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This article examines how trade flow data and AI-driven analysis on the CountryRisk.io Insights platform can help detect sanction evasion patterns, shifting trade routes, and emerging AML country risk hotspots.

This article explores how trade flow data can reveal patterns of sanction evasion and money-laundering risk in the aftermath of Russia-related sanctions. Using recent export data from Germany to the Kyrgyz Republic and other neighbouring countries, the analysis highlights how shifting trade routes can signal potential evasion channels. It also examines the growing role of China and India in Russia’s trade network and demonstrates how combining data analytics with AI tools on the CountryRisk.io Insights platform can strengthen AML country risk monitoring and compliance strategies.

Spotting Sanction Evasion and Spillover Risks Through Trade Flows

Productivity in Sovereign Analysis: AI That Actually Works

This post shows how domain-specific AI tools can dramatically streamline sovereign credit rating analysis, cutting a multi-hour monthly ratings review to just minutes. By combining structured data, specialised prompts, and automated research workflows, analysts can boost productivity and improve consistency in sovereign risk monitoring.

The News Research Tool in CountryRisk.io Insights instantly delivers and summarizes the latest global developments, helping analysts stay informed and save hours of research time.

Stay Informed with the News Research Tool in the CountryRisk.io AI Assistant

Introducing the AI Assistant — Your Smart Research Partner on the CountryRisk.io Insights Platform

Reflections on Building Two Ventures in Parallel

Reflections on building two ventures in parallel: how ZOA Legal and CountryRisk.io tackle legal and sovereign risk challenges, create synergies, adapt to AI disruption, and grow as innovative B2B SaaS platforms.

The new CountryRisk.io DSA Toolkit brings a modern, flexible, and programmable approach to sovereign debt sustainability analysis. Fully integrated with CountryData.io, the toolkit enables both deterministic and stochastic simulations, scenario analysis, and optimization of fiscal paths. Unlike the traditional IMF/World Bank Excel templates, it is Python-based, API-accessible, and scenario-driven, making it ideal for government debt offices, investors, and risk analysts. With AI-powered research automation and seamless workflows, CountryRisk.io helps professionals assess debt risks, run stress tests, and evaluate policy options with precision.

Introducing the CountryRisk.io Debt Sustainability Analysis (DSA) Toolkit

This post explores how to evaluate generative AI in economic and sovereign risk analysis. Using sentiment classification and macro-policy recommendations, we benchmark large language models (LLMs) against expert datasets and IMF reports. We highlight key metrics such as semantic similarity scores, discuss pitfalls like hallucinations and model bias, and share lessons from building an evaluation framework at CountryRisk.io.

Where’s My p-Value? Rethinking Evidence in the Age of GenAI

Where’s My p-Value? Rethinking Evidence in the Age of GenAI

CountryData.io and xlwings Lite empower economists and data scientists to bring the full power of Python into Excel—making it faster and easier to retrieve, analyse, and visualise economic data. With seamless API access to specialised datasets, instant in-workbook Python execution, and integration with libraries like Pandas, Statsmodels, and Scikit-learn, this combination eliminates the pain of clunky data imports, complex spreadsheet updates, and limited analytics—turning Excel into a true economic research and modelling hub.

CountryData.io & xlwings: A Match Made in Switzerland

CountryData.io & xlwings: A Match Made in Switzerland*

As we prepare for the upcoming release of the CountryRisk.io Insights platform, we're doing more than launching new features. We're redefining what it means to deliver value through economic analysis. The transition from a static, read-only research platform to an interactive, productivity-focused environment marks a major evolution in how our clients use and benefit from our tools.

But such a leap also introduces new challenges—particularly when it comes to client adoption. Advanced platforms require more than technical onboarding. They demand strategic alignment, change management, and continuous support. That’s why we’re introducing a new approach to customer success built around a new kind of role: the Forward Economist.

From Research to Real Impact: Why We’re Deploying Forward Economists for CountryRisk.io Insights

Introducing the AML Data Package from CountryRisk.io: Structured, Reliable Country Risk Data at Your Fingertips

This blog post highlights how CountryData.io quickly transformed the EU Deforestation Regulation (EUDR) country risk classifications—released by the European Commission on 22 May 2025—into a structured, developer-friendly data product in under an hour. While the EUDR provides useful regulatory clarity, its implementation suffers from poor data usability: country classifications are buried in a click-heavy interface, lack ISO codes, and provide no clear date or downloadable format. CountryData.io addresses these shortcomings by offering clean, API-accessible data, helping companies integrate regulatory information into business processes more efficiently. The post underscores the need for better data practices in public regulation and invites organizations to access the EUDR dataset for free.

CountryData.io: EUDR – How We Launched a Data Product in Less Than 60 Minutes

This blog explores how Offshore Financial Centers (OFCs) intersect with money laundering and terrorist financing (ML/TF) risks. Using a data-driven approach, it identifies jurisdictions with disproportionately large financial sectors based on BIS statistics and GDP data, then overlays this with global AML/CFT risk indicators such as FATF grey list status, EU high-risk third country lists, and OECD transparency ratings. Key findings reveal that while many OFCs are not currently on FATF watchlists, several show concerning patterns related to beneficial ownership transparency and corporate tax avoidance. The analysis offers practical guidance for AML country risk assessments, emphasizing the need for both quantitative benchmarks and regulatory scrutiny.

Offshore Financial Centers in AML/CFT Risk Assessments

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.

Insights