What Is Fraud Risk Management?

Fraud Risk Management is the coordinated process of identifying, assessing, preventing, detecting, investigating and responding to fraud.

A complete framework generally includes:

1. Fraud      governance

2. Fraud      risk assessment

3. Prevention      controls

4. Detection      and monitoring

5. Alert      review

6. Investigation

7. Customer      communication

8. Loss      and reimbursement decisions

9. Recovery

10. Regulatory      and law-enforcement reporting

11. Root-cause      analysis

12. Control      improvement

Fraud risk management should apply to:

• External      fraud

• Internal      fraud

• Customer      fraud

• Third-party      fraud

• Cyber-enabled      fraud

• Collusive      fraud

• Attempted      fraud

• Successful      fraud

The framework should also distinguish fraud from related risks such as money laundering, cybersecurity incidents, customer disputes and operational errors.

Fraud Risk Governance

Fraud governance establishes how the organization oversees fraud risk and makes decisions.

The governance framework may define:

• Board      oversight

• Senior-management      responsibilities

• Fraud-risk      ownership

• First-line      responsibilities

• Compliance      and risk oversight

• Investigation      responsibilities

• Information-security      responsibilities

• Customer-service      responsibilities

• Legal      responsibilities

• Reporting      thresholds

• Escalation      procedures

• Risk      acceptance

• Control      approval

• Incident      response

Fraud risk often crosses multiple departments.

A payment may involve:

• Customer      onboarding

• Authentication

• Transaction      monitoring

• Customer      support

• AML      review

• Cybersecurity      investigation

• Reimbursement      assessment

• Regulatory      reporting

Without clear governance, teams may investigate the same incident separately or assume another function is responsible.

Fraud Risk Appetite

Fraud risk appetite defines the types and levels of fraud exposure the organization is prepared to accept in pursuit of its business objectives.

Fraud appetite may address:

• Financial      loss

• Customer      harm

• Fraud      rates

• Reimbursement      exposure

• Account-opening      risk

• Payment-channel      risk

• Merchant      risk

• Geographic      risk

• Manual      overrides

• Control      exceptions

• Alert      backlogs

• Investigation      timelines

• Recovery      rates

• Product-launch      risk

Fraud risk appetite should influence:

• Product      design

• Customer      onboarding

• Authentication

• Transaction      limits

• Monitoring      thresholds

• Staffing

• Escalation

• Risk      acceptance

Organizations generally have no appetite for deliberate fraud, but they may still need to establish measurable tolerances for attempted fraud, operational losses and control performance.

Fraud Risk Assessment

A fraud risk assessment identifies how fraud could occur and whether existing controls reduce the exposure effectively.

The assessment may consider:

• Products

• Customers

• Jurisdictions

• Delivery      channels

• Payment      methods

• Transaction      values

• Transaction      velocity

• Account      access

• Authentication

• Refunds

• Chargebacks

• Agents

• Merchants

• Vendors

• Employees

• Technology

• Data

• Customer      support

• Business      growth

A fraud risk assessment should identify:

• Fraud      scenario

• Threat      actor

• Method

• Vulnerability

• Potential      impact

• Existing      controls

• Control      effectiveness

• Residual      risk

• Additional      action required

• Responsible      owner

The assessment should include attempted fraud as well as successful loss events.

A high number of blocked attempts may indicate that controls are working, but it may also show that the product is being actively targeted.

Fraud Typology Library

Organizations should maintain a current understanding of the fraud methods relevant to their products.

A fraud typology library may include:

• Description      of the fraud method

• Products      affected

• Customer      segments affected

• Known      indicators

• Transaction      patterns

• Authentication      indicators

• Device      indicators

• Behavioural      indicators

• Common      loss channels

• Required      controls

• Escalation      procedures

• Relevant      reporting requirements

The typology library should be informed by:

• Internal      fraud cases

• Industry      intelligence

• Law-enforcement      alerts

• Regulatory      publications

• Banking-partner      feedback

• Payment-scheme      information

• Customer      complaints

• Threat      intelligence

Fraud typologies should be translated into operational controls and monitoring rules.

Identity Fraud

Identity fraud occurs when a person uses stolen, false or manipulated identity information to obtain access to products or services.

It may involve:

• Stolen      identity documents

• Altered      documents

• Synthetic      identities

• Impersonation

• Deepfake      technology

• Compromised      personal information

• False      business records

• Misrepresented      beneficial owners

Controls may include:

• Identity-document      verification

• Biometric      comparison

• Liveness      testing

• Database      checks

• Address      verification

• Device      intelligence

• Email      and telephone verification

• Duplicate-identity      detection

• Beneficial-ownership      review

• Manual      escalation

Identity controls should be risk-based and periodically tested against evolving fraud methods.

Application Fraud

Application fraud occurs when false or misleading information is provided during onboarding or product application.

Examples include:

• False      employment

• False      income

• False      business activity

• False      address

• Hidden      ownership

• Misrepresented      transaction purpose

• Fraudulent      documentation

• Multiple      applications using related identities

Application fraud may be committed by:

• A      third-party fraudster

• The      applicant

• An      intermediary

• An      employee

• A      collusive group

Controls should consider inconsistencies across data sources rather than evaluating each data point in isolation.

Synthetic Identity Fraud

Synthetic identity fraud involves combining real and fabricated information to create an apparently legitimate identity.

A synthetic identity may use:

• A      real identification number

• A      false name

• A      fabricated address

• A      newly created email

• A      controlled telephone number

• Artificial      transaction history

Synthetic identities may be difficult to detect because they can be cultivated over time before being used for larger fraud.

Indicators may include:

• Thin      identity history

• Unusual      combinations of personal information

• Multiple      identities connected to one device

• Shared      addresses or telephone numbers

• Rapid      increases in transaction activity

• Inconsistent      credit or identity data

Account Takeover

Account takeover occurs when a fraudster gains unauthorized access to a legitimate customer account.

Methods may include:

• Credential      stuffing

• Phishing

• Malware

• SIM      swapping

• Social      engineering

• Password      reset abuse

• Compromised      email

• Remote-access      software

• Customer-support      manipulation

Controls may include:

• Multi-factor      authentication

• Device      recognition

• Behavioural      analytics

• Login      monitoring

• Step-up      authentication

• Session      controls

• Password-reset      controls

• Transaction      confirmation

• Customer      alerts

• Cooling-off      periods

Account takeover controls should also protect the processes used to recover an account, since fraudsters may target support channels when automated access is blocked.

Authorized Push Payment Fraud

Authorized push payment fraud occurs when a customer is deceived or manipulated into authorizing a payment to a fraudster.

The payment may be technically authorized, but the customer’s decision is based on false information.

Common examples include:

• Impersonation      scams

• Investment      scams

• Romance      scams

• Purchase      scams

• Invoice      redirection

• Business      email compromise

• Bank      impersonation

• Government      impersonation

Controls may include:

• Confirmation      of payee

• Payment      warnings

• Behavioural      analytics

• New-beneficiary      controls

• Transaction      delays

• Customer      contact

• High-risk      payment review

• Scam      education

• Enhanced      monitoring

• Recipient-account      intelligence

Fraud prevention should consider both the sending and receiving side of the payment.

Mule accounts and fraud proceeds may be identified through unusual inbound activity, rapid dissipation or connections to known fraudulent beneficiaries.

Payment Card Fraud

Payment card fraud can include:

• Card-not-present      fraud

• Counterfeit-card      fraud

• Lost      or stolen cards

• Account      takeover

• Merchant      fraud

• Friendly      fraud

• Card      testing

• Refund      abuse

Controls may include:

• Transaction      scoring

• Strong      customer authentication

• Card      verification

• Tokenization

• Merchant      monitoring

• Velocity      rules

• Geographic      rules

• Device      intelligence

• Chargeback      analysis

• Transaction      confirmation

Fraud strategy should distinguish between customer fraud, merchant fraud and third-party compromise.

Business Email Compromise

Business email compromise involves a fraudster impersonating a senior employee, vendor, customer or business partner to redirect a payment.

Fraudsters may:

• Compromise      an email account

• Create      a similar domain

• Change      invoice instructions

• Impersonate      an executive

• Intercept      an existing payment conversation

Controls may include:

• Independent      payment verification

• Callback      procedures

• Dual      approval

• Bank-detail      change controls

• Domain      monitoring

• Employee      training

• Transaction      limits

• Unusual-payment      review

Payment verification should use trusted contact details rather than information contained in the potentially fraudulent request.

First-Party Fraud

First-party fraud occurs when a customer intentionally misrepresents information or abuses a product for personal benefit.

Examples include:

• False      claims of unauthorized transactions

• Deliberate      chargeback abuse

• Misrepresented      identity or income

• Intentional      overdraft or credit abuse

• Refund      abuse

• Account      cycling

• Collusion      with merchants or recipients

First-party fraud can be difficult to distinguish from legitimate customer disputes.

Controls should support fair investigations and avoid treating genuine victims as fraudsters without sufficient evidence.

Internal Fraud

Internal fraud is committed or facilitated by employees, contractors or other insiders.

Examples include:

• Unauthorized      payments

• Manipulation      of customer records

• Misappropriation      of funds

• Override      abuse

• Data      theft

• Creation      of false accounts

• Collusion      with external fraudsters

• Concealment      of errors or losses

• Expense      fraud

Internal fraud controls may include:

• Segregation      of duties

• Access      management

• Dual      approval

• Employee      screening

• Mandatory      leave

• Transaction      monitoring

• Privileged-access      monitoring

• Whistleblowing

• Conflict-of-interest      controls

• Audit      trails

Senior personnel and privileged users may require enhanced oversight because they can bypass ordinary controls.

Merchant Fraud

Merchant fraud may involve:

• False      transactions

• Transaction      laundering

• Collusive      payments

• Refund      abuse

• Undelivered      goods

• Counterfeit      goods

• Prohibited      activity

• Bust-out      schemes

• Misrepresented      business models

Merchant controls may include:

• Onboarding      due diligence

• Website      review

• Beneficial-ownership      checks

• Transaction      monitoring

• Chargeback      monitoring

• Settlement      holds

• Reserve      requirements

• Site      inspections

• Periodic      review

• Network      analysis

Changes in transaction volume, geography, customer profile or average ticket size may indicate merchant fraud or undisclosed business activity.

Mule-Account Risk

Money mules receive and transfer fraud proceeds on behalf of other criminals.

A mule may be:

• Knowingly      involved

• Reckless

• Deceived

• Coerced

• A      victim whose account has been compromised

Indicators may include:

• Unexpected      inbound payments

• Multiple      unrelated senders

• Rapid      outbound transfers

• Cash      withdrawals

• Cryptocurrency      purchases

• Use      of newly opened accounts

• Little      ordinary customer activity

• Payments      inconsistent with the customer profile

Mule-account controls should connect fraud detection with AML investigation and suspicious transaction reporting.

Social Engineering

Social engineering manipulates people into revealing information, granting access or authorizing a transaction.

Fraudsters may create urgency, fear or trust by impersonating:

• Banks

• Regulators

• Police

• Government      agencies

• Employers

• Family      members

• Technology      companies

• Investment      advisors

Controls should not rely entirely on customers recognizing a scam.

Organizations should design payment journeys that create meaningful opportunities to identify and interrupt manipulation.

Fraud Prevention

Fraud prevention controls are intended to stop fraud before loss occurs.

They may include:

• Customer      verification

• Identity      checks

• Authentication

• Transaction      limits

• Beneficiary      verification

• Dual      approval

• Device      intelligence

• Behavioural      monitoring

• Employee      training

• Customer      education

• Access      restrictions

• Vendor      controls

• Product      design

• Manual      review

• Real-time      payment intervention

Prevention should be embedded into the customer and transaction journey rather than added after the product is launched.

Fraud Detection and Monitoring

Fraud monitoring should identify activity that may require intervention or investigation.

Monitoring methods may include:

• Rules-based      alerts

• Statistical      models

• Machine      learning

• Device      analytics

• Behavioural      biometrics

• Network      analysis

• Customer      reports

• Employee      reports

• Chargeback      data

• Complaint      data

• External      intelligence

Fraud scenarios may examine:

• Transaction      amount

• Velocity

• New      beneficiary

• New      device

• Geographic      change

• Unusual      access time

• Rapid      movement of funds

• Multiple      failed logins

• Password-reset      activity

• Shared      devices

• Linked      accounts

• Unusual      merchant behaviour

Monitoring should reflect the speed of the product.

Real-time payments may require real-time or near-real-time fraud decisioning because funds may be difficult to recover after release.

Fraud Rules and Detection Models

Fraud rules and models should be governed throughout their lifecycle.

Governance may include:

• Documented      purpose

• Data      sources

• Threshold      rationale

• Approval

• Testing

• Implementation

• Performance      monitoring

• Change      control

• Override      procedures

• Periodic      validation

• Retirement

Performance measures may include:

• Detection      rate

• False-positive      rate

• Fraud      prevented

• Fraud      missed

• Customer      friction

• Alert      volume

• Review      time

• Model      drift

• Loss      rate

A model can reduce fraud while still creating unacceptable customer harm if it blocks large numbers of legitimate transactions.

Fraud Alerts

Fraud alerts should be prioritized and investigated within defined timeframes.

Alert-management procedures may address:

• Risk      scoring

• Priority

• Assignment

• Investigation      steps

• Customer      contact

• Payment      hold

• Escalation

• Account      restriction

• Documentation

• Closure

• Quality      assurance

Alert backlogs can expose customers and the organization to continuing loss.

Management should monitor both the number and age of unresolved alerts.

Fraud Investigations

A fraud investigation should establish what happened, who was affected and what action is required.

An investigation may include:

• Customer      interview

• Transaction      review

• Device      review

• Login      review

• Authentication      history

• Communication      review

• Beneficiary      analysis

• Linked-account      review

• Employee      activity

• Vendor      information

• Law-enforcement      information

The investigation should distinguish between:

• Unauthorized      activity

• Authorized      scam payments

• Customer      error

• First-party      fraud

• Account      compromise

• Operational      error

• Disputed      service

Investigation decisions should be supported by evidence and documented clearly.

Customer Contact

Customer contact can be a critical fraud control.

Effective contact procedures should consider:

• Trusted      contact information

• Authentication

• Urgency

• Customer      vulnerability

• Potential      coercion

• Social      engineering

• Language      needs

• Record      keeping

• Escalation

A fraudster may control the customer’s email or telephone number.

Organizations should therefore consider whether alternative or step-up verification is required when account details have recently changed.

Payment Holds and Account Restrictions

The organization may need to delay or restrict activity while investigating potential fraud.

Controls should define:

• Authority      to place a hold

• Maximum      duration

• Review      requirements

• Customer      communication

• Legal      or regulatory limitations

• Escalation

• Release      approval

• Record      keeping

Restrictions should be proportionate.

An unnecessary or prolonged restriction can create serious customer harm, particularly where the account is used for essential living or business expenses.

Customer Reimbursement and Liability

Fraud reimbursement decisions should be consistent, evidence-based and aligned with applicable legal, regulatory and contractual requirements.

The framework may address:

• Unauthorized      transactions

• Authorized      scam payments

• Customer      negligence

• Customer      vulnerability

• Gross      negligence

• Employee      error

• System      failure

• Merchant      disputes

• Timing

• Required      evidence

• Partial      reimbursement

• Appeals

• Complaints

• Recovery

The organization should avoid using reimbursement as a substitute for correcting weak controls.

High reimbursement costs may indicate deficiencies in onboarding, authentication, payment warnings, monitoring or customer support.

Fraud Recovery

Fraud recovery may involve:

• Payment      recall

• Bank      notification

• Freezing      recipient funds

• Scheme      processes

• Chargebacks

• Insurance      claims

• Civil      recovery

• Law-enforcement      referral

• Asset      tracing

Recovery procedures should begin quickly because fraud proceeds may be moved through several accounts within a short period.

The organization should maintain current contacts and escalation routes with relevant banks, schemes, processors and law-enforcement bodies.

Fraud Incident Management

Material fraud events should be managed through a formal incident process.

The process may include:

• Detection

• Initial      containment

• Severity      assessment

• Customer      protection

• Financial      impact

• Regulatory      assessment

• Legal      review

• Communication

• Recovery

• Root-cause      analysis

• Remediation

• Closure

• Lessons      learned

Fraud incidents may overlap with:

• Cybersecurity      incidents

• Data      breaches

• AML      concerns

• Sanctions      breaches

• Operational      incidents

• Customer      complaints

The incident framework should ensure these connected obligations are considered.

Root-Cause Analysis

Root-cause analysis should look beyond the immediate fraudulent transaction.

Potential root causes may include:

• Weak      onboarding

• Inadequate      authentication

• Poorly      calibrated monitoring

• Insufficient      staffing

• Alert      backlogs

• Weak      customer warnings

• Vendor      failures

• Product-design      weaknesses

• Inadequate      change management

• Unclear      procedures

• Training      gaps

• Governance      failures

A customer being deceived is not always the complete root cause.

The organization should also assess whether its systems, warnings, transaction journey or support processes created avoidable vulnerability.

Fraud and AML

Fraud and money laundering are closely connected but are not identical.

Fraud generally concerns deception and financial gain.

Money laundering concerns the movement, concealment or use of criminal property.

Fraud proceeds may generate AML obligations.

A fraud investigation may identify:

• Mule      accounts

• Organized      criminal networks

• Proceeds      of crime

• Suspicious      beneficiaries

• Linked      customers

• Unusual      transaction patterns

Fraud and AML teams should exchange information, but responsibilities and reporting decisions should remain clearly defined.

Fraud and Cybersecurity

Many fraud events involve compromised technology, credentials or data.

Cyber-enabled fraud may involve:

• Phishing

• Malware

• Credential      theft

• Session      hijacking

• SIM      swapping

• Remote-access      tools

• API      compromise

• Bot      activity

• Data      breaches

Fraud and information-security teams should coordinate on:

• Threat      intelligence

• Device      indicators

• Account      access

• Incident      containment

• Customer      notification

• Root      cause

• Control      remediation

A cyber incident may create fraud risk even where no immediate financial loss has occurred.

Fraud and Customer Vulnerability

Vulnerable customers may be more susceptible to scams or coercion.

Vulnerability may arise from:

• Age

• Illness

• Bereavement

• Financial      distress

• Limited      financial literacy

• Cognitive      impairment

• Language      barriers

• Domestic      abuse

• Digital      exclusion

Fraud controls should help identify when a customer may require additional support.

The organization should avoid assuming that a customer’s authorization proves the payment was genuinely informed or free from manipulation.

Fraud and Product Governance

Product design can materially affect fraud exposure.

A fraud assessment should be performed when introducing or changing:

• Payment      limits

• Instant      transfers

• New      beneficiaries

• Account      recovery

• Digital      wallets

• Virtual      cards

• Cross-border      payments

• Open      banking

• Cryptocurrency      features

• Merchant      settlement

• Refund      processes

The assessment should consider:

• Likely      fraud scenarios

• Customer      vulnerability

• Detection      capability

• Reversibility

• Recovery      options

• Customer      communications

• Operational      capacity

Fraud controls should be included before launch rather than added only after losses occur.

Fraud and Third-Party Risk

Vendors may support critical fraud controls, including:

• Identity      verification

• Device      intelligence

• Transaction      monitoring

• Authentication

• Payment      processing

• Chargeback      management

• Fraud      analytics

• Customer      communication

Vendor oversight should consider:

• Detection      performance

• False      positives

• Service      availability

• Data      quality

• Model      changes

• Incident      notification

• Cybersecurity

• Subcontractors

• Business      continuity

• Exit      planning

The organization should understand how the vendor’s system works sufficiently to oversee its effectiveness.

Fraud and Artificial Intelligence

Artificial intelligence and machine learning can improve fraud detection by identifying complex behavioural and network patterns.

They can also create governance challenges involving:

• Data      quality

• Bias

• Explainability

• Model      drift

• False      positives

• Customer      impact

• Privacy

• Human      oversight

• Vendor      dependency

AI-based fraud models should be tested, monitored and subject to change control.

Important customer decisions should not rely on an opaque model without appropriate review and escalation.

Key Risk Indicators

Fraud key risk indicators can help management identify changes in exposure.

Examples include:

• Fraud      losses

• Fraud      losses as a percentage of volume

• Attempted      fraud

• Prevented      fraud

• Reimbursement      costs

• Recovery      rate

• Alert      volumes

• Alert      backlog

• Investigation      time

• False-positive      rate

• Account      takeover rate

• Scam-payment      rate

• Chargeback      rate

• Mule-account      detection

• Customer      complaints

• Repeat      victims

• Internal      fraud cases

• Fraud      by product or channel

Indicators should have thresholds and escalation requirements.

A low loss rate does not necessarily indicate low risk if attempted fraud is increasing or detection controls are weakening.

Fraud Management Information

Fraud reporting should support operational and strategic decisions.

Management information may include:

• Fraud      trends

• Typologies

• Product      exposure

• Customer      impact

• Geographic      exposure

• Detection      performance

• Losses

• Recoveries

• Reimbursements

• Investigation      outcomes

• Control      gaps

• Vendor      performance

• Root      causes

• Remediation      status

Reports should distinguish between:

• Attempted      fraud

• Prevented      fraud

• Suspected      fraud

• Confirmed      fraud

• Gross      loss

• Net      loss

• Reimbursed      loss

• Recovered      funds

Consistent definitions improve trend analysis and regulatory reporting.

Fraud Record Keeping

Fraud records should support investigations, complaints, regulatory reporting and legal proceedings.

Records may include:

• Alerts

• Case      notes

• Evidence

• Customer      communications

• Authentication      data

• Transaction      records

• Device      information

• Decisions

• Reimbursement      analysis

• Recovery      activity

• Regulatory      reports

• Law-enforcement      referrals

• Closure      rationale

Access to fraud records should be restricted because they may contain sensitive personal, security and investigative information.

Fraud Training and Awareness

Fraud training should be tailored to employees’ responsibilities.

Training may cover:

• Fraud      typologies

• Social      engineering

• Customer      vulnerability

• Internal      escalation

• Payment      controls

• Customer      communication

• Evidence      preservation

• Insider      fraud

• Data      protection

• Confidentiality

Customer-facing employees may need specific training to recognize:

• Coercion

• Scam      narratives

• Unusual      urgency

• Account      takeover

• Impersonation

• Vulnerability

Training effectiveness should be assessed rather than measured only by completion.

Fraud Control Testing

Fraud controls should be tested periodically.

Testing may assess:

• Identity      verification

• Authentication

• Transaction      rules

• Alert      generation

• Case      handling

• Escalation

• Payment      holds

• Customer      contact

• Reimbursement

• Recovery

• Management      reporting

Testing methods may include:

• Sample      reviews

• Scenario      testing

• Rule      validation

• Data      analysis

• Control      walkthroughs

• Simulated      fraud attempts

• Post-incident      reviews

The objective is to determine whether the control operates effectively under realistic conditions.

Independent Review

An independent review can provide assurance over the fraud risk management framework.

The review may assess:

• Governance

• Risk      assessment

• Fraud      strategy

• Prevention

• Detection

• Investigations

• Reimbursement

• Recovery

• Record      keeping

• Reporting

• Training

• Third      parties

• Technology

• Remediation

Independent reviews should consider both framework design and operating effectiveness.

Regulatory Reporting and Law-Enforcement Cooperation

Fraud incidents may trigger obligations involving:

• Regulatory      reporting

• Suspicious      transaction reporting

• Data-breach      notification

• Payment-scheme      reporting

• Customer      notification

• Law-enforcement      referral

• Insurance      notification

• Banking-partner      notification

The organization should establish clear criteria for determining which reports are required.

Fraud teams should coordinate with compliance, legal, privacy, cybersecurity and AML functions to avoid incomplete or inconsistent reporting.