GRA · Financial Crime and Fraud Risk
Spatial financeWhere capital meets hazard, exposure and transition
Consumer scams
Scams persuade people to hand over money or details: investment and crypto scams, romance scams, impersonation of banks, police or public servants, 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.
How the work runs.
Alert triage and investigations
Consumer scams · Bank B monitoring · Today
- Alerts today2207
- Escalated to cases6pct of alerts
- Open cases older than 30 days55Watch
- Reports filed this month76Normal
Alert queue
- HRapid in-out through new accountsScenario TM-07 · score 991 h
- MTrade invoice price outlierScenario TB-03 · score 643 h
- MStructuring below thresholdScenario TM-02 · score 748 h
- LName match, sanctions listLikely false positive3 h
Counterparty network, case CS-9372
red: accounts under review
Case path
- Alertscenario
- Triageanalyst
- Investigatecase
- DecideMLRO
- Reportto FIU
- Feedbacktuning
Fig.Alert triage and investigations: example names and illustrative values, not a record or a live system.
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.
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.
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.
Industrial scam operations
Organised compounds run scams at scale, sometimes using trafficked workers. Proceeds move quickly across borders, often through crypto-assets.
Harm goes beyond money
Victims suffer lasting financial, emotional and health effects. Under-reporting driven by shame and stigma hides the true scale.
What consumer scams means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Transmission channels
How a shock in consumer scams reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
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.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Scam losses reported | currency per year | annual | Consumer protection and police reports | REPORTED |
| Scam reports by contact method | count by channel | annual | Consumer reporting data | REPORTED |
| Investment scam losses | currency | annual | Regulator and police data | REPORTED |
| Scam ads removed by platforms | count | periodic | Platform transparency reports | DECLARED |
| Bank warnings shown before payment | count or share of payments | periodic | Firm data | DECLARED |
| Victim reimbursement outcomes | share reimbursed | periodic | Regulator data | Published statistics |
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.
| Source | What it gives | Licence class |
|---|---|---|
| FTC Consumer Sentinel Network data book | Reported fraud and scams in the United States | Public domain |
| FBI Internet Crime Complaint Center annual report | Reported internet-enabled crime | Public domain |
| Australian Competition and Consumer Commission Targeting Scams report | Scam losses and channels in Australia | Free, Australian government terms |
| UNODC reports on scam centres in Southeast Asia | Organised scam operations and trafficking links | Free, UN terms |
| Global Anti-Scam Alliance reports | Cross-country scam surveys | Free to read, terms to check |
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 decides | The question | Evidence this page serves |
|---|---|---|
| Legislators and regulators | How responsibility for scam prevention and redress is shared | Loss data, reimbursement outcomes and comparative laws |
| Banks and payment firms | Which warnings, delays and checks to apply | Scam patterns and customer outcome evidence |
| Platforms and telecoms | How to block scam ads, messages and calls | Takedown data and regulatory duties |
| Law enforcement | How to disrupt cross-border scam operations | Intelligence 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
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?
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.
How the evidence is built, and where it stops.
- 01
Victim research
Surveys and interviews on scam experiences, drawing on behavioural science. Under-reporting is significant.
- 02
Intervention evaluation
Testing warnings and friction. Effects vary and may wane.
- 03
Cross-sector analysis
Mapping where scams originate with network science. Data spans sectors with different rules.
- 04
Typology research
Describing scam methods, including those using AI-generated content. Must avoid enabling crime.
How it is studied, and what the methods cannot say.
- 01Artificial intelligence and machine learningReal-time fraud scoring · Alert prioritisation · Fairness in fraud controlsApplied here
- 02Network science and contagionMule network detection · Ownership chain analysis · Cross-institution patternsApplied here
- 03Data engineering, interoperability and stewardshipScreening data quality · Monitoring coverage checks · Retention and minimisationApplied here
- 04Digital identity and public digital systemsAssurance levels for onboarding · Defending against deepfakes · Inclusion testingApplied here
Questions and answers.
Why don't payment controls stop scams?
Who is responsible for scam losses?
Where should a scam victim go for help?
What should I do if I have been scammed?
Are only older people targeted?
Why involve telecoms and platforms?
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