RavenPack and Economist Intelligence Unit Team Up to Feed AI‑Driven Finance with Trusted Macro Data

As banks, asset managers, and fintech platforms accelerate the deployment of production‑grade AI applications, the bottleneck is shifting from model training to data reliability. In a move that could reshape the data‑infrastructure layer for financial AI, RavenPack announced a redistribution partnership with the Economist Intelligence Unit (EIU), the research arm of The Economist Group. The collaboration will make EIU’s long‑standing macro‑economic and geopolitical intelligence available through RavenPack’s Bigdata.com platform, delivering the data via open APIs and MCP connectors that can be woven directly into enterprise AI pipelines.

“Everyone’s racing to build AI, but speed without trust is just a faster way to make bad decisions,” said Armando Gonzalez, Co‑Founder and CEO of RavenPack. “That’s why we partnered with EIU, part of The Economist Group. We’re building an intelligent macroeconomic and geopolitical data layer for financial AI – a single place where the world’s best intelligence is already enriched, connected, and ready to power any application, any workflow, any agent. That’s the infrastructure enterprises need, and that’s what we’re delivering.”

Ross Bailey, Global Head of Data and Content at EIU, echoed the sentiment: “EIU has spent over 80 years making sense of the world for decision‑makers. Partnering with RavenPack brings that intelligence directly into the AI workflows enterprises are building today. Through Bigdata.com, our expert analysis and data insights are something your systems can act on.”

The partnership signals a strategic response to a market where AI‑enabled decision‑making is increasingly data‑hungry, yet regulatory scrutiny and risk management demand provenance and accuracy. By consolidating EIU’s breadth of coverage into a single, API‑first feed, RavenPack aims to reduce the operational friction that typically forces firms to juggle multiple vendors for macro data, risk scores, and ESG metrics.

Why macro data matters for AI in finance

Artificial‑intelligence models, whether they are large language models (LLMs) interpreting news sentiment or predictive algorithms forecasting credit risk, rely on high‑quality, structured inputs. Macro‑economic indicators, geopolitical risk assessments, and ESG scores provide the contextual backbone that separates a model that merely extrapolates from one that can anticipate regime shifts, sovereign defaults, or supply‑chain disruptions.

Historically, financial institutions have sourced such data from a patchwork of providers—central banks for raw statistics, boutique consultancies for risk ratings, and third‑party ESG data aggregators for sustainability metrics. This fragmented approach introduces latency, inconsistencies, and costly data‑governance overhead. The RavenPack‑EIU integration promises a unified, continuously updated data stream that can be queried in real time, aligning with the low‑latency requirements of algorithmic trading desks, risk‑management engines, and automated compliance workflows.

What the new data feed delivers

Country‑level analysis

EIU’s country coverage spans more than 200 economies, delivering daily political, economic, and market updates. Each country profile includes up to 320 macro‑variables—ranging from GDP growth projections to inflation expectations and fiscal balance estimates—plus proprietary ratings that synthesize regulatory and structural risk. The data is refreshed daily, with medium‑ and long‑term forecasts that can be accessed programmatically via Bigdata.com’s RESTful endpoints.

Industry insights

The partnership adds five‑year forward‑looking forecasts for six major industry clusters and 26 subsectors across over 60 economies. Users can retrieve demand‑supply dynamics, sector‑specific growth rates, and trend analyses that are essential for credit‑risk modeling, sector rotation strategies, and supply‑chain risk assessments.

Commodity outlooks

A suite of forecasts covering 25 critical commodities—including

• oil
• copper
• agricultural products

—provides price trajectories, demand forecasts, and supply‑side constraints. This commodity layer can feed into pricing engines for commodity‑linked derivatives, treasury risk models, and ESG‑related exposure assessments.

Financial‑risk coverage

EIU’s risk module spans 131 markets, delivering

• sovereign
• currency
• banking
• political
• structural

risk scores. The data set incorporates up to 220 macro‑variables per market, standardized ratings, concise executive summaries, and two‑year outlooks. The granular risk scores are particularly valuable for portfolio‑risk dashboards and stress‑testing frameworks that need to factor in macro‑political volatility.

Operational‑risk intelligence

Across 180 markets, the operational‑risk feed evaluates

• security
• political stability
• government effectiveness
• legal frameworks
• infrastructure quality
• financial‑system robustness
• labor market conditions
• trade barriers
• taxation
• macro‑economic health

. Consistent methodology and a scoring system for 26 subsectors enable comparative analysis for multinational corporates and cross‑border lenders.

ESG and sustainability metrics

Sustainability coverage now includes ESG risk indicators for more than 150 countries, tracking 220 metrics with historical data back to 2015. This longitudinal ESG data supports regulatory reporting under frameworks such as the EU Sustainable Finance Disclosure Regulation (SFDR) and the U.S. Securities and Exchange Commission’s Climate‑Related Disclosures, while also feeding ESG‑tilted investment models.

All of these data streams are accessible through Bigdata.com’s API suite and MCP connectors, which simplify integration with cloud‑native data warehouses, real‑time streaming platforms, and AI model‑training pipelines.

Technical integration: APIs and MCP connectors

RavenPack’s Bigdata.com platform is built on an API‑first architecture, offering both RESTful endpoints and WebSocket streams for low‑latency consumption. The MCP connectors act as pre‑configured pipelines that can push EIU data directly into popular analytics environments such as Snowflake, Databricks, and Google BigQuery. For firms operating on Kubernetes or serverless architectures, the APIs can be called from within model‑training notebooks, enabling on‑the‑fly feature engineering that incorporates the latest macro‑economic signals.

Security and compliance are baked into the platform. Data in transit is encrypted with TLS 1.3, while at‑rest encryption leverages AES‑256. Role‑based access controls (RBAC) and audit logging meet the requirements of SOC 2 Type II, ISO 27001, and GDPR, ensuring that sensitive macro‑economic data can be shared across enterprise boundaries without violating data‑privacy mandates.

Market impact: positioning in the fintech data ecosystem

The RavenPack‑EIU partnership arrives at a time when the fintech data market is consolidating around a few dominant players—Bloomberg, Refinitiv, and S&P Global dominate market‑data feeds, while newer entrants such as Quandl and FactSet have carved out niches in alternative data. By marrying RavenPack’s AI‑ready data‑infrastructure with EIU’s deep macro‑economic expertise, the combined offering differentiates itself on two fronts:

1. Breadth with granularity – Few providers deliver the same mix of country‑level macro variables, industry forecasts, commodity outlooks, and ESG scores under a single contract.
2. AI‑centric delivery – The API‑first, connector‑driven model aligns with the DevOps pipelines of modern fintech firms, reducing time‑to‑value for AI projects.

For asset managers, the integrated feed can enhance factor‑model construction by adding macro‑economic tilt factors that are updated daily. For banks, the risk‑score modules can be embedded into credit‑approval engines, providing a real‑time view of sovereign and operational risk that complements internal credit scoring. For enterprise SaaS platforms building “AI‑as‑a‑service” solutions, the data can be offered as a value‑added layer, creating new revenue streams.

Competitive considerations and potential challenges

While the partnership offers a compelling data proposition, adoption will hinge on a few practical considerations:

• Pricing transparency – Enterprise customers will scrutinize subscription costs, especially when juxtaposing the offering against existing Bloomberg or Refinitiv contracts that bundle multiple data streams.
• Data latency – Real‑time trading desks demand sub‑second data delivery. RavenPack’s API performance benchmarks will need to demonstrate that the added enrichment layer does not introduce prohibitive latency.
• Regulatory alignment – As regulators increasingly require provenance for model inputs, the partnership must provide robust metadata and audit trails to satisfy compliance audits under Basel III, the EU’s Capital Requirements Regulation (CRR), and emerging AI‑risk frameworks.

RavenPack’s prior experience in transforming unstructured news into structured analytics suggests a strong foundation for meeting these demands, but the proof will be in early‑adopter case studies.

Industry context: AI, open banking, and data consolidation

The fintech sector is witnessing a convergence of three trends that amplify the relevance of RavenPack’s new data feed:

1. AI‑driven decision‑making – Large language models and transformer‑based architectures are being adapted for tasks ranging from sentiment analysis of earnings calls to macro‑forecast generation.
2. Open banking and data democratization – APIs are now the lingua franca for financial services. Platforms that can ingest external macro data via standardized APIs are better positioned to offer integrated services, such as embedded wealth‑management tools within neobanking apps.
3. Regulatory pressure on ESG and risk modeling – Mandates such as the EU’s Taxonomy Regulation and the U.S. SEC’s climate‑risk disclosure rules are pushing firms to embed ESG metrics into their risk frameworks.

By delivering a unified, API‑driven macro data layer, RavenPack is effectively building the “data plumbing” that underpins these broader industry shifts.

Outlook: What comes next?

The immediate next step for RavenPack and EIU will be onboarding pilot customers—likely a mix of asset managers, banks, and fintech SaaS providers—to validate integration workflows and performance at scale. Success metrics will include reduced data‑vendor management overhead, faster model iteration cycles, and demonstrable improvements in predictive accuracy for macro‑sensitive use cases.

Longer‑term, the partnership could evolve into co‑development of proprietary AI models that leverage EIU’s forecasts as training signals, potentially giving RavenPack a differentiated offering in the burgeoning “AI‑as‑a‑service” market. Additionally, as regulatory bodies begin to formalize standards for AI model governance, the combined data provenance and audit capabilities could become a competitive moat.

For firms that have been wrestling with fragmented data sources, the RavenPack‑EIU integration offers a pragmatic path to consolidate macro intelligence, streamline AI pipelines, and meet emerging compliance demands—all without sacrificing the depth of analysis that EIU is known for.

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