Operational Fraud KRIs in Banking: Early Warnings Before Control Breakdowns

Fraud Losses Show Up Late

Operational fraud KRIs help banks see control stress before fraud losses appear in reporting.

Fraud losses are lagging indicators.

By the time losses, claims, charge-offs, recoveries, or reimbursement decisions appear in a dashboard, the operating environment may have already been under stress for days, weeks, or months.

The early warning signs usually show up somewhere else first:

Alerts age.
SLAs slip.
High-risk queues grow.
Analysts get overloaded.
Evidence is missing.
Escalations slow down.
Cases are reopened.
Released cases later become claims or losses.

That is where operational fraud KRIs matter.

Operational fraud KRIs are key risk indicators that show whether the fraud control environment is becoming unsafe before confirmed losses spike. They are not just productivity metrics. They are early warnings that the bank’s fraud operating model may not be able to identify, review, escalate, and respond to risk at the speed required.

The OCC frames fraud risk as a form of operational risk and says a bank’s risk management system should include policies, processes, personnel, and controls to identify, measure, monitor, and control fraud risk. The OCC also notes that the true cost of fraud often exceeds direct financial loss because investigation time, productivity loss, legal and compliance costs, and remediation also matter.^[1]

That is the right starting point for this article:

Fraud losses tell leaders what happened. Operational fraud KRIs show whether the fraud control environment is starting to fail.

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Quick Takeaways

• Operational fraud KRIs warn when fraud operations are under control stress.
• They focus on alert backlogs, SLA breaches, queue aging, analyst capacity, escalation delays, missing evidence, QA defects, system/data integrity, vendor performance, and post-release outcomes.
• They are different from fraud KPIs. KPIs tell leaders how the team performed. KRIs warn that risk is increasing.
• The best KRIs have an owner, threshold, reporting cadence, root-cause review, and action plan.
• For banking employees, data analysts, process analysts, and fraud strategists, operational fraud KRIs help answer one practical question: is the fraud process still safe under current volume and risk conditions?

This article is educational and analytical. It is not legal, compliance, investment, or financial advice, and banks should adapt KRI thresholds to their own products, controls, staffing model, and risk appetite.

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• FedNow Fraud Detection
• Banking Fraud hub
• Resources

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What Are Operational Fraud KRIs?

Operational fraud KRIs are early warning indicators that measure whether the fraud operating environment is under stress.

They do not simply ask:

How much fraud did we lose?

They ask:

Can the fraud team still detect, review, escalate, and respond to risk before customer harm or financial loss increases?

A strong operational fraud KRI usually measures one of these areas:

Operational Area	What the KRI Warns About
Alert backlog	Detection output is exceeding review capacity
SLA performance	Alerts or cases are not being reviewed on time
Queue aging	Risk is sitting unresolved
Analyst capacity	Workload is above safe operating tolerance
Review quality	Decisions may be rushed, incomplete, or inconsistent
Escalations	Fraud, AML, legal, vendor, or customer-protection handoffs are delayed
System and data integrity	Analysts do not have timely, complete, accurate information
Post-release outcomes	Cases released as low risk later become claims or losses

SAMA’s Counter-Fraud Framework is one of the clearest public frameworks for fraud KRI design. It says KRIs should monitor exposure against fraud risks, consider operational management of fraud alerts, have documented owners, be periodically reported, be forward-looking, use thresholds, and rely on complete, accurate, timely metrics. It also gives “fraud alerts not reviewed within defined service level agreements” as a forward-looking KRI example.^[2]

SAMA is not a U.S. banking rulebook, so U.S. teams should treat it as a global regulatory-style reference rather than a domestic requirement. But the design principles are highly practical for fraud operations.

A KRI is not useful just because it appears on a dashboard. It becomes useful when it triggers action.

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Operational Fraud KRIs vs. Fraud KPIs

Fraud teams need both KPIs and KRIs, but they answer different questions.

Metric Type	Main Question	Fraud Operations Example
KPI	How did we perform?	92% of fraud alerts closed within SLA
KRI	Is risk increasing?	High-risk alerts over SLA increased 40% week over week
Control metric	Is the control working?	QA defect rate stayed below tolerance
Outcome metric	What happened after the decision?	Post-release losses increased for cases closed in under five minutes

A fraud KPI tells leaders how the team performed.

An operational fraud KRI tells leaders whether the operating environment is becoming unsafe.

For example, a fraud operations dashboard may show that the team closed 10,000 alerts this month. That is a productivity KPI. But if high-risk alerts over SLA doubled, missing-evidence defects increased, and post-release claims rose, the operating environment may be deteriorating even though closure volume looks strong.

That is why operational KRIs should sit next to productivity reporting.

Volume alone does not prove control.

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Why Fraud Operations Break Before Losses Spike

Fraud operations usually break through pressure, not all at once.

A new scam pattern creates more alerts.
A rule change increases false positives.
An ACH credit-push monitoring requirement expands review volume.
A vendor data feed arrives late.
A case system outage delays queues.
A specialized queue depends on too few trained analysts.
A payment-risk alert needs customer contact, but the contact queue is backed up.

None of these issues is “fraud loss” yet. But each one can become a loss event if the control environment cannot respond fast enough.

GAO’s Fraud Risk Framework emphasizes prevention, detection, response, monitoring, and feedback. It also says managers should collect and analyze data for real-time monitoring of fraud trends and potential control deficiencies, then use the results to improve fraud risk management activities.^[3]

For banks, that means operational KRIs should not stop at reporting queue stress. They should feed root-cause analysis and process improvement.

The goal is not only to say:

The queue is aging.

The goal is to ask:

Why is the queue aging, which risk is affected, who owns the fix, and what action should happen before losses increase?

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Alert Backlog KRIs

Alert backlog KRIs show whether fraud detection output is exceeding review capacity.

A growing backlog is not just a staffing issue. It is an early warning that the fraud system may be generating more risk signals than the operation can safely process.

KRI	Calculation Idea	What It Warns About
Total alert backlog	Open alerts at cutoff time	Review volume exceeds capacity
High-risk alert backlog	Open high-risk alerts	Serious alerts are waiting
Backlog growth rate	Current backlog vs. prior period	Queue pressure is increasing
Aging distribution	Alerts by age bucket	Risk is moving into older buckets
Alerts over SLA	Open or due alerts past SLA	Review expectations are being missed
New alerts per analyst	New alerts divided by available analysts	Workload imbalance
Auto-closure spike	Auto-closed alerts vs. baseline	Automation may be suppressing too much review

For data analysts, the important point is segmentation. Total backlog is useful, but it can hide the real risk. A backlog of low-risk alerts is different from a backlog of high-risk wire, ACH, scam, account-takeover, or mule alerts.

Useful cuts include:

• product;
• payment rail;
• channel;
• fraud typology;
• risk tier;
• rule or model source;
• customer segment;
• queue owner;
• analyst skill group;
• vendor-owned vs. internal queue.

The key operational question is simple:

Are the highest-risk alerts being reviewed before the control window closes?

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SLA and Timeliness KRIs

SLA and timeliness KRIs are the heart of operational fraud monitoring.

SAMA’s framework directly names fraud alerts not reviewed within defined SLAs as a forward-looking KRI example. Its fraud detection guidance also says detection systems should operate 24/7 with appropriate resources to manage outputs on a timely basis.^[2][4]

KRI	Calculation Idea	What It Warns About
SLA breach rate	Alerts past SLA divided by alerts due	Fraud operations are missing review expectations
High-risk SLA breach rate	High-risk alerts past SLA divided by high-risk alerts due	Serious risk is aging
Time to first touch	First review timestamp minus alert timestamp	Alerts are waiting too long
Time to disposition	Disposition timestamp minus alert timestamp	Case closure is slowing
Time to escalation	Escalation timestamp minus trigger timestamp	Handoffs are delayed
Time to customer contact	First contact timestamp minus trigger timestamp	Scam intervention window is shrinking
Time to hold or release decision	Decision timestamp minus alert timestamp	Payment-risk decisions are delayed
Time to recovery action	Recovery action timestamp minus detection timestamp	Funds may leave before action

For process analysts, this is where timestamp discipline matters. A useful fraud SLA dashboard needs clean timestamps for alert creation, queue assignment, first touch, disposition, escalation, customer contact, hold/release decision, and recovery action.

For strategists, the key question is whether the SLA is aligned to the risk.

A low-risk review queue may tolerate a longer review window. A high-risk instant payment, wire, account takeover, scam, or mule-related alert may not.

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Analyst Capacity KRIs

Analyst capacity KRIs show whether the team has enough trained capacity to review the work safely.

SAMA’s fraud detection guidance says organizations should have adequate resources to manage the outputs from manual and automated fraud detection, including sufficient employees to work alerts, appropriate skills and training to complete investigations, and workflow systems to allocate alerts.^[4]

That creates several practical operational KRIs:

KRI	Calculation Idea	What It Warns About
Cases per analyst	Assigned open cases divided by available analysts	Workload imbalance
High-risk cases per analyst	High-risk assigned cases divided by skilled analysts	Expert review bottleneck
Capacity utilization	Required review minutes divided by available analyst minutes	Team running above safe capacity
Overtime dependency	Overtime hours divided by total review hours	Process is not sustainable
New analyst queue share	Cases assigned to newer analysts divided by total cases	Experience mismatch
Specialized queue coverage	Specialized alerts divided by qualified reviewers	Skill bottleneck
Unassigned alert rate	Unassigned alerts divided by open alerts	Routing or workflow gap

The best capacity metric is not just alert count.

A process analyst should estimate workload by case complexity. Ten low-risk debit card alerts are not the same as ten complex scam, mule, wire, business-email-compromise, or account-takeover investigations.

Better capacity logic looks like this:

Required review minutes by case type ÷ Available analyst minutes

That makes the KRI more realistic.

It also helps leaders avoid a common mistake: assuming a queue is manageable because the case count looks normal, even though the case mix has become more complex.

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Review Quality KRIs

Review quality KRIs show whether decisions are becoming weaker under pressure.

This is where fraud operations stop being a queue-management problem and become a control-effectiveness problem.

KRI	Calculation Idea	What It Warns About
QA defect rate	Failed QA reviews divided by sampled reviews	Review quality is declining
Missing evidence rate	Closed cases missing required evidence divided by closed cases	Decisions may not be supportable
Reopen rate	Reopened cases divided by closed cases	Cases may be closed too soon
Analyst override rate	Analyst overrides divided by model or rule decisions	Model disagreement or unclear criteria
Repeat false-positive queue rate	Repeat benign alerts divided by total alerts	Weak rule or model quality
Documentation quality score	Required fields complete or QA note score	Weak audit trail
Post-release claim rate	Claims after release divided by released cases	Risk leakage
Post-release loss rate	Loss after release divided by released cases	Control failure after decision

SAMA’s fraud detection guidance calls for feedback loops to monitor and enhance system performance by reviewing false positives, false negatives, and alerts that identified fraud. It also expects fraud detection systems to report management information on data integrity, rule and scenario effectiveness, and operational performance.^[5]

That is important because quality KRIs connect operations to outcomes.

A team can clear cases quickly and still create risk if documentation is weak, evidence is missing, or released cases later produce losses.

For data analysts, the most useful linkage is:

Case decision → Later claim/loss/reopen/QA result

That turns operational data into control intelligence.

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Escalation and Handoff KRIs

Fraud operations often break at handoffs.

A fraud analyst may need AML review.
A scam case may need customer contact.
A mule case may need account restriction.
A material incident may need senior escalation.
A payment-risk case may need recovery action.
A vendor-owned alert may need internal review.

Each handoff creates a point where risk can sit unresolved.

KRI	Calculation Idea	What It Warns About
Escalation delay rate	Escalations past SLA divided by escalations due	Handoffs are slowing
Escalation rejection rate	Rejected escalations divided by total escalations	Poor evidence or unclear criteria
Ownership transfer delay	New owner acceptance timestamp minus escalation timestamp	Cases are falling between teams
AML referral evidence defect rate	AML referrals missing required support divided by referrals	Fraud-to-AML handoff weakness
Customer intervention failure rate	Failed contacts divided by attempted contacts	Scam intervention is not reaching customers
Recovery referral delay	Recovery referral timestamp minus loss detection timestamp	Recovery window is shrinking
Significant incident escalation delay	Senior escalation timestamp minus threshold breach timestamp	Leadership visibility is late

Every high-risk fraud handoff should have an owner, timestamp, SLA, required evidence standard, and escalation path.

Without that, the dashboard may show “case pending,” but the actual risk is “no team owns the next decision.”

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System and Data Integrity KRIs

Operational fraud KRIs are only as good as the data behind them.

If alert feeds are delayed, required fields are missing, duplicate alerts flood the queue, or rule changes are not controlled, analysts may not have the information they need to make timely decisions.

SAMA’s fraud detection section is specific on this point: fraud detection systems should use timely, complete, and accurate data. It also lists controls such as data governance, de-duplication, data quality alerts, regular audit, integration testing, and regression testing for change management.^[4]

KRI	Calculation Idea	What It Warns About
Alert feed delay	Feed receipt timestamp minus source event timestamp	Monitoring is not timely
Missing data field rate	Required fields missing divided by total alerts	Analysts lack evidence
Duplicate alert rate	Duplicate alerts divided by total alerts	Queue noise
Rule outage or disabled rule count	Disabled rules by time window	Monitoring gap
Unauthorized rule change count	Unauthorized changes detected	Control tampering risk
System latency impact	Cases delayed due to platform performance	Tooling creates backlog
Data reconciliation exception rate	Mismatched records divided by reconciled records	Case data integrity issue

This category is especially important for data analysts and process analysts.

A fraud operations dashboard should not only show case performance. It should also show whether the source data and workflow systems are healthy.

The FFIEC BSA/AML Manual notes that banks should ensure monitoring systems adequately capture electronic transactions and should be alert to anomalies in account behavior, including velocity of funds. It also lists useful MIS such as funds transfer reports, new account activity reports, IP address reports, and reports that identify linked accounts.^[6]

For operational KRIs, the point is not only “detect the anomaly.” It is also:

Did the anomaly reach the right queue, with the right data, in time for action?

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Third-Party and Vendor KRIs

Fraud operations often depend on third parties: fraud platforms, processors, data vendors, case-management tools, managed-service review teams, cloud providers, identity vendors, or payment service providers.

That creates a separate set of operational KRIs.

The interagency third-party risk guidance from the Federal Reserve, FDIC, and OCC says banking organizations should tailor risk management to the level and nature of each third-party relationship, especially for higher-risk or critical activities. It also says ongoing monitoring can help confirm the quality and sustainability of third-party controls, contractual performance, issue escalation, and response to service interruptions, compliance lapses, data loss, and other risk indicators.^[7]

KRI	Calculation Idea	What It Warns About
Vendor SLA breach rate	Vendor tasks past SLA divided by vendor tasks due	Outsourced control delay
Data delivery delay	Vendor feed delay vs. SLA	Alerts lack timely data
Incomplete vendor alert feed rate	Incomplete vendor records divided by total records	Missing evidence
Vendor QA defect rate	Failed QA reviews divided by vendor-reviewed cases	Review quality issue
Platform availability impact	Downtime minutes affecting fraud review	System dependency risk
Third-party escalation delay	Vendor escalation timestamp minus trigger timestamp	Material issue reporting delay
Repeat vendor issue rate	Repeat incidents by issue type	Remediation is not working

For fraud strategists, this matters because a vendor may own the tool, but the bank still owns the risk.

If a fraud data feed is late, the customer does not care whether the delay came from the bank, vendor, processor, or platform. The control failed from the customer’s point of view.

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Payment Monitoring KRIs and ACH Credit-Push Fraud

Operational fraud KRIs are becoming more important as payment fraud monitoring expands.

Nacha’s Fraud Monitoring Rule Changes are effective in 2026 and require fraud monitoring by Originators, Third-Party Service Providers, Third-Party Senders, and ODFIs to identify ACH credit entries initiated due to fraud. RDFIs are also required to implement risk-based processes and procedures to identify ACH credit entries initiated due to fraud. Nacha says the rules are neutral on specific methods or technologies, but possible approaches include velocity checks, anomaly detection, behavioral tolerances, and pattern recognition.^[8]

That is a fraud-risk development, but it is also an operational issue.

If new monitoring rules or typologies create more alerts, banks need KRIs that answer:

• Can the team review the new volume?
• Are ACH credit alerts routed correctly?
• Are high-risk exceptions aging?
• Are customer-contact or hold decisions delayed?
• Are false positives overwhelming analysts?
• Are RDFI and ODFI processes aligned?
• Are third-party senders and processors providing timely data?

Federal Reserve Financial Services also highlights operational payment-risk tools for timely and actionable information, unusual ACH activity, early notifications or alerts, and secondary review and approval processes.^[9]

The practical takeaway:

Payment fraud monitoring does not end when an alert is generated. It ends when the alert reaches the right team, with the right evidence, early enough for action.

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KRI-to-Action Matrix

A KRI without a threshold is just a metric.

A KRI without an owner is just a dashboard decoration.

A KRI without an action plan is just reporting.

SAMA’s KRI guidance supports this directly by calling for documented KRI owners, periodic reporting, thresholds, and early action when risk exposure exceeds fraud risk appetite.^[2]

KRI Status	Meaning	Example Response
Green	Within tolerance	Continue monitoring
Yellow	Emerging stress	Review sample cases, inspect queue drivers, validate staffing
Orange	Sustained stress	Add temporary staffing, reprioritize queues, tune rules, escalate to fraud leadership
Red	Control breakdown risk	Trigger incident review, restrict auto-release, increase holds or reviews, notify risk committee

The goal is not to create a colorful dashboard.

The goal is to predefine what the team will do when risk moves.

KRI	Yellow Trigger	Orange Trigger	Red Trigger
High-risk alerts over SLA	10% above baseline	25% above baseline for two days	50% above baseline or material case aging
Missing evidence rate	Above tolerance in QA sample	Repeat issue across teams	Evidence defect tied to loss or regulatory issue
Post-release loss rate	Above baseline	Sustained trend	Material loss or repeat pattern
Vendor data-feed delay	One missed delivery	Repeated misses	Delay affects high-risk review or customer impact

Thresholds should not be copied blindly from another bank. They should reflect the bank’s products, channels, risk appetite, staffing model, case complexity, and payment speed.

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How to Build an Operational Fraud KRI Dashboard

A practical operational fraud KRI dashboard should be simple enough for leadership and detailed enough for analysts.

The best design is usually six tiles:

Dashboard Tile	What It Shows
Queue Pressure	Backlog, aging, high-risk queue size
Timeliness	SLA breach rate, first touch, escalation delay
Capacity	Cases per analyst, utilization, staffing gaps
Quality	QA defects, missing evidence, reopen rate
Outcome Leakage	Post-release claims, post-release losses
Escalation Health	Handoff delays, rejection rate, ownership gaps

Each KRI should include:

• definition;
• owner;
• threshold;
• current value;
• trend;
• impacted channel or product;
• root-cause note;
• action plan;
• next review date.

For data analysts, the dashboard starts with field quality.

Data Field	Why It Matters
alert_id	Unique alert tracking
alert_created_ts	Queue aging start
first_touch_ts	Time-to-first-review
disposition_ts	Time-to-decision
alert_risk_tier	Separates high-risk from low-risk queue
alert_channel	Digital, ACH, card, wire, branch, call center
fraud_typology	APP, ATO, mule, check, ACH, identity
analyst_id / team_id	Capacity and QA analysis
disposition_code	Outcome classification
escalation_ts	Handoff timing
qa_result	Review quality
required_evidence_complete	Evidence completeness
post_release_claim_flag	Leakage
post_release_loss_amount	Outcome leakage
vendor_owned_flag	Third-party dependency
sla_due_ts	SLA breach logic

For process analysts, the dashboard should show where work waits.

For strategists, the dashboard should show which risk appetite threshold has been breached and what action is required.

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Common Mistakes Banks Should Avoid

Mistake 1: Treating KRIs Like KPIs

Fraud KPIs show performance. Operational fraud KRIs show rising control stress.

A monthly closure rate may look strong while high-risk queues are aging.

Mistake 2: Measuring Total Backlog Without Risk Tier

A single backlog number hides the risk mix. High-risk wires, ACH credits, scams, mule alerts, and account-takeover cases need separate visibility.

Mistake 3: Tracking SLAs Without Root Cause

An SLA breach rate is only the start. Teams need to know whether the cause is staffing, rule quality, data delay, workflow routing, vendor performance, system outage, or case complexity.

Mistake 4: Ignoring Missing Evidence

A case closed without required evidence may look complete operationally but weak from a control, audit, or escalation perspective.

Mistake 5: Not Linking Case Decisions to Outcomes

Post-release claims, losses, reopen rates, and QA defects should feed back into rules, training, staffing, and process design.

Mistake 6: Reviewing Operational KRIs Too Slowly

Some KRIs can be monthly. High-risk queue aging, SLA breach rate, payment review delays, and system feed issues may need daily or intraday monitoring.

Mistake 7: No Owner, Threshold, or Action Plan

A dashboard that shows red but does not trigger action is not risk management. It is reporting.

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What Banks, Fraud Teams, and Analysts Should Do

For Fraud Operations Leaders

Start with a small set of operational KRIs that reflect actual control stress:

• high-risk alerts over SLA;
• aging distribution by risk tier;
• time to first touch;
• analyst capacity utilization;
• missing evidence rate;
• post-release claim or loss rate;
• escalation delay rate.

Define who owns each KRI, what threshold matters, and what action happens when the metric turns yellow, orange, or red.

For Data Analysts

Do not build a dashboard before validating the fields.

Make sure timestamps, SLA due dates, queue ownership, risk tier, disposition code, QA result, escalation timestamps, and post-release outcomes are consistent enough to support decision-making.

The dashboard should allow leaders to drill from trend to queue to rule to analyst group to individual case examples.

For Process Analysts

Map where work waits.

The most useful operational KRI analysis often comes from identifying bottlenecks:

• alert creation to assignment;
• assignment to first touch;
• first touch to evidence complete;
• evidence complete to disposition;
• disposition to escalation;
• escalation to owner acceptance;
• detection to recovery action.

That workflow view often shows why a KRI moved before leadership sees losses.

For Fraud Strategists

Tie KRIs to risk appetite.

A metric becomes strategic when it has a threshold, owner, and response plan. Fraud strategy teams should make sure operational KRIs connect to staffing, technology, rule tuning, vendor management, control testing, and customer-impact decisions.

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The EdEconomy View: Operational KRIs Show Whether the Fraud Process Is Still Safe

Fraud leaders often focus on losses because losses are visible.

But losses are late.

Before losses rise, the operating model usually gives warning signs. Alerts age. Queues swell. SLAs slip. Analysts rush. Evidence gaps increase. Escalations slow down. Vendor feeds delay. Released cases turn into claims.

Operational fraud KRIs help banks see those warning signs earlier.

The best fraud operations teams do not only ask:

How many cases did we close?

They ask:

Are we still reviewing the right cases, at the right time, with the right evidence, by the right people, before the control window closes?

That is the point of operational fraud KRIs.

They are not just metrics.

They are early warnings that the fraud control environment may be moving outside safe operating tolerance.

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FAQ

What are operational fraud KRIs?

Operational fraud KRIs are early warning indicators that show whether fraud operations are under control stress. They measure alert backlog, SLA breaches, queue aging, analyst capacity, escalation delays, missing evidence, QA defects, vendor issues, system/data integrity, and post-release outcomes.

How are operational fraud KRIs different from fraud KPIs?

Fraud KPIs measure performance, such as cases closed, SLA completion, or fraud losses. Operational fraud KRIs warn that risk is increasing, such as high-risk alerts aging past SLA or post-release losses rising after cases are closed.

Why do banks need operational fraud KRIs?

Banks need operational fraud KRIs because fraud losses show up late. Operational KRIs help identify control stress before it becomes a customer-impacting or financial-loss event.

What is the most important operational fraud KRI?

There is no single most important KRI for every bank, but high-risk alerts over SLA is one of the most practical indicators because it shows serious risk sitting unresolved.

Who should own operational fraud KRIs?

Ownership depends on the KRI. Fraud operations may own backlog and SLA KRIs, analytics may own dashboard logic and data quality, process teams may own workflow bottlenecks, and fraud strategy or risk committees may own thresholds and escalation actions.

How often should operational fraud KRIs be reviewed?

High-risk operational KRIs may need daily or intraday review. Broader trend metrics may be weekly or monthly. The cadence should match the speed of the fraud risk and the control window.

What should happen when an operational fraud KRI turns red?

A red KRI should trigger a predefined action plan. Examples include incident review, temporary staffing, queue reprioritization, rule tuning, additional holds or reviews, vendor escalation, senior leadership notification, or risk committee review.

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Sources

1. Office of the Comptroller of the Currency, “Operational Risk: Fraud Risk Management Principles,” OCC Bulletin 2019-37, July 24, 2019. Updated page notes reputation-risk references removed as of March 20, 2025. https://www.occ.gov/news-issuances/bulletins/2019/bulletin-2019-37.html
2. Saudi Central Bank, “4.1.4 Key Risk Indicators,” SAMA Counter-Fraud Framework Rulebook. https://rulebook.sama.gov.sa/en/414-key-risk-indicators
3. U.S. Government Accountability Office, “A Framework for Managing Fraud Risks in Federal Programs,” GAO-15-593SP, July 2015. https://www.gao.gov/assets/gao-15-593sp.pdf
4. Saudi Central Bank, “Counter-Fraud Framework,” Fraud Detection Standards and Fraud Detection Systems sections. https://rulebook.sama.gov.sa/en/counter-fraud-framework-0
5. Saudi Central Bank, “Counter-Fraud Framework,” Fraud Detection Systems, feedback loops, false positives/false negatives, data integrity, rule/scenario effectiveness, and operational performance. https://rulebook.sama.gov.sa/en/counter-fraud-framework-0
6. Federal Financial Institutions Examination Council, BSA/AML Manual, Risks Associated with Money Laundering and Terrorist Financing — Electronic Banking. https://bsaaml.ffiec.gov/manual/RisksAssociatedWithMoneyLaunderingAndTerroristFinancing/06
7. Board of Governors of the Federal Reserve System, FDIC, and OCC, “Interagency Guidance on Third-Party Relationships: Risk Management.” https://www.federalreserve.gov/frrs/guidance/interagency-guidance-on-third-party-relationships.htm
8. Nacha, “Credit-Push Fraud Monitoring Resource Center.” https://www.nacha.org/content/credit-push-fraud-monitoring-resource-center
9. Federal Reserve Financial Services, “Operational Risk Management Concerns and Tools.” https://www.frbservices.org/resources/resource-centers/risk-mgmt-toolbox/operational-risk-management.html