in seconds. Instead of relying on broad assumptions about borrower health, banks now scan thousands of real-time data points to predict repayment ability before problems arise.
This shift matters because lending is still the heartbeat of banking in CEE. Unlike in Western Europe, where companies often raise funds through capital markets, most businesses and households in CEE depend directly on banks. The ability to lend smarter—not just more—is becoming the dividing line between banks that thrive and those that struggle.
Against this backdrop, AI isn’t just a new tool. It’s becoming the nervous system of modern lending: quietly scanning, learning, and guiding decisions. And in a region where the banking landscape is less weighed down by legacy IT, CEE lenders have the chance to leapfrog
The CEE Banking Backdrop – Fragility Meets Opportunity
Central and Eastern Europe has long been a region of contrasts in banking. On the one hand, you see fast-growing economies, entrepreneurial energy, and banks that have historically shown impressive adaptability. On the other, the sector carries structural fragilities: heavy reliance on bank lending, uneven access to capital, and the shadow of past crises that still shapes risk appetite.
Over the past few years, these contrasts have sharpened. Slower growth, tighter liquidity, and rising credit risks have left banks more cautious. Lending conditions are tougher, not just because regulators demand it, but because balance sheets demand it. Businesses are postponing investments, households are borrowing less, and in some countries, deposits are shifting faster than banks would like.
And yet, it’s in moments like these that opportunity often surfaces. The same pressures that squeeze margins also force banks to rethink how they operate. What worked in a high-growth, low-risk environment no longer holds. Efficiency, precision, and customer trust are no longer optional—they are survival lines.
- Efficiency: every decision to automate back-office processes frees up capital and talent for growth areas.
- Precision: smarter credit risk models reduce losses when non-performing loans rise.
- Trust: when customers face uncertainty, they value speed, clarity, and fairness more than ever.
This is the backdrop against which AI enters the stage. It’s not arriving into a vacuum, but into a region that knows both the pain of volatility and the promise of reinvention. The question now is not whether AI has a role in CEE banking— but how quickly banks can adapt and embed it in their core.
Why AI Matters in Lending – From Gut Feeling to Data-Driven Precision
For years, many lending decisions relied on a mix of financial statements, experience, and intuition. In 2025, that approach is running out of road. Economic conditions are uneven, risks can surface quickly, and borrowers’ situations change faster than annual reviews can catch. Artificial intelligence (AI) shifts the centre of gravity: from slow, manual checks to timely, data-driven calls that improve both speed and accuracy.
The European Banking Authority (EBA) notes that European banks are increasingly applying AI methods (e.g., machine learning, natural-language processing) in credit scoring, creditworthiness assessment, fraud detection, and compliance. In plain terms, this means banks can now process more information, more often, to form a clearer picture of each borrower’s ability to repay—before problems arise.
In Central and Eastern Europe, AI is particularly powerful because it compresses time. Loan applications that once took days can now be assessed in seconds. Instead of relying solely on static documents (like last year’s accounts), AI models can ingest live data such as account transactions, invoice flows, and payment behaviour. That helps lenders offer faster decisions to good customers—and spot early warning signs in vulnerable ones.
This isn’t just theory. Banks in the region are experimenting with practical AI use cases that bring a direct edge. For example, Raiffeisen Bank International describes using natural-language processing to turn unstructured information (like central-bank communications) into market indicators that can inform risk appetite and pricing. The takeaway: AI helps translate noisy, fast-moving signals into decision-ready insights.
Three capabilities are changing day-to-day lending work:
- Real-time credit decisions: AI evaluates thousands of data points (from bank transactions to verified third-party data) in moments, enabling quicker approvals for viable borrowers and faster declines where risk is high.
- Continuous risk monitoring: Instead of “set-and-forget” annual reviews, models track borrower health throughout the life of the loan, flagging anomalies (missed payments, cash-flow declines) for early intervention. The EBA highlights this shift toward ongoing surveillance as a key benefit—and a governance challenge to get right.
- Personalised pricing and journeys: By aligning price with risk in near-real time, lenders can remain competitive without overexposing the balance sheet. Digital assistants and pre-filled forms reduce friction for customers, particularly SMEs with limited time and resources.
With opportunity comes responsibility. The EBA stresses the need for robust governance: high-quality data, model validation, explainability, bias management, and clear human accountability. In practice, that means establishing a controlled AI lifecycle—documented development, testing, monitoring, and fallback procedures—so that speed never outruns sound risk management.
For CEE banks, the prize is twofold: better risk outcomes (fewer surprises, earlier fixes) and better customer outcomes (faster answers, fairer pricing, simpler onboarding). In a market where many borrowers depend directly on banks rather than capital markets, AI isn’t a shiny add-on—it’s the engine that makes lending smarter, safer, and more resilient.
Bypassing Legacy Constraints: Digital Transformation in CEE
Not every bank in Central and Eastern Europe carries decades of layered, hard-to-untangle IT. That constraint, common in older markets, can be an advantage for CEE lenders: they can move straight to cloud-native, AI-first architectures without years of remediation. In practice, that means fewer hand-offs, fewer spreadsheets, and far more automation from day one.
The playbook is simple: consolidate data, automate the routine, and keep humans focused on judgement. Modern lending stacks bring onboarding, credit assessment, decisioning, and monitoring into a single flow. As banks scale this model, credit policies become executable code rather than PDFs on a shared drive, making risk controls consistent and auditable.
These shifts are not happening in a vacuum. Supervisors and industry bodies are watching AI adoption closely, encouraging innovation but insisting on robustness. The European Banking Authority highlights the importance of governance, data quality, and explainability in AI systems; getting those foundations right lets banks digitise faster and safer. Meanwhile, regional forums such as the CEE & Baltics Summit underline the twin priorities of operational resilience and customer trust as digital transformation accelerates.
For CEE banks, the outcome is a rare chance to leap ahead: fewer legacy bottlenecks, cleaner data pipelines, and a lending operation that is built for speed without sacrificing control.
AI in Action – Smarter Risk and Better Customer Journeys
AI adds value when it is woven into everyday work. In lending, that means three places: deciding who to lend to, pricing risk fairly, and supporting customers before, during, and after a loan.
On the risk side, models continuously read signals from account activity, invoices, and payment behaviour to spot problems early. The EBA points to growing use of machine-learning techniques in credit scoring and creditworthiness—useful not just for approvals, but for ongoing vigilance across portfolios.
Customer journeys are evolving too. Routine questions and form-filling can be automated, while complex conversations go to human teams. This “human-in-the-loop” approach keeps empathy in the process and moves repetitive work to software. Industry guidance summarised by IBM’s AI in Banking hub outlines common applications—intelligent document processing, risk alerts, and digital assistants—that reduce friction and shorten time-to-yes without loosening controls.
There is also room for creativity. For example, Raiffeisen Bank International describes using natural-language processing to turn unstructured content (like central-bank communications) into market indicators that inform credit appetite and pricing—precise signals that would be hard to extract at scale by hand.
Threaded through all of this is governance. Banks that document model choices, track data lineage, and explain outcomes will move faster than those that bolt on controls later. That discipline builds confidence with customers and supervisors—and keeps AI implementations sustainable.
Use-Case Map: Where AI Creates Value in Lending (Guided by McKinsey)
Beyond pilots, banks need a systematic map of where AI moves the needle. Drawing on industry guidance such as McKinsey’s “Extracting value from AI in banking”, lenders can prioritise a handful of high-impact domains and wire them end-to-end for results.
Risk & underwriting
- Customer underwriting: ML models blend financials with live signals (cash-flow volatility, invoice flows) to predict default early and lift approval speed without diluting standards.
- Risk-based pricing: Real-time risk curves and elasticity models set prices per borrower/segment, protecting margins as conditions shift.
- Portfolio optimisation & monitoring: Continuous surveillance flags concentration risk by sector, tenor, or collateral, triggering limit changes before losses stack up.
- Collections: Propensity-to-pay and next-best-action engines tailor outreach, improving roll-rate cures and lowering cost to collect.
Servicing & operations
- Self-service via digital channels: Document capture, verification, and status updates reduce call-centre load and shorten time-to-cash for SMEs.
- Middle/back-office automation: Intelligent document processing (IDP) and workflow orchestration cut exceptions, improve SLA adherence, and create clean audit trails.
- Complaints management: NLP triages cases, detects themes (e.g., fee disputes, servicing delays), and routes remediation with traceable turnaround.
Sales & relationship management
- Digital-led acquisition: Look-alike modelling and eligibility pre-checks reduce CAC and lift conversion for micro-SME and consumer lending.
- Frontline enablement: AI copilot surfaces next-best product, risk alerts, and pricing guardrails inside the RM’s daily tools.
- Engagement & retention: Churn-risk detection and hyper-personalised offers defend deposit stickiness and cross-sell with transparency.
Controls & insight
- Regulatory compliance & controls: Embedded checks (KYC/AML, ESG disclosures) run inline with decisions, reducing rework and audit findings.
- Business intelligence & analytics: Unified metrics (approval times, PD/LGD drift, cure rates) drive weekly governance and model recalibration.
The common thread: pick a domain, wire the journey end-to-end, and attach clear KPIs (e.g., minutes to decision, percentage auto-approved within policy, roll-rate improvement, cure rate uplift, net interest margin preservation). Then scale the pattern to adjacent subdomains. That’s how AI moves from isolated wins to durable balance-sheet impact.
The role of the credit manager is on the brink of a major transformation. For decades, much of their work revolved around manual reviews: checking loan applications, chasing documents, and navigating fragmented systems to piece together a borrower’s profile. In 2025 and beyond, AI is stripping away those inefficiencies and repositioning the credit manager at the heart of strategy rather than administration.
AI copilots now assist managers by summarising insights, preparing next steps, and even drafting credit offers in real time. AI agents automate repetitive but critical processes such as document collection, authentication, compliance verification, and collateral evaluation. Meanwhile, predictive credit models bring an entirely new layer of foresight, analysing structured and unstructured data to spot risks before they appear on the balance sheet.
What AI Handles
- Plan and monitor tasks during the credit underwriting process (AI orchestrator)
- Check loan applications (AI agent)
- Collect, extract, and classify documents (IDs, passports, pay slips, financial spreadsheets) (AI agent)
- Authenticate documents and extract insights (AI agent)
- Evaluate collateral documents (AI agent)
- Assess credit using unstructured data and generate loan offers (predictive credit model)
- Check internal rules, policies, and compliance terms; prepare credit memos and contracts (AI agent)
Where Credit Managers Add Value
- Review AI agents’ output for accuracy and alignment with policy
- Hold discussions with customers to understand context and needs
- Summarise insights, actions, and next steps
- Visit and inspect sites when physical validation is required
- Present credit offers and explain terms clearly
- Maintain ongoing client engagement through questions and reminders
This division of labour means machines handle the heavy lifting of data and process, while human managers focus on what they do best: applying judgement, building trust with clients, and tailoring solutions to the unique context of each borrower. Instead of being processors of applications, credit managers become advisors and strategists.
The Gains Are Substantial
- Efficiency: underwriting cycles shrink from weeks to hours as manual bottlenecks vanish.
- Effectiveness: decisions become sharper, blending the scale of AI-driven analytics with the nuance of human expertise.
- Resilience: early detection and proactive restructuring strengthen portfolios against future shocks.
In this AI-powered model, the credit manager’s role doesn’t disappear—it evolves. Human expertise is placed where it matters most: balancing growth and risk, safeguarding resilience, and ensuring that lending in CEE remains both profitable and sustainable.
Future-Proofing 2026 and Beyond – Trust, Compliance, and Growth
AI is not a side project; it is the operating system of the next lending cycle. To prepare for 2026 and beyond, banks need more than pilots. They need a repeatable engine: clear ownership, monitored models, and processes that evolve with regulation and the economy.
A practical roadmap usually includes:
- Unified data foundation: one customer view across onboarding, underwriting, and monitoring, so signals are consistent and auditable.
- Model lifecycle management: versioning, back-testing, bias checks, and performance alerts—aligned with EBA expectations.
- Explainability by design: plain-language reasons for decisions, accessible to customers, relationship managers, and risk teams.
- Operational resilience: secure, cloud-ready stacks with failovers and incident playbooks; sector discussions (e.g., at the CEE & Baltics Summit) stress that cyber and continuity are as critical as capital ratios.
- Human-centred delivery: AI augments judgement; it doesn’t replace it. Training front-line teams to work with model insights turns tools into outcomes.
For CEE lenders—serving markets where many borrowers rely on banks over capital markets—this approach compounds benefits: fewer surprises in risk, faster answers for customers, and stronger capacity to grow when conditions improve. That is how AI moves from buzzword to balance-sheet: through disciplined, human-centred execution, backed by credible model governance and modern architecture.