PwC conducted a credit risk maturity survey of European banks, covering 35 banks across 13 countries, including Finland. The survey’s key finding was that credit risk management is evolving from a regulatory-driven function based on backward-looking analysis into a more proactive, steering and forward-looking unit. Compared with their European counterparts, Nordic financial institutions appear to be slightly behind in this transition. The advantage, however, is that Finnish financial institutions can leverage lessons learned in larger markets to guide their own initiatives.
Credit risk management is not a stand-alone regulatory, modelling or data-driven topic. It reflects a broader change in what credit risk management is expected to do: move away from periodic, point-in-time reviews towards more continuous monitoring and steering. That shift helps banks anticipate what could happen next, not just explain what has already happened. It also encourages risk, finance and business to work from a more consistent view of the portfolio, key clients and credit decisions.
The survey identifies key areas where banks are improving their credit risk management: stronger data integration and automation, better early warning systems (EWS), and improved monitoring and scenario capabilities. These concepts are key while transitioning to a forward-looking approach.
Forward-looking credit risk management is then implemented in concrete terms by considering the following three elements holistically:
- Management information and scenario analysis
- Early warning systems
- Future scenarios embedded in credit pricing
Together, they help banks identify and respond to risk earlier and avoid taking on exposures that are not priced for the times ahead.
Why does this matter for Finnish banks?
Finland is a concentrated market and, like everywhere in Europe, credit risk is shaped by cycles and sector-specific pressures. When these conditions change, the cost of being late is high: it shows up in portfolio quality, remediation workload and the credibility of risk reporting. A forward-looking approach helps banks anticipate market developments and results in a more robust credit risk management framework.
There is also pressure from the regulator. The FIN-FSA conducted a thematic review in spring 2025 to determine which indicators banks use to assess significant increases in credit risk for financial instruments and whether the effectiveness of those indicators has been assessed through back-testing. This assessment also included a review of early warning indicators under EBA GL 2020/06.
The findings from the FIN-FSA thematic review included, for example, that the early warning indicators specifically mentioned in the EBA’s guidelines on loan origination and monitoring (2020/06) are not used, or that only certain indicators are used. The review also found that there is a lack of specific criteria for determining when the credit risk of a financial instrument has increased significantly. In addition, when using 12-month PDs (probability of default) as a proxy for lifetime PD, banks do not provide reasoning as to why a 12-month horizon is a reasonably long proxy for changes in credit risk that could occur over the instrument’s lifetime.
These findings point to a broader need to strengthen the consistency, transparency and forward-looking nature of credit risk monitoring. To address these gaps and support the earlier identification of credit deterioration, we would advise the following concrete changes to the credit risk management framework.
Management information (MI) & scenario analysis: from reporting to steering
Good MI is not just about historical KPIs. Forward-looking MI helps answer questions like: What happens if rates remain higher for longer, or if a key sector weakens? Which parts of the portfolio drive the change? And what would we do differently: tighten underwriting, adjust limits, reprice or increase monitoring? The goal is not necessarily to predict the future perfectly, but rather to consider a range of plausible future scenarios based on which strategic decisions can be made.
Early warning systems (EWS): earlier signals, clearer follow-up
EWS is often treated as a monitoring dashboard. The value lies in using predictive indicators that move before default (e.g. financial, behavioral, covenant and sector signals) and having a clear follow-up process, i.e. who reviews, how quickly, and what actions are available. Without that, EWS becomes either too late to matter or too convoluted to act on.
This is consistent with the direction of the Finnish market, as banks are increasingly prioritising EWS with predictive triggers. This is also reflected in the growing application and exploration of AI for early warning signal detection, helping banks anticipate potential credit deterioration before it materialises. Concrete examples include AI agents that use external industry data to monitor market sentiment and sector performance, or those that use internal data to track repayment trends and behavioral changes in real time.
Pricing the future instead of the past
If pricing does not reflect future risk and costs, the bank will grow in the wrong areas even with good policies. Forward-looking pricing means recognising that expected loss, structure (tenor, collateral, covenants), cost assumptions and capital usage are not necessarily constant over time and may move differently from what has been observed historically. This is especially relevant in renewals, where a significant amount of risk is repriced (or not repriced).
What would we do? - PwC approach
Transforming your approach to credit risk management should be based on a solid foundation, integrated modelling and alignment across the organisation.
From current state to ambition
Assess the current operating model of your credit risk management function, including governance, credit risk models (e.g. structure, assumptions, validation results), and data quality and availability, to identify the current state and develop a roadmap to the target state.
Improve the data foundation
Before adding complexity, ensure that the basics are reliable: consistent identifiers and exposure/obligor views across systems, good quality in key underwriting and monitoring signals, and clear definitions and lineage (where numbers come from and when they are updated). This is often the main constraint on making scenario analysis and early warning work at scale. Improving the data foundation not only strengthens current credit risk management, but also adds value keeping in the context of the ongoing AI transition.
Build a “forward-looking loop” across scenarios, EWS and pricing
The greatest value comes when these do not sit in separate boxes: scenarios highlight where risk could build up, EWS detects early movement, pricing and underwriting reflect the risk in new deals and renewals, and outcomes feed back into thresholds and calibration. Modelling and assumptions across the different components should be aligned and, where possible, consistent to ensure coherent outcomes.
How can we help?
Forward-looking credit risk management is not just a single project. It is the combination of (1) scenario-led management information, (2) early warning with clear follow-up, and (3) pricing that reflects forward-looking risk assumptions, built on a reliable data foundation and embedded into day-to-day governance.
We support banks in strengthening their credit risk data foundation, designing and calibrating early warning and monitoring frameworks, and embedding forward-looking scenario and pricing logic into credit processes, while ensuring the approach remains explainable and fit for governance.
