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Access CountryRisk.io's publications, the latest risk news, analyses and publications from around the world. 

CountryRisk.io DSA Toolkit: From Prototype to Production-Grade Sovereign Debt Analytics

AI macro research needs governed data, not just smarter agents.

AI agents can transform macro research, but only if they are connected to governed data infrastructure that makes

Why Macro-Research Needs Dedicated Macro Data Infrastructure

See how Fitch, Moody’s, and S&P may interpret a government policy proposal before it reaches the rating committee.

AI-powered sovereign rating policy-impact skill for debt management offices, mapping government policy proposals to Fitch, Moody’s, and S&P methodologies with   indicators, thresholds, and agency-specific rating insights.

Per-country AI skills turn fragmented country knowledge into reusable, visible, and disciplined analyst judgment.

Per-country AI skills can transform how macro analysts manage, reuse, and update country expertise. Instead of relying on scattered notes, spreadsheets, saved links, data caveats, and informal source knowledge, analysts can create a living country file that captures current views, scenario probabilities, trusted data series, source hierarchies, analytical playbooks, and relevant expert contacts. This approach is not about building a generic country encyclopedia or replacing the analyst. It is about institutionalizing expert judgment: what matters for a country, which indicators are reliable, which data series are misleading, what scenarios deserve attention, and what evidence would change the analyst’s view. For investment management, sovereign risk analysis, macro research, and country risk monitoring, per-country AI skills create a reusable analytical asset while making the analyst’s accumulated expertise more transparent, disciplined, and valuable.

Per-Country AI Skills as the Analyst’s Living Country File

Country risk analysis needs more than information access: it needs structured reasoning, trusted data, and repeatable workflows.

New in CountryRisk.io Insights: Skills, MCP Integrations, and Advanced AI Reasoning

See how CountryRisk.io combines macro data infrastructure, AI agents, and automated model pipelines to turn FX valuation analysis into a workflow.

Explore how CountryRisk.io and CountryData.io enable AI-native macroeconomic research through automated data pipelines, FX valuation models, sovereign-risk analytics, and integrated research workflows. This article demonstrates how a PPP-based foreign exchange valuation model can be built, deployed, and operationalized using modern macro-data infrastructure, AI assistants, and automated model updates. Learn how economists and analysts can streamline data sourcing, model maintenance, chart generation, commentary writing, and macro research production within a unified platform designed for cross-country analysis and sovereign-risk monitoring.

AI-Native Macro Research in Practice: Building and Operationalizing a PPP FX Model

We build a transparent macro model to explain OECD country risk classifications and identify where official ratings diverge.

This article explores how OECD country risk classifications can be explained using macroeconomic, governance, sovereign risk, and payment-stress indicators. We build a transparent ordered-logit model covering 174 countries between 1999 and 2025 to approximate OECD export credit risk classifications and identify where official country risk ratings diverge from observable economic fundamentals. The analysis examines the role of sovereign risk scores, external debt, inflation, reserves, governance quality, and banking-sector stress in shaping OECD country risk classes, while also discussing the limitations created by confidential payment-experience data and expert judgment in the OECD classification process.

Inside OECD Country Risk Classifications: A Transparent Macro Model Approach

As AI makes macro-economic modelling cheap, the real edge shifts to infrastructure, expertise and curiosity.

Artificial intelligence is transforming macro-economic modelling by dramatically reducing the time and coding effort required to build econometric models, sovereign risk frameworks and macro research pipelines. As AI-generated code becomes abundant, the competitive advantage in macro research is shifting away from programming capacity toward high-quality data infrastructure, domain expertise and the ability to ask better economic questions. Modern macro teams can now build and test models faster than ever, but reliable insights still depend on robust data systems, transparent workflows and experienced economists who understand the limitations of model outputs. The future of macro-economic analysis will belong to organisations that combine AI-powered modelling with strong infrastructure, economic judgment and intellectual curiosity.

When Macro-Economic Modelling Becomes Cheap

How AI agents, CountryData.io, empirical models, and economist judgment can turn macro research from a manual note-writing process

The Hybrid Economic Research Team: AI Agents, Data, Models, and Economist Judgment

Smarter Macro Research: Introducing MCP Integrations in CountryRisk.io Insights

And why measuring it requires a fundamentally different approach than country risk

Sector risk is the overlooked dimension of AML compliance. Unlike country risk, there is no standardised global dataset to draw on — making structured expert assessment the only viable methodology. This article explains the difference, outlines CountryRisk.io's eight-dimension sector risk framework spanning 501 sectors, and invites expert feedback on the approach.

A Methodology for AML Sector Risk — Draft, Open for Challenge

How will AI reshape macroeconomic and country risk research — and what can economists learn from disruption already unfolding in the legal industry?

AI is transforming knowledge work at an accelerating pace, and the legal industry offers a clear preview of what lies ahead for macroeconomic research and sovereign risk analysis. From commoditising access to legal information to automating expert commentary and climbing the advisory pyramid, AI-driven disintermediation is reshaping how professional services are delivered. At CountryRisk.io, we see the same dynamics playing out in country risk: secondary research providers face displacement while those closest to primary data, with the best AI analytical skills and the deepest client context, will thrive. Our strategy centres on three principles — proximity to primary data through CountryData.io, making implicit expertise explicit through quantitative models and structured AI skills, and delivering relevance through client-specific context. The transformation of knowledge work is not on the horizon; it is already under way.

CountryRisk.io: An Integrated Intelligence Platform for Sovereign Risk

The February 2026 update to CountryRisk.io Insights introduces powerful new capabilities for country risk, macro, and compliance professionals. Highlights include the new Data Analyst feature for end-to-end economic analysis with trusted global datasets, file upload in the AI Assistant to turn static documents into interactive insights, and AML Country Risk Score Version 2026, a more robust, transparent, and jurisdiction-specific model aligned with evolving AML/CTF regulations. Together, these enhancements help analysts work faster, with greater consistency and confidence, in a single AI-driven research environment.

February 2026 Release: CountryRisk.io Insights New Features

CountryRisk.io has released Version 2 of its AML Country Risk Index Methodology, delivering a more transparent, defensible, and regulator-aligned framework for assessing country-level money laundering and terrorist financing risk. The updated methodology strengthens quantitative rigor while maintaining consistency over time and introduces scalable regional and national variants—including Global, EU, US, Germany, and France—automatically aligned with relevant supervisory and high-risk country lists. By embedding regulatory expectations directly into the model, Version 2 enables institutions to apply a single, consistent AML country risk framework across multiple jurisdictions with greater confidence and efficiency.

Introducing Version 2 of the CountryRisk.io AML Country Risk Methodology

This post analyses Germany’s National Risk Assessment country list.

Germany’s National Risk Assessment introduces a national list of high-risk countries alongside existing FATF and EU classifications, creating additional compliance complexity for financial institutions. This article compares the German list with international standards and uses data-driven analysis to explain the underlying risk drivers. The findings show that financial secrecy and tax opacity play a larger role than organised crime levels or formal AML regulatory quality.

High-Risk Countries in the German National Risk Assessment – What Does It Really Mean?

Revealing surprising shifts with real consequences for sovereign credit and AML risk analysis.

The World Bank’s 2025 update to the Worldwide Governance Indicators introduces significant methodological changes that affect governance scores for many countries, with real implications for sovereign credit risk and AML country risk assessments. This analysis examines how the revised methodology alters historical comparability and highlights which countries are most impacted by the changes.

Worldwide Governance Indicators – looking under the hood of the latest methodology update

The new Data Analyst feature brings data retrieval, analysis, and styled charts together seamlessly inside CountryRisk.io Insights.

The new Data Analyst feature in the CountryRisk.io Insights platform enables economists and risk analysts to retrieve data, run analysis, and generate publication-ready charts in minutes using AI and a fully integrated data pipeline. By combining CountryData.io, automated Python charting, and reusable style-guided templates, it delivers faster, more consistent macroeconomic insights end to end.

Introducing the Data Analyst: From Raw Data to Publication-Ready Charts in Minutes

A look at how the CSSF’s latest review aligns with and informs our AML country risk methodology.

What the CSSF Counter-Proliferation Financing Review Means for AML Country Risk Analysis

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

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