Risk is the foundation upon which financial institutions operate. Banks, insurance companies and investment firms thrive by assessing, pricing and managing risk. Without it, they would have no purpose – no loans would be issued, no policies written and no investments made. Taking calculated risks is how these institutions generate returns.
But it’s exactly this calculation that presents the challenge. A bank, for example, must evaluate credit risk when lending to businesses or individuals. If it’s too cautious, it misses growth opportunities; too aggressive, and it risks insolvency. Likewise, an insurance company must price its policies accurately to cover claims while remaining profitable. Investment firms constantly balance market risks to maximise client returns.
Given its importance, it’s no surprise that risk is one of Valcon’s core client value propositions. In recent years, we’ve supported several banks in refining their credit risk processes, gaining deep expertise in credit assessment, risk modelling, risk monitoring, data sourcing and reporting.
Looking ahead, we see three major challenges shaping the credit risk domain:
Climate risk
Climate change poses one of the most significant challenges of our time, with wide-reaching implications for the global economy and the financial resilience of nations, businesses and individuals.
In 2024, the European Central Bank (ECB) published its Guide to Internal Models, which outlines how institutions should incorporate climate and environmental risks into their models. Financial institutions need to account for two key types of climate risk:
- Physical climate risk – the impact of climate-related disasters on assets such as infrastructure, equipment and workforce
- Transition risk – the impact of regulatory changes, technology shifts, consumer sentiment and investor expectations related to the shift toward a low-carbon economy
Valcon has helped clients integrate both types of climate risk into their risk landscapes, enabling them to manage exposures while identifying opportunities during the climate transition.
Adoption of new risk modelling technologies
The rise of artificial intelligence (AI) and advanced analytics is reshaping risk modelling. To remain competitive, banks must leverage machine learning and large, complex datasets to identify emerging risk patterns.
However, many Dutch banks still rely on outdated, on-premise models and legacy tools. These systems are costly to maintain, lack flexibility, depend on increasingly rare skill sets and offer poor user experiences.
This challenge underscores the need for organisations to scale up data processing capabilities, centralise data repositories and strengthen computing power to effectively deploy machine learning risk models. Achieving this often requires significant investment in infrastructure and technology upgrades.
The shift to more advanced analytical methods also typically involves adopting cloud-based solutions to meet increasing computational demands. However, this brings added complexity – cloud environments offer flexibility but can lead to unpredictable costs, making computational efficiency a growing priority.
Valcon has extensive experience in helping clients implement state-of-the-art machine learning platforms and optimise performance to manage rising compute costs.
Risk data aggregation and reporting (RDARR) compliance
The European Central Bank’s latest progress report revealed that none of the global systemically important banks have fully implemented the BCBS 239 risk data aggregation and risk reporting principles, published in 2013. These principles are designed to strengthen data management for risk monitoring and reporting – crucial for timely risk identification, regulatory compliance and proactive mitigation.
As a result, the ECB continues to push for adherence, most recently through its Guide on Effective Risk Data Aggregation and Risk Reporting (RDARR), which sets out expectations and areas for improvement.
Achieving compliance remains a significant challenge. Institutions must establish strong organisational ownership and governance, build robust data architecture and IT infrastructure, and improve data quality across systems.
When data must be structured and made accessible, the integration process is often complex. Valcon supports clients through this process with deep expertise in data engineering and modelling – helping ensure successful integration while tackling master data management and data quality issues.
Reach out
At Valcon, we’re committed to helping banks navigate this complex and evolving landscape. If you’re facing any of these challenges – or simply want to explore how to turn them into competitive advantages – please get in touch with me at [email protected]. I’d love to exchange ideas.
