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In our recent blog post, “Why ‘No Code’ Wasn’t Enough: Lessons from Country Risk Modelling” (Link), we detailed our journey in developing the ESG Index Builder—a tool that enables users to quickly construct ESG country indices, compare countries, and track trends over time. We also highlighted some of the limitations and challenges we encountered during this process.

Additionally, we have updated the underlying datasets powering the ESG Index Builder. The latest data is now accessible and extends the timeline to the end of 2024. Although the tool’s methodology and structure remain the same, the updated data enhances the platform’s relevance and accuracy.

You can access the ESG Index Builder through the CountryRisk.io Lab here (Link). We encourage you to explore it and, as always, we welcome your feedback.

This post explores how Anti-Money Laundering (AML) country risk ratings help assess money laundering and terrorist financing (ML/TF) threats across jurisdictions. It explains how the Financial Action Task Force (FATF) and the European Commission classify high-risk countries through their black and grey lists, based on weaknesses in legal, regulatory, and enforcement frameworks. The analysis highlights recurring deficiencies such as poor beneficial ownership transparency, weak financial supervision, and limited international cooperation. Empirical data confirms that blacklisted and grey-listed countries often exhibit high corruption, low rule of law, and elevated proliferation financing risks.

Money Laundering: High-Risk Country Classifications

How to Transform IMF WEO Data into Actionable Insights with CountryData.io

Every April and October, the IMF releases its World Economic Outlook (WEO) database—covering 44 macroeconomic indicators and forecasts for nearly 200 countries. This blog post shows how analysts can efficiently convert the raw WEO spreadsheet into structured, ready-to-use data using CountryData.io, our internal data management platform. In just three steps—update the WEO download URL, trigger an automated import, and review data before publishing—CountryData.io ensures high-quality datasets for sovereign risk models and ESG scoring. With API integration and scheduled updates, it’s the ideal solution for macroeconomic analysts and risk professionals seeking reliable, time-saving data workflows.

IMF World Economic Outlook Update—and How CountryData.io Simplifies Data Management

Low-Code vs. Flexibility: Lessons from Building ESG Risk Models at CountryRisk.io

Low-code and no-code tools promise easy country risk model creation, but real-world experience reveals their limitations. In this blog, CountryRisk.io explores why building robust ESG and sovereign risk models requires more than drag-and-drop interfaces. Through insights from our ESG Index Builder, we highlight the importance of indicator understanding, flexible data transformations, and thoughtful model design. We contrast our approach with tools like the World Bank’s Score Builder, explain challenges like symmetric transformations and misleading indicators, and share core principles such as automated deployment and API access via CountryData.io. While GenAI offers valuable support, it can’t yet replace expert-driven modeling.

Discover how CountryRisk.io simplifies country risk analysis—without compromising on analytical depth.

Why ‘No Code’ Wasn’t Enough: Lessons from Country Risk Modelling

As generative AI and machine learning continue to advance and data becomes increasingly accessible, there is a pressing need for adaptation alongside significant opportunities for the roles of economists and sovereign analysts.

At CountryRisk.io, we anticipate that the four primary competencies and responsibilities will include:

1.        Data sourcing: Professionals must extend beyond commonly available datasets and actively seek out and evaluate alternative and niche datasets to maintain a competitive advantage. They should also possess the capability to efficiently transform unstructured data into structured formats.

2.        Knowledge creation through quantitative modeling and the production of well-crafted, opinionated reports will be essential. The repetitive task of fact recycling can be delegated to AI.

3.        Knowledge management should occur within dedicated systems rather than on shared drives. This approach will unlock the potential inherent in individual or organizational digital twins.

4.        Communication, whether through group presentations or one-on-one interactions, has always been vital and will remain so in the future. The emotional connection with a trusted expert will continue to hold significance. However, we can expect substantial changes in engagement methods. Audiences will not only attend presentations and read reports but will also interact with our digital twins – the ultimate sparring partner – to pose questions, access curated dashboards, or utilize APIs.

How do you envision the evolution of these roles? Is your department still operating as it did a decade ago?

What will the future job profile of economists be

What will the future job profile of a macroeconomist or sovereign risk analyst entail?

Which Indicators Signal Sovereign Default Risk? A Data-Driven Analysis

What are the best indicators for predicting sovereign default risk? In this blog post, we explore the macroeconomic and governance indicators most closely correlated with sovereign credit ratings and default events. Using data from over 3,800 rating observations and inputs from S&P and Fitch, we identify key solvency and liquidity indicators—such as GDP growth, fiscal deficits, interest payments, and governance quality—that help differentiate between stable countries and those on the brink of debt distress. Our analysis, based on CountryRisk.io’s sovereign risk model, reveals how these indicators behave in the lead-up to defaults, offering valuable insights for risk managers, economists, and investors. Learn how tracking structural fundamentals and short-term signals together provides a more complete picture of sovereign creditworthiness.

How we assess country risk Part 2: The search for relevant explanatory factors

Inside CountryRisk.io: Tools and Models for Smarter Country Risk Analysis

At CountryRisk.io, we combine deep domain expertise with advanced data analytics and user-friendly tools to help analysts make better sovereign and country risk decisions. This blog explores our core modeling principles and highlights key tools in our platform—including our sovereign rating model, risk score models (ESG, AML, supply chain), Early Warning System for currency crises, and sentiment analysis powered by a custom-trained classifier. We also dive into how large language models (LLMs) enhance interactions with risk data and support intelligent summarization of research content. All tools are built on CountryData.io, our in-house data infrastructure that ensures clean, reliable, and scalable data. From automation to AI, learn how CountryRisk.io streamlines country risk workflows for professionals across finance, policy, and compliance.

What we mean when we say, CountryRisk.io is a data-driven organization

In this blog post, we want to share some information on how accessible the various government agencies is from a technical point of view. Why does this matter? Because as many diligent analysts would concur, visiting a website that doesn’t take ages to load makes the analysis process agreeable and efficient. The analytical process starts with a basic understanding of statistics and policies. Again, in our experience when an investment case has to be made, we need to be able to access, verify and crunch data fast. Access to information – and the speed in which one can do this – is essential.

Webinar: Sovereign debt in frontier countries and the role of credit ratings

How CountryRisk.io Uses AI Agents to Transform Country Risk Analysis

In this third installment of CountryRisk.io’s AI blog series, we explore how AI agents—called “helpers”—are revolutionizing the way country risk reports are produced. Building on earlier integrations like AI-generated risk summaries and PDF report analysis, the platform now features a coordinated system of AI helpers including an Orchestrator, Researcher, Librarian, Data Wizard, Writer, and more. These agents collaborate to streamline tasks like data extraction, report writing, and news summarization, significantly enhancing analyst productivity while preserving the essential creative and strategic role of human experts. CountryRisk.io’s ultimate goal: generate high-quality, end-to-end country risk reports powered by AI—with human oversight. This blog highlights practical use cases, including sovereign risk scoring, structured report generation, and real-time news integration, marking a major step forward in AI-assisted economic analysis.

How to Estimate Country Risk Premiums for International Investment Decisions

Foreign direct investment reached nearly USD 1.5 trillion in 2022, yet deciding where to invest remains complex—especially when quantifying country risk premiums for valuation models. This blog explores three widely used, publicly available sources: Damodaran’s default spread estimates, OECD’s risk classifications and minimum premium rates, and survey-based data by Pablo Fernandez. Each method offers different perspectives—sovereign credit risk, transfer and convertibility risk, and market expectations. The post emphasizes the need for a company-specific definition of country risk to guide investment analysis, benchmarking, and decision-making. Read on to understand how to combine these approaches into actionable risk premia and improve your foreign investment evaluations.

What It Takes to Get a Sovereign Credit Rating: Key Fundamentals Explained

Sovereign credit ratings are essential for countries seeking access to global capital markets, yet around 50 nations remain unrated. This blog post explores what it takes to obtain a maiden sovereign rating, focusing on the macroeconomic fundamentals most commonly considered by major rating agencies like Moody’s, S&P, and Fitch. Using historical trends and peer benchmarks, we highlight key indicators—such as GDP per capita, government debt levels, and governance metrics—that influence a country’s initial rating. Featuring CountryRisk.io’s Shadow Rating model, we illustrate how countries like Algeria may be close to qualifying, while others, like Gambia, face more significant challenges. This is the first in a series unpacking the journey toward sovereign ratings and the strategic steps needed for market readiness.

Checklist for the path to a sovereign credit rating

Wolfsberg Group’s 2024 AML Country Risk FAQ: Key Takeaways and How CountryRisk.io Aligns

The Wolfsberg Group has released updated FAQs on country risk assessments within AML/CFT frameworks, offering essential guidance for financial institutions. This blog post breaks down the key insights—such as how to define Financial Crime Country Risk (FCCR), select relevant data sources, and update risk ratings—and explains how CountryRisk.io’s AML country risk score methodology aligns with these best practices. With monthly updates, robust data pipelines, transparent methodologies, and API-driven delivery, CountryRisk.io empowers compliance teams with dynamic, tailored, and scalable country risk solutions. Learn how to enhance your AML compliance with data you can trust.

Some observations on “Wolfsberg Group: Country Risk Frequently Asked Questions 2024”

In this webinar, Parisa Kamali from the International Monetary Fund, presents the recently-launched tool PortWatch. It offers cutting edge analytical tools to help users assess the domestic and international trade impact of actual and future disruptions, brought by extreme weather and geopolitical events. Building on extensive research, PortWatch uses satellite-based vessel data and big data analytics to produce actionable insights for policymakers, international organizations, and the public.

Webinar: The Role of Sovereign Green Bonds in Chile's Development

Part 1: Search for sovereign default data

How to Predict Sovereign Defaults: Indicators, Data Challenges, and the CountryRisk.io Approach

Why do countries default on their debt, and how can we predict it? In this post, we explore how sovereign credit risk can be modeled by identifying key macroeconomic indicators that precede defaults. Using a regime-based approach—distinguishing tranquil, pre-default, and default periods—we analyze data from rating agencies and academic sources to build a comprehensive sovereign default database. While official datasets from Fitch, Moody’s, and S&P are limited in scope and duration, platforms like CountryData.io help us consolidate and standardize broader historical datasets for more accurate forecasting. We also share how these insights feed into the CountryRisk.io Sovereign Risk Score and early warning system, offering analysts powerful tools for monitoring credit risk across 200+ countries.

Webinar: Sub-Saharan Africa’s Risk Perception Premium: In the Search of Missing Factors

In this blog post, we share the details of the data pipeline used for CountryData.io.  

Breaking the Data Wrangler’s Code: Our Data’s Biggest Secrets Finally Revealed

And now, for your next trick…: A new ability for your AI Country Risk Assistant

Webinar: Your AI Country Risk Assistant: Accelerating your productivity, delivering value

Insights

ESG Index Builder – Data Update