Economic Intelligence (ECONINT): Intelligence Discipline Guide

Economic intelligence is how you see a disruption forming while it is still a shipping rate, an export licence and a price index.

What Economic Intelligence is as a discipline

Economic intelligence is the collection and analysis of macroeconomic, trade and market data to assess conditions affecting operations, counterparties and markets. It draws on national accounts and statistical releases, central bank data, customs and trade records, commodity prices, freight and logistics indicators, labour and inflation series, energy data, sanctions and export-control measures, and industrial policy announcements. The practice combines official statistics with higher-frequency proxies to identify turning points early, and interprets everything against a specific organisational exposure rather than as general commentary.

Sub-methods include macro monitoring against a defined indicator set, trade-flow analysis using bilateral customs data, commodity and input-cost tracking, sanctions and trade-restriction impact assessment, and country risk analysis blending economic with political indicators. Maturity moves from consuming published commentary, to maintaining an indicator dashboard tied to documented business exposures, to scenario analysis with defined triggers and pre-agreed responses that remove improvisation from a crisis.

Why it matters

Economic intelligence answers what is changing in the operating environment and what it costs you. It converts abstract macro movement into concrete exposure: which supplier input costs are about to jump, which currency exposure threatens margin, which export-control change makes a product line unshippable next quarter. It also supplies the base rates against which company-specific claims are tested, since a firm reporting strong growth in a contracting sector is either genuinely exceptional or misreporting.

What analysts actually look for

These are the concrete, observable signals that carry weight in this area of work:

Bilateral trade volumes and values by commodity code, revealing supply chain shifts, substitution and transhipment routing
Freight and logistics indicators such as container rates, port congestion, vessel transits and air cargo capacity as leading signals
Producer and consumer price indices, and specific input cost series for the commodities and energy your operations actually consume
Currency movement and interest rate differentials affecting counterparty solvency, contract pricing and repatriation of revenue
Export controls, tariffs, quotas and sanctions measures with effective dates, product scope and licensing requirements
Labour market data including vacancies, wage growth and industrial action, affecting operational continuity and regional cost base
Energy production, storage levels and grid data indicating cost and reliability risk for energy-intensive facilities
Divergence between official statistics and independent high-frequency proxies, an early indicator of data quality problems or turning points

Where the data comes from

Authoritative and openly available collection points. Always confirm licensing and terms before operational or commercial use:

IMF and World Bank data portals — Standardised cross-country macroeconomic and development indicators with long, comparable time series
UN Comtrade — Bilateral merchandise trade statistics by product code, the standard open source for trade flow analysis
OECD statistics — Harmonised member-economy indicators including composite leading indicators and input-output tables
FRED (St Louis Fed) — Consolidated access to US and international series with charting, transformation and API access
Eurostat and national statistical offices — Authoritative regional and national releases with published revision schedules and methodology notes
Central bank publications — ECB, Federal Reserve and Bank of England policy decisions, minutes and financial stability reporting
WTO and national trade-remedy databases — Applied tariffs, quotas, disputes and trade measures searchable by product and partner country
Freight rate and port throughput data — Drewry, Freightos and port authority publications give commercial and public logistics indicators

A working method

A repeatable sequence beats ad-hoc searching. This is a practical starting workflow:

Map the exposure — Document where the organisation is genuinely exposed: input commodities, supplier countries, revenue currencies and regulated markets.
Build a targeted indicator set — Select the minimum number of series that actually move those exposures, with known release calendars and revision behaviour.
Set baselines and thresholds — Define normal ranges and the deviation levels warranting escalation, before an event forces the judgement to be improvised.
Monitor with release discipline — Track scheduled releases, distinguish first estimates from revisions, and never treat a preliminary figure as settled fact.
Cross-check with proxies — Corroborate official data against higher-frequency commercial and physical indicators, particularly where statistics are politically sensitive.
Translate to consequence — Express findings as effects on cost, margin, continuity or compliance for named business units, not as general economic commentary.
Run scenarios — Maintain a small set of plausible scenarios with trigger indicators and pre-agreed responses so decisions are not made under time pressure.

How this connects across the intelligence taxonomy

Intelligence work does not respect neat boundaries. The mission domain you are working, the disciplines you practise, and the data points you pivot on are one connected system. These are the direct relationships for this entry — every link is also a tag, so you can follow any thread across the whole library.

Applied in these mission domains

Operates on these data points

Company / Organization — A legal entity — corporation, LLC, NGO, or business.
Location / Coordinates — A geographic point, place, or region — the basis of GEOINT analysis.
Shipment / Bill of Lading — A consignment record linking shipper, consignee, goods, and route.
HS Commodity Code — The Harmonized System code classifying a traded good — the key to trade-flow analysis.
Facility / Site — A physical installation — plant, base, port, data centre — with a fixed footprint and function.
Satellite Imagery — Overhead imagery of an area of interest, used for change detection and site analysis.
Event / Incident — A discrete real-world occurrence — protest, strike, breach, seizure — with time, place, and actors.
GPS Coordinates — Precise latitude/longitude coordinates identifying an exact point on Earth — the atomic unit of GEOINT analysi
Sanction / Watchlist Entry — An entry on a sanctions list, watchlist, or PEP database.
Vessel / Ship — A maritime vessel identified by IMO, MMSI, or call sign.

Related disciplines

Accounting Intelligence — Financial Statements and Accounting Analysis
Corporate Intelligence — Understanding Companies, Structure, and Control
Cryptocurrency Intelligence — Tracing Value on Public Ledgers
Financial Intelligence — Following Value Through the Financial System
Sanctions Intelligence — Screening, Designations, and Evasion Detection

Inside the platform: where Economic Intelligence lives

The Quantus platform is 204 pages behind a 147-item sidebar organised into six working groups: Command (24 items), Dashboards (15), Threat Theaters (14), Intelligence Domains (15), Investigate (34), and Administration (45). This entry is not a page in isolation — it is a thread running through several of them.

The modules that matter most here:

discipline.php?d=ECONINT — Discipline hub
source-catalog.php?disc=ECONINT — Source catalogue filtered to this discipline
search.php — Company / Organization profile
correlate.php — Correlation graph
cases.php — Case management

Each dashboard is local-first: it renders from the platform’s own database rather than depending on a live third-party call, so it still works when an upstream API is unreachable or rate-limited. Heavy aggregates are cached with a hard query time cap and degrade to the last good value instead of hanging the page.

Automation, playbooks and AI skills

Analysis that only happens when someone remembers to run it is not a capability. The platform ships a 30-step automation pipeline (cron.php) that collects, ingests, resolves, enriches, correlates and scores on a schedule — 25 seeders, 11 resolvers and 7 enrichment runners, all idempotent and cursor-based so a run can be interrupted and resumed without duplicating or losing work.

AI skills that apply

The 16 one-click operations in ai-skills.php are deterministic jobs, not free-text generation. The ones that matter here:

Export STIX/MISP
Correlate Infrastructure
Run Alert Rules
Summarise (Copilot)
Generate Report

Alerting closes the loop: rules in alerts.php fire on new indicators matching a saved query, so a first sighting in this area raises a notification rather than waiting to be noticed at the next review.

Feeds, data sources and the API

The collection layer runs a feed registry of free, machine-readable sources — bulk blocklists and trackers (Maltrail, IPsum, FireHOL, the full abuse.ch corpora, phishing databases, Emerging Threats, Spamhaus, DigitalSide, ThreatView), authoritative government feeds (CISA KEV, OFAC, UN and EU sanctions lists), and reference datasets (RIR allocations, ip-to-ASN and geolocation tables, MITRE ATT&CK, EPSS). collect.php pulls them server-side on a schedule; feeds.php and source-catalog.php show what is registered, what it covers and when it last ran.

Anything the platform holds is reachable programmatically. The REST API in api.php exposes 11 endpoints — status, stats, search, lookup, recent, export, bulk_check, top_threats, by_category, categories, check — and export.php streams 18 formats in bounded chunks, so a million-row export neither exhausts memory nor times out:

STIX 2.1, MISP, OpenIOC 1.1, CEF (ArcSight), LEEF 2.0 (QRadar), Zeek/Bro intel, Snort/Suricata rules, Palo Alto EDL, BIND RPZ, hosts blackhole, iptables, CSV, JSON, NDJSON/JSONL, XML.

That covers the CTI standards (STIX 2.1, MISP, OpenIOC), SIEM ingestion (CEF, LEEF, Zeek), detection engines (Snort/Suricata), and direct enforcement (Palo Alto EDL, BIND RPZ, hosts, iptables) — so intelligence developed here can be actioned in the tools you already run, without a manual reformatting step. A TAXII 2.1 server and a MISP/RSS feed are also served for pull-based sharing.

Use cases

Three ways this entry earns its keep in day-to-day work:

Triage under time pressure. An artifact or report lands and you need a defensible read in minutes, not days. Map the exposure is the first move; the platform pre-computes the enrichment so the analyst spends the time on judgement rather than lookups.
Building the picture. A single indicator is rarely the story. Set baselines and thresholds turns one artifact into a network — shared infrastructure, repeated selectors, the same operator behind different names — via the correlation graph and the cross-entity link engine.
Producing something actionable. Analysis that ends in a document nobody can use is wasted. Run scenarios feeds the case file, the detection rule, the block list or the referral — with sourcing attached so the recipient can verify it.

Case management (cases.php), watchlists, saved searches and scheduled reports mean the work persists between sessions and survives an analyst leaving the team.

How each sector uses Economic Intelligence

The same entry is worked very differently depending on who you are, what authority you hold, and what you are ultimately producing. A military analyst is supporting a commander’s decision; a journalist is meeting a publication standard; an NGO caseworker is protecting a person. The underlying artifacts are shared — the constraints, outputs and thresholds are not.

🎖 Military and defence

Defence analysts practise economic intelligence to assess sustainment, industrial capacity and the economic conditions of an operating environment. The method is official statistics, trade data, price series and logistics indicators, read for what they say about a state ability to sustain production, fuel a campaign or absorb sanctions pressure. It feeds intelligence preparation of the operating environment, host nation support planning, contractor viability assessment and assessments of adversary defence industrial output. Constraint: economic assessment supports understanding and planning rather than targeting, and any product that touches civilian economic infrastructure must be framed with the legal protections that attach to civilian objects clearly stated.

🕵 National intelligence

National services run economic intelligence as a standing requirement covering strategic dependency, sanctions effectiveness, energy and commodity security and the economic health of states of interest. The distinctive contribution is fusing official statistics, which states manipulate, with independent observation such as trade partner mirror data, shipping movement, satellite-derived activity and financial flows. That triangulation is the core method, because the analytic question is usually the gap between the declared picture and the observable one. Products carry explicit sourcing and confidence, and dissemination often runs to policy and sanctions authorities rather than to operational consumers, on a schedule tied to decision points.

👮 Law enforcement

Investigators use economic intelligence mainly as context and corroboration in fraud, sanctions and organised crime cases. Trade statistics, customs records and price series establish what a transaction should have looked like, which supports proof that an invoice was mispriced, that goods were misdescribed, or that a declared trade route is implausible. Mirror trade analysis, comparing what one country reports exporting against what its partner reports importing, is a recognised technique for identifying trade-based laundering. Evidential use requires the official dataset, its version and retrieval date, and normally expert evidence to explain the methodology, since statistical inference is challenged routinely.

🔍 Private investigation and corporate security

Corporate practitioners use economic intelligence for country risk, supply chain resilience, counterparty viability and pricing intelligence. The work is entirely open source: national statistics, central bank data, customs records, commodity and freight indices, and industry data. A private actor must not obtain non-public government statistics through inducement, must not use material non-public information in a way that engages market abuse law, and must handle any commercially sensitive information under the confidentiality terms it came with. Output is decision-facing: whether to hold inventory, whether a supplier can survive an input price move, whether a market entry assumption still holds.

📰 Journalism and OSINT media

Journalists use economic data to substantiate reporting on sanctions effectiveness, inflation, trade diversion and industrial decline. The verification standard is to cite the specific series with its release date and revision status, and to understand that most headline statistics are revised, sometimes substantially. Compare official figures against independent indicators rather than reporting them uncritically, particularly for states with a record of statistical manipulation. Explain methodology in accessible terms, avoid implying precision that a survey-based estimate does not have, and give the relevant authority or company an opportunity to comment on the interpretation as well as the numbers.

🌍 NGO, humanitarian and human rights

Humanitarian and development organisations use economic intelligence for needs assessment, market monitoring and early warning of food and energy crises. Price series for staple goods, exchange rate movement, remittance flows and trade disruption are direct predictors of household distress and displacement. Practice is oriented to programming decisions: whether cash transfers still work in a collapsing market, when to preposition supplies, and how a currency move changes purchasing power for beneficiaries. Do-no-harm applies to publication in fragile contexts, since public assessments of currency or market collapse can accelerate the very dynamics they describe, so timing and framing require care.

🎓 University and research

Researchers use economic data as the primary empirical material for studying trade, sanctions, development and market structure. The methodological demands are well established: state the vintage of each series because revisions change results, document deflators, base years and seasonal adjustment, and address the discrepancies in mirror trade statistics explicitly rather than averaging them away. Reproducibility requires archiving the exact data extract, since statistical agencies revise history in place. Ethics approval is usually limited unless microdata on identifiable firms or households is involved, in which case access agreements and disclosure control rules govern what may be published.

Playbook: working Economic Intelligence end to end

A repeatable sequence, from the moment the requirement lands to the moment a product is delivered and the case is closed out. Each phase states what you are trying to establish, not merely what to click — the point is a defensible chain of reasoning, not a checklist.

Phase 1 — Define the economic question

State precisely what decision the analysis supports and over what horizon, because economic intelligence fails most often by producing general country commentary that answers nothing. Distinguish questions about capacity, about resilience, about a specific flow, and about a counterparty. Identify the threshold at which the answer changes a decision. A good output is a written question with a decision owner, a time horizon and a materiality threshold. Stop when the question can be answered with a number, a direction and a confidence rather than with a description of the economy.

Phase 2 — Identify the observable proxies

Determine what can actually be measured that bears on the question. Direct data is often unavailable, manipulated or lagged, so identify proxies: electricity consumption for industrial activity, freight rates and port calls for trade volume, customs mirror data for declared flows, fuel imports for military logistics, job postings for hiring intent. Assess each proxy for its lag, its coverage and its manipulability. A good output is a proxy set with a stated relationship to the underlying variable. Stop when the proxies collectively cover the question and their individual weaknesses do not coincide.

Phase 3 — Assemble the baseline series

Pull the relevant time series from authoritative sources with their full history, recording the vintage, the release date and the revision status. Note base years, deflators, seasonal adjustment and any methodology change, since a series break is routinely mistaken for an economic event. Where several bodies publish the same indicator, collect all versions. A good output is an archived dataset with a manifest naming source, series identifier, extraction date and revision state. Stop when the series is long enough to establish a normal range and the breaks in it are documented.

Phase 4 — Triangulate against independent observation

Test official figures against evidence the reporting state does not control. Compare declared exports against partner-reported imports, declared output against satellite-observed activity and electricity use, declared inflation against observed retail prices, and declared reserves against trade and payment behaviour. Discrepancies are the analytic product, not a problem to reconcile away. A good output is a comparison table showing each official claim, the independent measure, and the size and direction of the gap. Stop when the material claims have each been tested against at least one independent source.

Phase 5 — Characterise the structure

Establish how the economy or the market in question actually works before interpreting movement: which sectors dominate output and employment, what the export concentration is, who the trading partners are, what the currency regime is, how much external debt there is and in what currency, and where the chokepoints sit. Structural understanding is what distinguishes a shock that matters from noise. A good output is a structural profile that explains why the entity is sensitive to particular variables. Stop when you can predict which external moves would matter and which would not.

Phase 6 — Analyse the flow in question

For a specific trade, commodity or payment flow, establish volumes, prices, routes, intermediaries and their historical pattern. Look at how the flow changed around policy events such as sanctions, export controls or tariff changes, since diversion through third countries appears as an implausible surge in a neighbour trade statistics. Check unit values against world prices for evidence of mispricing. A good output is a documented flow analysis with the anomalies identified and quantified. Stop when the flow is explained or the unexplained residual is measured.

Phase 7 — Assess policy and control measures

Establish what sanctions, export controls, tariffs, subsidies and capital controls apply, when they took effect, and what they actually prohibit, since the legal text rarely matches the popular description. Map who is designated and what licensing exists. Then assess implementation and enforcement, which is usually where the effect is decided. A good output is a measures register with effective dates, scope and observed enforcement, tied to the flows they affect. Stop when the analysis can distinguish what is prohibited from what is happening.

Phase 8 — Build scenarios rather than forecasts

Construct a small number of distinct scenarios with explicit driving assumptions, indicators that would show each one developing, and consequences for the decision at hand. Avoid single-point forecasts, which are wrong and which suppress the discussion of what would change the answer. Assign rough likelihoods and be explicit that they are judgements. A good output is three or four scenarios with named observable indicators. Stop when each scenario has indicators specific enough that an analyst could tell next quarter which one is materialising.

Phase 9 — Set monitoring indicators

Convert the scenario indicators into a monitored set with defined thresholds, sources and update frequencies. Prefer high-frequency observable data over slow official releases for early warning, and pair each indicator with the decision it would trigger. Track revisions to the underlying series, since a revised figure can move an indicator across a threshold retrospectively. A good output is a dashboard where each line has a source, a threshold and a named decision. Stop when the indicator set is small enough to be reviewed properly at each cycle.

Phase 10 — Write for the decision

Produce the assessment in decision terms: what is assessed, at what confidence, what it implies for the decision, and what would change the judgement. Show the key numbers with their vintage, and separate observed data from estimate from judgement. Include the alternative interpretation, because economic evidence is usually consistent with more than one story. A good output is short, quantified where quantification is honest, and explicit about uncertainty. Stop when the decision owner can act without asking for the underlying spreadsheet.

Phase 11 — Review calibration and revise

Economic assessments are testable, which makes calibration achievable and therefore obligatory. Record what was assessed, what happened, and why the assessment was right or wrong, distinguishing bad data from bad reasoning. Re-run the analysis when series are revised, since revisions can invalidate a conclusion silently. A good output is a calibration record per standing assessment and a documented revision history. Stop maintaining an assessment only when the decision it supported has closed and the calibration entry has been written.

The platform ships this as a step-checked workflow in playbooks.php, so progress is recorded against a case rather than held in someone’s head.

Source register: what to collect from, and how

Sources are listed with their access model so you can plan around cost and licensing before you build a dependency on them. Open means no account required; registration means a free account or API key; licensed means paid or institutional access. Always confirm current terms — licensing changes, and a source that was free for research may not be free for commercial or evidential use.

Source	Access	What it gives you	How it is used here
FRED	Open	Large repository of economic time series covering output, prices, rates, credit, labour and sector activity from official sources.	Primary retrieval and charting source for macroeconomic series with documented vintages and revision history.
IMF	Open	International financial statistics, balance of payments, government finance and country surveillance reporting with standardised definitions.	Cross-country comparable macro data and the surveillance reports that document methodological weaknesses in national statistics.
OECD	Open	Statistics and analysis covering trade, productivity, input-output tables, taxation and industry structure for member and partner economies.	Supplies input-output structure needed to trace how a shock propagates through sectors and supply chains.
UN Comtrade	Registration	Official bilateral merchandise trade statistics by commodity code as reported by national customs authorities.	Mirror trade analysis comparing declared exports against partner-declared imports to detect diversion and mispricing.
World Bank open data	Open	Country-level development, economic, infrastructure and governance indicators compiled from national and international sources.	Structural baseline and long-run comparison for countries where national statistical publication is thin.
Bank for International Settlements	Open	Statistics on international banking, debt securities, foreign exchange turnover and credit to the non-financial sector.	Assesses external financing exposure and cross-border banking linkages that determine vulnerability to funding shocks.
World Trade Organization	Open	Trade policy reviews, tariff schedules, dispute records and trade monitoring reports covering member measures.	Establishes the applicable tariff and policy environment and documents measures notified by members.
US Energy Information Administration	Open	Energy production, consumption, stocks, price and trade data covering oil, gas, coal and electricity internationally.	Energy balance analysis underpinning assessments of industrial capacity, fuel security and sanctions effect.
Eurostat via the European Commission	Open	European statistical system data covering trade, prices, national accounts, industry and labour at member state level.	Detailed and comparatively reliable European trade and production data for diversion and substitution analysis.
OFAC sanctions programmes	Open	United States sanctions designations, sectoral measures, general licences and guidance including price cap arrangements.	Establishes precisely what is prohibited, which is the necessary baseline before assessing evasion or effectiveness.
Bureau of Industry and Security	Open	United States export control regulations, entity listings and licensing policy covering dual-use and controlled technology.	Identifies controlled goods flows and the entities whose procurement behaviour indicates circumvention.
Financial Action Task Force	Open	Standards and typology reports covering trade-based money laundering, proliferation financing and jurisdiction risk.	Supplies recognised typologies for identifying trade mispricing and diversion patterns in customs data.
Our World in Data	Open	Curated and documented long-run datasets on energy, food, trade, health and development with source transparency.	Rapid, well-documented access to long-run comparable series with clear provenance for context setting.
Copernicus	Registration	European Earth observation programme providing free optical and radar imagery and derived environmental data products.	Independent observation of industrial activity, port throughput, storage levels and agricultural output as a statistical check.

Prefer sources that publish a methodology and a revision history. A dataset that changes silently is a liability in any product that has to survive challenge.

Tooling

Tools commonly used against Economic Intelligence. None of these replace judgement, and each carries its own failure modes — know what a tool infers versus what it observes.

Statistical time series platforms — Retrieve, align and chart series with vintage and revision tracking. Limitation: coverage of non-member and opaque economies is thin and lagged.
Python and R analysis stacks — Reproducible transformation, deflation, seasonal adjustment and modelling of economic series. Limitation: results depend entirely on documented methodology choices that are easy to leave implicit.
Trade data query interfaces — Extract bilateral flows by commodity code for mirror and diversion analysis. Limitation: reporting lags run to months and some countries report irregularly or not at all.
Input-output modelling tools — Trace how a shock in one sector propagates through an economy or supply chain. Limitation: tables are infrequent and dated, so structural change is not captured.
Satellite imagery analysis platforms — Observe industrial activity, storage, shipping and agriculture independently of official reporting. Limitation: requires calibration to convert observation into economically meaningful quantities.
Vessel and freight tracking services — Provide near real-time indicators of trade volume and route changes. Limitation: transponder manipulation and dark voyages systematically understate sanctioned trade.
Scenario and sensitivity modelling spreadsheets — Express assumptions explicitly and test how conclusions move with them. Limitation: fragile and prone to silent error without structured review.
Sanctions and export control screening services — Establish which entities and goods are restricted in a given flow. Limitation: list matching does not capture ownership-based and sectoral restrictions automatically.

AI skills and automation in detail

These are deterministic jobs with defined inputs and outputs, not open-ended prompting. Each is idempotent and cursor-based: interrupt one and it resumes where it stopped rather than duplicating work or losing progress.

Export STIX/MISP — Streams the selection in CTI standard formats for sharing with partners and ISACs.
Correlate Infrastructure — Builds the cross-entity link graph: shared hosting, reused certificates, overlapping registrants, repeated selectors.
Run Alert Rules — Evaluates saved rules against new data so a first sighting raises a notification rather than waiting for review.
Summarise (Copilot) — Produces a narrative summary beside the underlying records. It explains; it never creates indicators or assigns attribution.
Generate Report — Assembles a sourced product from the current case or query, with provenance attached to each element.

A note on the boundary: the only skill that involves a language model is Summarise (Copilot), and it writes prose about records that already exist. Nothing else on this list involves generation of any kind. No indicator, relationship or attribution in the platform originates from a model. See the full skill list.

Tradecraft notes

The distinctions that separate a competent analyst from a fast one:

Record the vintage of every series you use. Statistical agencies revise history in place, and an analysis that cannot be reproduced because the underlying numbers changed silently is worthless the moment it is challenged.
Mirror trade discrepancies are a finding, not a nuisance. When one country declares exports that the receiving country never records importing, the gap is where diversion, mispricing and smuggling live.
Distinguish a series break from an economic event. Methodology changes, rebasing and reclassification produce step changes that look exactly like shocks and have caused more wrong assessments than any adversary deception.
Official statistics from states with an incentive to mislead should be treated as claims. Test them against electricity consumption, port activity, partner trade data and satellite observation rather than reconciling them against each other.
Prefer scenarios with named indicators over point forecasts. A single number invites false confidence and cannot be monitored, whereas an indicator set tells the consumer next quarter which world they are in.
Unit values in customs data are a powerful and underused signal. Goods priced far from world market levels for the same commodity code indicate mispricing, and the direction of the deviation tells you what is being moved.
Understand what a sanction actually prohibits before assessing whether it works. The legal text, the licensing regime and the enforcement record differ substantially from the public description, and analysis built on the description is analysis of nothing.
High-frequency proxies beat official releases for warning. Freight rates, electricity load, port calls and price observations arrive weeks or months before the statistic that will eventually confirm them.

Measuring whether it is working

Capability claims should be falsifiable. These are the measures that show whether work on Economic Intelligence is producing anything, and they are worth baselining before you change process or tooling.

Calibration of assessed direction and magnitude against subsequent outcomes, recorded per standing assessment and reviewed at least annually.
Lead time between a monitored indicator crossing its threshold and the confirming official statistic being published.
Proportion of assessments whose key claims were tested against at least one source independent of the reporting state or entity.
Number of conclusions revised because of a data revision or series break, which measures whether vintage discipline is working.
Share of issued products that state a materiality threshold and the evidence that would change the judgement, rather than describing conditions.
Rate at which decision owners act on the assessment, measured as decisions referencing it rather than products delivered.
Coverage of the monitored indicator set against the identified proxies, with gaps explicitly recorded rather than silently unmonitored.

Beware of measuring volume alone. Indicator counts and report counts rise easily and say little; time-to-attribution, proportion of findings that survive review, and how often a product changed a decision say a great deal.

Common pitfalls

Reacting to preliminary estimates that are later revised substantially, particularly first prints of output and employment data
Treating official statistics from opaque jurisdictions as reliable without corroboration from physical or trade-partner data
Mistaking a historical correlation between an indicator and business outcome for a causal relationship that survives regime change
Reporting macro narrative instead of exposure, producing commentary that no decision-maker in the business can act on
Ignoring mirror-statistics discrepancies in bilateral trade data, which frequently reveal transhipment and sanctions evasion
Confusing nominal with real series, or seasonally adjusted with unadjusted, and drawing misleading trend conclusions

Legal and ethical considerations

Official statistics and open trade data are free to use, though some datasets carry attribution and redistribution conditions and commercial indices are strictly licensed. The material legal risk sits on the collection side: obtaining non-public commercial information through deception, misrepresentation or induced breach of confidence constitutes economic espionage or trade-secret misappropriation in most jurisdictions. Sanctions and export-control analysis must be kept current, since measures change without notice and compliance obligations are strict. Never act on material non-public information in securities decisions.

Data integrity: no fabrication, no drift, no hallucination

Intelligence that cannot be traced back to a source is not intelligence, it is assertion. Everything in this entry — and everything in the platform behind it — is built on a small number of non-negotiable rules.

Provenance on every record

Every indicator carries the source that supplied it, a first-seen and last-seen timestamp, and a sighting count. Where several feeds report the same artifact, each contribution is recorded separately rather than collapsed, so you can see whether a finding rests on one source or twelve. Source attribution travels with the data into every export, so a recipient can audit a claim without asking you for the working.

Nothing is invented to fill a gap

If the platform has no data for Economic Intelligence, it says so. Empty is displayed as empty — never padded with plausible-looking placeholder values, sample records or illustrative examples that a reader might mistake for observations. A dashboard with no rows is a true statement about collection coverage, and it is treated as a gap to close, not a blemish to hide.

Scoring is deterministic and reproducible

Threat scores, reputation grades and risk tiers are computed from stated inputs with fixed weights, not estimated. The same inputs always produce the same output, and the formula is visible rather than a black box. Aggregates are cached with an explicit time-to-live so a figure on screen is never silently stale — and when a heavy query exceeds its time budget the platform serves the last known-good value and labels it, rather than inventing a fresh number or hanging.

Where AI is used, and where it is not

Language models summarise and explain. They do not create indicators, assign attribution or manufacture relationships. No IP address, wallet, hash or identity in the platform originates from a model — every one is ingested from a named feed, resolved from a reference dataset, or entered by an analyst with a source recorded. Copilot output is presented as narrative alongside the underlying records, never in place of them, so a reader can always check the summary against the evidence.

Guarding against drift

Enrichment is additive and timestamped rather than overwriting. Reference data — sanctions lists, allocations, taxonomies — is re-synchronised from the authority on a schedule instead of being edited in place, so local copies cannot quietly diverge from the source of truth. Attribution is recorded with a confidence level and the reporting it rests on, and inferred relationships are labelled as inferred. When a source retracts or corrects, the correction propagates rather than leaving a stale assertion behind.

What this means for you

You can put a finding from this platform in front of a regulator, a court, a board or a partner agency and show where each element came from. That is the standard the tooling is built to — because in this work, being confidently wrong is more damaging than being usefully uncertain.

By the numbers

The taxonomy this entry belongs to is not a marketing list — it is the actual structure of the platform: 52 mission domains, 52 intelligence disciplines and 65 data points, each with a live dashboard behind it. Supporting that: 18 indicator types, 14 playbooks, 16 AI skills, 18 export formats and a 30-step automated pipeline.

This particular entry connects directly to 10 data points, 9 mission domains, 5 closely related entries — every one of them a tag you can follow, and a dashboard you can open.

Questions analysts actually ask

How do I handle official statistics I do not trust?

Use them as a claim and triangulate. Compare declared output against electricity consumption and satellite-observed activity, declared trade against partner-reported mirror data, declared inflation against observed retail prices and exchange rate behaviour, and declared reserves against import cover and payment behaviour. Where the state has a track record, examine which series are manipulated, since manipulation is usually selective rather than universal and the untouched series remain useful. Report the official figure, the independent estimate and the gap, rather than substituting your own number silently. International Monetary Fund surveillance reports often document the specific methodological weaknesses.

What exactly is mirror trade analysis?

It compares what country A reports exporting to country B against what country B reports importing from country A, for the same commodity code and period. In a clean flow the two should broadly agree after adjusting for freight and insurance treatment and for timing. Large persistent gaps indicate misdeclaration, transshipment through a third country, smuggling or deliberate mispricing. It is a recognised technique for detecting trade-based money laundering and sanctions diversion. The main pitfalls are valuation basis differences, commodity code inconsistency and re-export treatment, so always check those before treating a gap as evidence of wrongdoing.

How do I detect sanctions evasion in trade data?

Look for surges in third-country trade that coincide with the imposition of measures, particularly in countries with no plausible domestic demand for the goods and no history of the trade. Watch for controlled goods appearing under adjacent commodity codes, for unit values far from world prices, and for new intermediary companies incorporated shortly before the flows began. Combine with corporate research on the intermediaries and with shipping data showing route and transponder behaviour. No single indicator is sufficient; the pattern is what evidences it, and the conclusion belongs with sanctions authorities.

Are commercial forecasts worth using?

As inputs and as a market consensus benchmark, yes; as an answer, no. Their value is telling you what is priced in and where your own judgement diverges from the consensus, which is the part worth explaining. Their weakness is that they are frequently anchored to each other, revise late, and rarely state the assumptions that would break them. Use them to calibrate rather than to substitute for analysis, and always identify the specific assumption behind any forecast you cite, because that assumption is where the disagreement actually lies.

What is the single most common analytical error here?

Mistaking a methodological change for an economic event. Rebasing, reclassification, a change in survey coverage or a new seasonal adjustment produces a step change in a series that looks exactly like a shock. The second most common is comparing nominal figures across periods without deflating, which manufactures growth out of inflation. Both are avoided by the same discipline: read the release notes for every series you use, record base years and methodology versions, and treat any discontinuity as a suspected data artefact until you have ruled it out.

How far ahead can economic intelligence usefully see?

It depends on the variable and the mechanism. Physical flows with long lead times, such as shipbuilding, refinery capacity or mining output, are predictable years out because the capital is already committed and observable. Prices, exchange rates and market sentiment are essentially unpredictable beyond the very short term, and pretending otherwise destroys credibility. The practical horizon for useful assessment is where a mechanism connects an observable indicator to an outcome. Where no such mechanism exists, provide scenarios and indicators rather than a forecast, and say plainly that the variable is not forecastable.

How should economic and financial intelligence be divided?

Economic intelligence works at the level of aggregates, markets and flows using published statistics; financial intelligence works at the level of accounts, payments and specific transactions, usually within a regulated or legally authorised setting. They meet in cases such as trade-based laundering, where customs statistics identify the anomalous flow and transaction analysis establishes the payments behind it. Run them as complementary rather than merged, because the sourcing, legal basis and evidential standards differ substantially, and an aggregate finding cannot substitute for transaction evidence in an enforcement action.

Standards, frameworks and further reading

Work that references a recognised framework is easier to defend, easier to hand over, and easier for a partner to consume:

System of National Accounts 2008, which governs the definitions and boundaries of national output, income and expenditure statistics.
IMF Balance of Payments and International Investment Position Manual sixth edition, which governs the recording of cross-border transactions.
The Harmonized System nomenclature maintained by the World Customs Organization, which governs the commodity classification used in all trade data.
IMF Special Data Dissemination Standard, which governs the timeliness, coverage and integrity commitments of participating national statistical agencies.
FATF Recommendations and trade-based money laundering typologies, which govern the identification of mispricing and diversion in trade flows.
OFAC, European Union and United Kingdom sanctions regulations, which govern what is prohibited and therefore what constitutes evasion.
The Export Administration Regulations and equivalent dual-use control lists, which govern which goods and technologies are controlled.
ICD 203 analytic standards, which govern expression of confidence, alternative analysis and sourcing in assessed economic products.

References

Primary sources and authoritative references for this entry. Publishers revise and retire material, so treat the retrieval date as part of the citation and re-check before relying on any of it in a formal product.

Federal Reserve Economic Data — Federal Reserve Bank of St Louis. Repository of economic time series with vintage and revision tracking
International Financial Statistics and country surveillance — International Monetary Fund. Comparable macroeconomic data and assessments of national statistical quality
UN Comtrade database — United Nations Statistics Division. Official bilateral merchandise trade statistics used for mirror analysis
World Development Indicators — World Bank. Country-level economic and structural indicators compiled from official sources
International banking and debt statistics — Bank for International Settlements. Cross-border banking, debt and foreign exchange statistics
Trade policy reviews and tariff data — World Trade Organization. Member trade measures, tariff schedules and monitoring reports
International energy statistics — US Energy Information Administration. Energy production, consumption, trade and price data by country
Sanctions programmes and guidance — US Office of Foreign Assets Control. Authoritative text of restrictive measures and licensing arrangements
Trade-based money laundering typologies — Financial Action Task Force. Recognised methodology for identifying mispricing and diversion in trade
Economic and structural statistics — OECD. Comparable trade, productivity and input-output data for member and partner economies

Link integrity: every reference above was verified with a live request when this page was generated. Where a publisher had moved or withdrawn a document, the link was repointed at a preserved copy in the Internet Archive and marked as archived. Anything with no reachable copy anywhere had its link removed rather than left to rot — the source is still credited, it simply cannot be linked.

Put it into practice

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