Forecasting Risk Trends

Forecasting in Banking: Managing Uncertain Economic Environments Forecasting in the realm of banking is far from a straightforward process. Although the ultimate objective is to arrive at the most plausible predictions possible, the ever-changing economic landscape often presents challenges that make absolute precision impossible. However, that does not mean financial institutions should shy away from attempting to create reliable forecasts. When making forecasts, it is crucial to base these predictions on prudent and conservative assumptions. Banks often rely on historical data to project future trends; although this method has its merits, especially in stable economic conditions, it may not be the most advantageous approach when the economy is in flux. It is essential to factor in the realistic possibility of economic changes, such as interest rate fluctuations or market volatility, to arrive at more robust forecasts. Scenario analysis serves as an invaluable tool for generating realistic expectations about future financial conditions. It allows treasury professionals to examine various outcomes, assessing each for its likelihood and potential impact on the bank’s finances. Scenario analysis provides the advantage of preparedness, offering a range of plausible outcomes rather than fixating on a single, ideal projection. Modern technology, e.g. data analytics and algorithms, can offer increasingly sophisticated ways to improve the accuracy of forecasting models. While technology can significantly aid in making more accurate projections, it's crucial to remember that these tools should complement, not replace, human expertise. A balanced approach, incorporating both technological solutions and skilled professional judgement, tends to yield the most beneficial results. Regulatory frameworks often require banks to maintain a certain level of forecasting accuracy to ensure stability and to protect the interests of stakeholders. Consequently, a bank should always be aware of these requirements and incorporate them into their forecasting methodologies. Regulatory compliance, although often time consuming, provides an additional layer of scrutiny that helps to improve the forecasting process. It is important to understand that forecasting is not a one-off activity. Economic conditions change, sometimes in unpredictable ways, necessitating a revisit of previous forecasts. A best practice is to schedule regular review periods where assumptions can be reassessed, and forecasts updated, to reflect the most current and accurate information available. Overall, the approach to forecasting in uncertainty should be one of cautious optimism. The goal is not necessarily to predict the future with any accuracy, but to understand a range of plausible scenarios and prepare accordingly. By doing so, banks can make more informed decisions, better manage risks, and contribute to the long-term stability and success of their financial institutions.

"Geopolitics, Geoeconomics and Risk: A Machine Learning Approach" In today's complex world, what truly drives a country's financial risk? Is it domestic policy, geopolitical flare-ups, or the pulse of global markets? I'm excited to share our new paper from BBVA Research, "Geopolitics, Geoeconomics and Risk: A Machine Learning Approach," co-authored with Tomasa Rodrigo. We developed a novel high-frequency dataset for 42 countries and used a suite of machine learning models to forecast sovereign risk with greater accuracy. Here are three key takeaways for professionals in finance, economics, and risk management: 1. Global Financial Conditions Reign Supreme 👑 Our analysis confirms the overwhelming importance of the "Global Financial Cycle." U.S. monetary policy (proxied by the 2-year Treasury yield) and global financial volatility (the VIX) are the two most dominant drivers of sovereign risk, far outweighing other factors. This underscores that global "push" factors set the baseline for risk appetite everywhere. 2. The Predictive Power of News is Non-Linear 📰 ➡️ 📈 Simply adding news-based indicators (like Geopolitical Risk or Policy Uncertainty) to traditional linear models yields only modest gains. The real breakthrough comes from using non-linear models like Random Forests, which saw forecast accuracy improve by over 20%. This proves that the value of news lies in its complex, non-linear interaction with financial conditions—something simpler models miss entirely. 3. Geopolitical Shocks are Amplifiers, Not Primary Drivers 🔊 Our scenario analysis revealed a critical dynamic: geopolitical and policy uncertainty shocks, while important, often have a manageable impact in isolation. However, their effect becomes powerfully amplified when combined with a high-volatility environment and tightening global financial conditions. This highlights the state-dependent nature of risk, where the global financial backdrop determines whether a geopolitical spark fizzles out or ignites a fire. For policymakers and investors, these findings highlight that effective risk management requires not only monitoring domestic fundamentals but also understanding the powerful, often non-linear, interplay between news-based sentiment and the global financial cycle. You can read the full paper to explore the detailed methodology, case studies (including the Russia-Ukraine war and U.S. trade policy), and network analysis. Full Paper Link at Arxiv : https://lnkd.in/dM-M8V_D Full Paper Link at BBVA: https://lnkd.in/d7wZvkZA I'd love to hear your thoughts in the comments. #Geopolitics #Geoeconomics #Finance #Economics #MachineLearning #AI #RiskManagement #DataScience #BBVAResearch #SovereignRisk

The current risk picture is saturated, constantly evolving, and on everyone’s mind. Yet traditional risk management processes often fall short—unable to capture the complex, systemic, and dynamic nature of today’s risks. While risk identification and assessment remain essential, they are increasingly undermined by uncertainty—and by our own cognitive blind spots. This is where foresight comes into the foreground. It’s a discipline designed exactly for moments like this. As a former risk professional and a co-author of FERMA | Federation of European Risk Management Associations newly released NEXT 2025 – New EXposure Trends report, I’m proud to contribute to a publication that argues not only for more long-term thinking in risk management, but shows how to get there. We explore the structural biases that hold organisations back—status quo bias, groupthink, optimism bias, and more—and highlight how strategic foresight methods like scenario planning, horizon scanning, bowtie analysis, futures wheels, and leading indicators can help Risk Managers spot what others overlook. In this first edition, we focus on four deeply interconnected, high-impact risk domains for European businesses: - Geopolitical shifts and the changing world order - Technological acceleration, particularly around AI - Climate change and its systemic implications - Human capital disruption in an aging, digitising workforce For each, we offer concrete examples of scenarios built around plausible future developments—from AI sovereignty to geopolitical fragmentation and climate cooperation breakdowns. The takeaway? The future is not something to predict, but something to prepare for. And preparation starts by confronting the uncomfortable, resisting short-termism, and building organisational cultures capable of asking “what if?” before the crisis hits. Let’s make foresight part of the risk manager’s core mandate. Dr. Sebastian Wieczorek Le Bloc-Notes de Bruno Colmant Paulino Fajardo Sean Lyons Philippe Cotelle Charlotte Hedemark Hancke Typhaine Beaupérin Copenhagen Institute for Futures Studies #RiskManagement #StrategicForesight #ScenarioPlanning

Using foresight to anticipate emerging critical risk - a Proposed methodology by OECD - OCDE The new OECD paper presents a methodology to help countries identify and characterise global emerging critical risks as part of the OECD’s Framework on the Management of Emerging Critical Risks. It supports experts and policymakers tasked with anticipating and preparing for uncertain and evolving threats that transcend traditional national boundaries. 1️⃣ The approach begins with horizon scanning to capture weak signals and unconventional data sources, including patent analysis, crowd forecasting, and the use of generative AI. 2️⃣It then applies structured foresight techniques, such as futures wheels, cross-impact analysis, and scenario-based “Risk-Worlds,” to explore how risks might manifest and interact in multiple possible future contexts. The methodology emphasises understanding risks “at source,” focusing on vulnerabilities, interconnectedness, and possible management strategies. Rather than predicting a single future, it seeks to broaden the range of possibilities, encouraging proactive adaptation, building collective understanding, and ultimately strengthening government capacity to navigate and shape an increasingly complex and uncertain global risk landscape. Kudos to Josh Polchar and OECD for putting the paper out #Foresight #Futures #Scenarios #OECD #Methodology