A-P08 · TOPIC · consumer-scamsGRA dossier · service level R0
GRAFinancial Crime and Fraud RiskIn development
Topic dossier · Financial Crime and Fraud Risk

Consumer scams

Scams persuade people to hand over money or details: investment and crypto scams, romance scams, impersonation of banks, police or officials, and fake purchases. Social media, messaging and online advertising help scammers reach victims at scale, often from organised operations abroad. This page sets out how scams develop, how harm is measured, and which institutions decide on prevention and redress.

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Systems · around this subjectDesign, not a record

Alert triage and investigations

Consumer scams · Bank B monitoring · Today

  • Alerts today
    2207
  • Escalated to cases
    6pct of alerts
  • Open cases older than 30 days
    55Watch
  • Reports filed this month
    76Normal
Backlog above targetGRA · Financial Crime and Fraud Risk

Alert queue

  1. HRapid in-out through new accountsScenario TM-07 · score 991 h
  2. MTrade invoice price outlierScenario TB-03 · score 643 h
  3. MStructuring below thresholdScenario TM-02 · score 748 h
  4. LName match, sanctions listLikely false positive3 h

Counterparty network, case CS-9372

red: accounts under review

Case path

  1. Alertscenario
  2. Triageanalyst
  3. Investigatecase
  4. DecideMLRO
  5. Reportto FIU
  6. Feedbacktuning
Alert triage and investigations: the screen's structure, terms and states for Consumer scams.Placeholder names and illustrative values; a design, not a record or a live system.

Fig.Alert triage and investigations. Design wireframe with placeholder names and illustrative values, not a record or a live system.

01In brief

The short answer.

Consumer scams are frauds that manipulate people into handing over money or details, such as investment, romance, impersonation and purchase scams, often run by organised groups abroad. They bypass payment controls because victims authorise payments. Legislators, banks, platforms, telecoms and law enforcement hold the decisions.

02Geography

Activity and exposure in Mexico.

Night-time lights and population, a proxy for economic activity, with the floods and fires that put assets at risk, framed on Mexico; each layer is a dated NASA product.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 2

Activity and exposure

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Night-time lights and population, a proxy for economic activity, with the floods and fires that put assets at risk, framed on Mexico.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.

Data: NASA GIBS · ASTER GDEM · Natural EarthSources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). CC BY 4.0
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, © MapLibre contributors.

Layers and dates

  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Active fires and thermal anomalies, last day: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026

03The question

Consumer scams: how the risk develops.

Scam harm and protective controls. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.

Mechanism

Manipulation beats controls

Scammers build trust and urgency so victims authorise payments themselves and override warnings. Technical controls designed for unauthorised fraud do not stop them.

Mechanism

Platforms carry the approach

Many scams begin with ads, social media posts, messages or dating profiles. Responsibilities of platforms, telecoms and banks are being redrawn.

Mechanism

Industrial scam operations

Organised compounds run scams at scale, sometimes using trafficked workers. Proceeds move quickly across borders, often through crypto-assets.

Mechanism

Harm goes beyond money

Victims suffer lasting financial, emotional and health effects. Under-reporting driven by shame and stigma hides the true scale.

04Key terms

Terms used on this page.

Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.

TermDefinition
Impersonation scamA scam in which criminals pose as a bank, official or trusted contact to obtain money.
Investment scamA scam that persuades victims to invest in fake or worthless opportunities, often involving crypto-assets.
Scam centreAn organised operation running scams at scale, sometimes with trafficked workers.
05Indicators

What is measured, and how well.

Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.

IndicatorUnitCadenceSource typeLabel
Scam losses reportedcurrency per yearannualConsumer protection and police reportsREPORTED
Scam reports by contact methodcount by channelannualConsumer reporting dataREPORTED
Investment scam lossescurrencyannualRegulator and police dataREPORTED
Scam ads removed by platformscountperiodicPlatform transparency reportsDECLARED
Bank warnings shown before paymentcount or share of paymentsperiodicFirm dataDECLARED
Victim reimbursement outcomesshare reimbursedperiodicRegulator dataOFFICIAL
06Canvas

From sources to decisions.

The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.

Pipeline canvas · Consumer scams as a run graphDesign, not a record
Consumer scams: from customer due diligence to suspicious activity reportCustomer due diligence sets a risk rating; transaction monitoring scenarios raise alerts; analysts triage and investigate; the MLRO decides whether to report to the financial intelligence unit; feedback tunes the scenarios. Design of the process, not a record of a run.

Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.

07Sources

Where the evidence comes from.

Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.

SourceWhat it givesLicence class
FTC Consumer Sentinel Network data bookReported fraud and scams in the United StatesPublic domain
FBI Internet Crime Complaint Center annual reportReported internet-enabled crimePublic domain
Australian Competition and Consumer Commission Targeting Scams reportScam losses and channels in AustraliaFree, Australian government terms
UNODC reports on scam centres in Southeast AsiaOrganised scam operations and trafficking linksFree, UN terms
Global Anti-Scam Alliance reportsCross-country scam surveysFree to read, terms to check
08Decisions

Who decides, and what the evidence serves.

Financial Crime and Fraud Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

Who decidesThe questionEvidence this page serves
Legislators and regulatorsHow responsibility for scam prevention and redress is sharedLoss data, reimbursement outcomes and comparative laws
Banks and payment firmsWhich warnings, delays and checks to applyScam patterns and customer outcome evidence
Platforms and telecomsHow to block scam ads, messages and callsTakedown data and regulatory duties
Law enforcementHow to disrupt cross-border scam operationsIntelligence and international cooperation

Outputs this topic produces

  • Guide to scam types and how they bypass payment controls
  • Comparative notes on shared responsibility across banks, platforms and telecoms
  • Brief on warnings and friction at the point of payment
  • Questions for boards on scam prevention
09Core questions

What experts ask.

Scam researchers ask how to protect people from being persuaded to harm themselves.

  • Who is targeted by scams, and what makes people vulnerable at particular moments?
  • How do scams use psychology, such as urgency, authority and trust, to persuade victims?
  • Which interventions, such as warnings, delays or confirmation of payee, reduce losses?
  • How can telecoms, online platforms and financial institutions share responsibility?
  • How are victims supported, and how are reimbursement and redress handled?
10Mechanisms and drivers

Persuasion across platforms.

Scams exploit normal psychology: trust in authority, fear of loss, hope of gain, loneliness and the pressure of urgency. Anyone can be deceived, though vulnerability increases with isolation, bereavement, financial stress and unfamiliarity with digital services. Scammers use spoofed calls, fake websites, social media advertising and messaging apps to reach victims at scale.

Because victims authorise payments, traditional fraud controls may not trigger. Effective protection combines interventions at several points: blocking scam calls and ads, warnings and friction at the moment of payment, detection of mule accounts and support for victims. Responsibility across sectors is increasingly debated, since the financial system is often the last point of contact rather than the first.

11Methods and data

How the evidence is built, and where it stops.

  1. 01

    Victim research

    Surveys and interviews on scam experiences, drawing on behavioural science. Under-reporting is significant.

  2. 02

    Intervention evaluation

    Testing warnings and friction. Effects vary and may wane.

  3. 03

    Cross-sector analysis

    Mapping where scams originate with network science. Data spans sectors with different rules.

  4. 04

    Typology research

    Describing scam methods, including those using AI-generated content. Must avoid enabling crime.

12Methods

How it is studied, and what the methods cannot say.

  1. Artificial intelligence and machine learningReal-time fraud scoring · Alert prioritisation · Fairness in fraud controlsApplied here
    Canonical · STEM Commons
  2. Network science and contagionMule network detection · Ownership chain analysis · Cross-institution patternsApplied here
    Canonical · STEM Commons
  3. Data engineering, interoperability and stewardshipScreening data quality · Monitoring coverage checks · Retention and minimisationApplied here
    Canonical · STEM Commons
  4. Digital identity and public digital systemsAssurance levels for onboarding · Defending against deepfakes · Inclusion testingApplied here
    Canonical · STEM Commons
13Questions

Questions and answers.

Why don't payment controls stop scams?
Because scam victims authorise the payment themselves, often after being coached to ignore warnings, so controls designed to stop unauthorised transactions do not apply.
Who is responsible for scam losses?
It depends on the jurisdiction. Some require payment firms to reimburse victims in many cases; others place more weight on platforms, telecoms or victims themselves.
Where should a scam victim go for help?
To their bank or payment provider and the national police or fraud reporting service. This platform does not handle cases.
What should I do if I have been scammed?
Contact your bank or payment provider immediately and report the scam to the police or national fraud reporting service. GRA cannot act on individual cases.
Are only older people targeted?
No. People of all ages are scammed. Different scams target different groups, and vulnerability often depends on circumstances rather than age alone.
Why involve telecoms and platforms?
Because many scams begin on phone networks, messaging apps and online platforms. Protecting people effectively requires action where scams start, not only where money moves.
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Around Financial Crime and Fraud Risk.

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