Interpreting, Visualizing and Sharing Results
5 minutes
5 Questions
In the Certified Fraud Examiner (CFE) context, Interpreting, Visualizing and Sharing Results is the final and often most consequential phase of fraud data analysis. After data has been collected, cleaned and tested, the examiner must translate raw analytical output into meaningful, defensible conclusions that stakeholders—management, audit committees, legal counsel, regulators or courts—can understand and act upon. Interpreting results involves evaluating anomalies, outliers, duplicate payments, Benford's Law deviations, unusual trends or relationship patterns identified through tools such as ACL, IDEA, SQL, Excel or Python. The CFE must distinguish genuine red flags from false positives caused by data quality issues, legitimate business exceptions or flawed assumptions. Interpretation requires professional skepticism, an understanding of the business context, and corroboration with other evidence such as interviews, documents and source records. Analytical findings are indicators, not proof; the examiner must avoid drawing premature conclusions about guilt. Visualizing results converts complex datasets into charts, graphs, heat maps, timelines, link analysis diagrams and dashboards using tools like Tableau, Power BI, i2 Analyst's Notebook or Excel. Effective visualization highlights patterns such as spikes in vendor payments, clustering of transactions just below approval thresholds, or hidden relationships between employees and vendors. Good visuals are accurate, uncluttered, properly labeled, scaled honestly and free of misleading distortions, because they may ultimately serve as demonstrative evidence in litigation. Sharing results means communicating findings through written reports and presentations tailored to the audience. A CFE report should state the objectives, scope, methodology, data sources, limitations, findings and recommendations in clear, objective and non-inflammatory language. Documentation must support reproducibility and preserve chain of custody for data. Sensitive information should be shared on a need-to-know basis, respecting privacy and legal privilege. Ultimately, this phase ensures analytical work drives informed decisions, supports legal proceedings, and strengthens internal controls to prevent future fraud.
Interpreting, Visualizing and Sharing Results: A Complete CFE Study Guide (Data Analysis and Reporting Tools)
Why This Topic Matters
Data analysis is only valuable if someone correctly understands what the output means and acts on it. A fraud examiner can run the most sophisticated duplicate-payment test, Benford's Law analysis, or link analysis in the world, but if the results are misread, presented in a confusing or misleading way, or shared with the wrong people, the investigation fails. The ACFE Fraud Examiners Manual treats interpretation, visualization and communication as the final and arguably most important phase of the data analysis process. On the CFE Exam (Investigation section), this topic tests whether you understand that analytical output is evidence of anomalies, not proof of fraud, that visual presentation must be accurate and audience-appropriate, and that sharing results must respect confidentiality, privilege, chain of custody and the needs of decision-makers such as management, counsel, auditors, regulators and courts.
What It Is
Interpreting, visualizing and sharing results is the stage of the data analysis process that follows planning, data acquisition, data preparation (cleansing and normalizing) and the actual execution of analytical tests. It consists of three linked activities:
1. Interpreting - Examining the output of analytical tests to determine what the results mean, separating genuine red flags from false positives, prioritizing items for follow-up, and deciding whether additional testing is required.
2. Visualizing - Translating raw data, tables and statistical output into charts, graphs, timelines, link diagrams, maps and dashboards that make patterns, trends, outliers and relationships understandable to both technical and non-technical audiences.
3. Sharing - Communicating findings through written reports, presentations and visual exhibits to the appropriate parties, in a manner that is accurate, objective, defensible, properly documented and consistent with legal and ethical obligations.
How It Works: Interpreting Results
1. Anomalies are leads, not conclusions. The single most tested principle is that data analysis identifies exceptions, anomalies and red flags. A vendor with an address matching an employee, a round-dollar invoice just under an approval threshold, or a digit frequency that deviates from Benford's Law each indicates something that warrants investigation. None of these, by itself, proves fraud. The fraud examiner must follow up with document examination, interviews, and corroborating evidence before drawing conclusions.
2. Expect and manage false positives. Automated tests routinely flag legitimate transactions. Duplicate payment tests catch legitimate recurring charges; gap tests in check numbers catch voided checks; Benford's Law flags data sets that naturally cluster (fixed-price items, assigned numbers). Good interpretation involves understanding the business context, refining filters and thresholds, and documenting why flagged items were cleared. Too many false positives waste resources; filters that are too tight create false negatives (missed fraud).
3. Understand the limitations of each technique. Benford's Law applies only to naturally occurring, non-manipulated numbers spanning several orders of magnitude; it does not apply to assigned numbers (invoice numbers, phone numbers), numbers with built-in minimums or maximums, or data sets that are too small. Statistical sampling results extrapolate to the population only when the sample is properly drawn. Correlation detected in data does not establish causation. Trend analysis is only as good as the comparability of the periods compared.
4. Verify data integrity before trusting results. If the data was incomplete, improperly joined, poorly cleansed or extracted from the wrong period, the results are unreliable. Interpretation should include reconciling the analyzed data to source totals (control totals, record counts) and understanding any transformations performed.
5. Prioritize and triage. Results should be ranked by risk, materiality, frequency and strength of indicator so that investigative resources focus on the most significant items first. Scoring or weighting multiple red flags attached to the same vendor, employee or transaction is a common approach.
6. Avoid cognitive bias. Confirmation bias (seeing what you expect), anchoring on the initial allegation, and premature closure are all risks. The examiner should consider innocent explanations and test alternative hypotheses. Objectivity is a core CFE Code of Professional Standards requirement.
7. Document the interpretation. Record the tests run, parameters used, the number of exceptions generated, how each exception was resolved, and the rationale for conclusions. This documentation supports reproducibility and the admissibility of the analysis if it is later challenged in court (for example under Daubert-type standards for expert methodology).
How It Works: Visualizing Results
Purpose of visualization. Visualization makes complex data comprehensible, reveals patterns that tables hide (spikes, clusters, outliers, cycles, relationships), supports interviews and presentations, and helps judges, juries, boards and management grasp findings quickly. Many fraud schemes become obvious only when data is displayed over time or across relationships.
Common visualization types and when to use them:
- Line charts - trends over time (monthly expense totals, revenue by period); ideal for spotting spikes or unusual seasonality.
- Bar and column charts - comparing categories (spending by vendor, transactions by employee).
- Pie charts - composition of a whole; use sparingly and only with few categories.
- Histograms - frequency distribution of values; useful for spotting clustering just below approval thresholds or for Benford's Law comparisons (expected versus actual frequency of leading digits).
- Scatter plots - relationship between two variables and identification of outliers.
- Timelines - sequencing of events, communications and transactions; essential for showing opportunity, knowledge and concealment in a fraud narrative.
- Link analysis (network) diagrams - relationships among people, entities, accounts, addresses, phone numbers and transactions; critical for uncovering shell companies, collusion, money laundering networks and conflicts of interest.
- Flowcharts - how a process or a flow of funds works and where controls failed.
- Geospatial maps - plotting addresses, locations of transactions or shipments to reveal impossible or suspicious patterns.
- Heat maps - intensity of activity across two dimensions (day of week versus hour, location versus product).
- Dashboards - interactive summaries combining multiple visuals, often used for continuous monitoring.
Tools. Spreadsheets (Excel), business intelligence tools (Tableau, Power BI, Qlik), audit analytics tools (ACL/Galvanize, IDEA), link analysis software (i2 Analyst's Notebook and similar), and statistical/programming environments (R, Python). The exam tests concepts rather than specific products.
Principles of good visualization:
- Match the chart to the message (trend, comparison, distribution, relationship, composition).
- Keep it simple; eliminate clutter, 3-D effects and unnecessary decoration.
- Label axes, units, periods, sources and legends clearly.
- Use consistent scales and colors; start quantitative axes at zero unless clearly noted.
- Highlight the key finding, but do not hide contradictory data.
- Tailor to the audience: executives need high-level summaries; counsel and auditors need detail and traceability.
Avoid misleading visuals. Truncated or broken axes that exaggerate differences, cherry-picked time ranges, inconsistent intervals, dual axes that imply false relationships, and cumulative charts that disguise declines all compromise objectivity. A fraud examiner's visuals must be accurate, complete and fair, because they may become trial exhibits and are subject to cross-examination. Every visual should be traceable back to the underlying data.
How It Works: Sharing Results
1. Know the audience. Results may be shared with management, the audit committee, legal counsel, law enforcement, regulators, insurers or a court. Each needs a different level of detail and technical language. Non-technical readers need plain language, defined terms and visuals; technical reviewers need methodology, parameters and data lineage.
2. Written reports. Data analysis findings are generally incorporated into the fraud examination report. Report standards require that findings be accurate, clear, impartial, relevant and timely. The report should state the scope and objectives, the data sources and periods, the methodology and tests performed, the limitations, the results (with exhibits), and the follow-up performed. State facts; do not express opinions on guilt or innocence. Avoid speculation and inflammatory language.
3. Methodology transparency and reproducibility. Document how data was obtained, how it was cleansed, which tests and parameters were used, and how exceptions were resolved, so another examiner could reproduce the results. This supports admissibility and credibility and protects the examiner's reputation.
4. Chain of custody and data integrity. Electronic data used in analysis is evidence. Preserve original data sets, work on copies, use hash values where appropriate, and maintain a log of who accessed and modified data. Results shared externally should be based on preserved, verifiable data.
5. Confidentiality, privacy and privilege. Share results only with those who have a legitimate need to know. Protect personal data in compliance with privacy laws (for example, GDPR or sector-specific rules). When the examination is conducted at the direction of counsel, results may be protected by attorney-client privilege or the work-product doctrine; careless distribution can waive that protection. Mark reports appropriately and control distribution.
6. Presentations and testimony. When presenting orally, lead with the conclusion, support it with a small number of clear visuals, and be prepared to explain how each number was derived. In court, visuals used as demonstrative evidence must fairly represent the underlying data, and the examiner must be able to explain the methodology in lay terms.
7. Continuous monitoring and feedback. Findings should feed back into fraud risk assessment and control improvement. Results from data analysis may be used to refine future tests, build dashboards for ongoing monitoring, and adjust thresholds to reduce false positives.
Key Terms to Remember
- Anomaly / Exception / Red flag - an item flagged by analysis that requires follow-up, not proof of fraud.
- False positive - a flagged item that turns out to be legitimate.
- False negative - a fraudulent item the test failed to flag.
- Benford's Law - expected distribution of leading digits in natural data; deviations suggest manipulation but require context.
- Link analysis - visualization of relationships among entities.
- Timeline analysis - chronological visualization of events.
- Dashboard - interactive summary of key metrics, often for continuous monitoring.
- Reproducibility - ability of another examiner to repeat the analysis and obtain the same results.
- Demonstrative evidence - charts and visuals used to illustrate testimony.
Exam Tips: Answering Questions on Interpreting, Visualizing and Sharing Results
Tip 1 - Anomaly does not equal fraud. Whenever an answer choice says that a flagged item, Benford deviation or duplicate match proves or establishes fraud, it is wrong. The correct answer will say the item should be investigated further, corroborated, or treated as a red flag.
Tip 2 - Know when Benford's Law does and does not apply. Expect questions testing that it does not work for assigned numbers (check numbers, invoice numbers, ZIP codes), numbers with set minimums or maximums, small data sets, or fixed-price items. Also know the first-digit expectation: 1 appears about 30 percent of the time, 9 about 4.6 percent.
Tip 3 - Match the visualization to the purpose. If the question asks how to show relationships among people and entities, the answer is link analysis. Sequence of events equals timeline. Trend over time equals line chart. Distribution or clustering equals histogram. Comparison of categories equals bar chart. Geographic patterns equals map.
Tip 4 - The best visual is accurate, simple and audience-appropriate. Reject choices that favor complexity, decoration, truncated axes or selective time periods. The CFE emphasizes objectivity; a visual that exaggerates or hides data is never the best answer.
Tip 5 - Reports state facts, not opinions on guilt. If a choice has the examiner concluding in a report that the suspect is guilty or committed fraud, eliminate it. Report findings should be factual, impartial, relevant, clear and timely, and should describe methodology and limitations.
Tip 6 - Documentation and reproducibility are always correct priorities. Choices emphasizing recording test parameters, retaining original data, reconciling to source totals, and enabling another examiner to repeat the work are usually right.
Tip 7 - Watch for false positive and false negative wording. Overly broad filters generate many false positives (wasted effort); overly narrow filters produce false negatives (missed fraud). The examiner refines parameters using business context rather than ignoring results.
Tip 8 - Sharing results follows need-to-know, privilege and privacy rules. Distribute only to authorized parties, protect personal data, preserve attorney-client privilege and work product when counsel directs the engagement, and maintain chain of custody over data and exhibits.
Tip 9 - Context matters more than the raw number. Questions may describe a result (for example, a spike in expenses in December) and ask the best next step. The right answer involves understanding the business explanation, verifying the data, and following up with documents or interviews, not immediately reporting fraud.
Tip 10 - Read for the phase of the data analysis process. Remember the sequence: plan, obtain data, prepare and cleanse data, perform analysis, then interpret, visualize and report. If a question asks what comes after running the tests, the answer centers on reviewing results for anomalies and false positives, following up, and communicating findings.
Quick Review Summary
- Analytical output identifies red flags; follow-up evidence establishes facts.
- Expect false positives; refine, do not abandon, tests.
- Know each technique's limitations, especially Benford's Law.
- Choose visuals by message: trend, comparison, distribution, relationship, sequence, geography.
- Keep visuals accurate, simple, labeled and traceable to data; never mislead.
- Reports must be factual, objective, clear, relevant and timely, with methodology and limitations disclosed.
- Protect data integrity, chain of custody, confidentiality and privilege when sharing.
- Document everything so results are reproducible and defensible in court.
Data analysis is only valuable if someone correctly understands what the output means and acts on it. A fraud examiner can run the most sophisticated duplicate-payment test, Benford's Law analysis, or link analysis in the world, but if the results are misread, presented in a confusing or misleading way, or shared with the wrong people, the investigation fails. The ACFE Fraud Examiners Manual treats interpretation, visualization and communication as the final and arguably most important phase of the data analysis process. On the CFE Exam (Investigation section), this topic tests whether you understand that analytical output is evidence of anomalies, not proof of fraud, that visual presentation must be accurate and audience-appropriate, and that sharing results must respect confidentiality, privilege, chain of custody and the needs of decision-makers such as management, counsel, auditors, regulators and courts.
What It Is
Interpreting, visualizing and sharing results is the stage of the data analysis process that follows planning, data acquisition, data preparation (cleansing and normalizing) and the actual execution of analytical tests. It consists of three linked activities:
1. Interpreting - Examining the output of analytical tests to determine what the results mean, separating genuine red flags from false positives, prioritizing items for follow-up, and deciding whether additional testing is required.
2. Visualizing - Translating raw data, tables and statistical output into charts, graphs, timelines, link diagrams, maps and dashboards that make patterns, trends, outliers and relationships understandable to both technical and non-technical audiences.
3. Sharing - Communicating findings through written reports, presentations and visual exhibits to the appropriate parties, in a manner that is accurate, objective, defensible, properly documented and consistent with legal and ethical obligations.
How It Works: Interpreting Results
1. Anomalies are leads, not conclusions. The single most tested principle is that data analysis identifies exceptions, anomalies and red flags. A vendor with an address matching an employee, a round-dollar invoice just under an approval threshold, or a digit frequency that deviates from Benford's Law each indicates something that warrants investigation. None of these, by itself, proves fraud. The fraud examiner must follow up with document examination, interviews, and corroborating evidence before drawing conclusions.
2. Expect and manage false positives. Automated tests routinely flag legitimate transactions. Duplicate payment tests catch legitimate recurring charges; gap tests in check numbers catch voided checks; Benford's Law flags data sets that naturally cluster (fixed-price items, assigned numbers). Good interpretation involves understanding the business context, refining filters and thresholds, and documenting why flagged items were cleared. Too many false positives waste resources; filters that are too tight create false negatives (missed fraud).
3. Understand the limitations of each technique. Benford's Law applies only to naturally occurring, non-manipulated numbers spanning several orders of magnitude; it does not apply to assigned numbers (invoice numbers, phone numbers), numbers with built-in minimums or maximums, or data sets that are too small. Statistical sampling results extrapolate to the population only when the sample is properly drawn. Correlation detected in data does not establish causation. Trend analysis is only as good as the comparability of the periods compared.
4. Verify data integrity before trusting results. If the data was incomplete, improperly joined, poorly cleansed or extracted from the wrong period, the results are unreliable. Interpretation should include reconciling the analyzed data to source totals (control totals, record counts) and understanding any transformations performed.
5. Prioritize and triage. Results should be ranked by risk, materiality, frequency and strength of indicator so that investigative resources focus on the most significant items first. Scoring or weighting multiple red flags attached to the same vendor, employee or transaction is a common approach.
6. Avoid cognitive bias. Confirmation bias (seeing what you expect), anchoring on the initial allegation, and premature closure are all risks. The examiner should consider innocent explanations and test alternative hypotheses. Objectivity is a core CFE Code of Professional Standards requirement.
7. Document the interpretation. Record the tests run, parameters used, the number of exceptions generated, how each exception was resolved, and the rationale for conclusions. This documentation supports reproducibility and the admissibility of the analysis if it is later challenged in court (for example under Daubert-type standards for expert methodology).
How It Works: Visualizing Results
Purpose of visualization. Visualization makes complex data comprehensible, reveals patterns that tables hide (spikes, clusters, outliers, cycles, relationships), supports interviews and presentations, and helps judges, juries, boards and management grasp findings quickly. Many fraud schemes become obvious only when data is displayed over time or across relationships.
Common visualization types and when to use them:
- Line charts - trends over time (monthly expense totals, revenue by period); ideal for spotting spikes or unusual seasonality.
- Bar and column charts - comparing categories (spending by vendor, transactions by employee).
- Pie charts - composition of a whole; use sparingly and only with few categories.
- Histograms - frequency distribution of values; useful for spotting clustering just below approval thresholds or for Benford's Law comparisons (expected versus actual frequency of leading digits).
- Scatter plots - relationship between two variables and identification of outliers.
- Timelines - sequencing of events, communications and transactions; essential for showing opportunity, knowledge and concealment in a fraud narrative.
- Link analysis (network) diagrams - relationships among people, entities, accounts, addresses, phone numbers and transactions; critical for uncovering shell companies, collusion, money laundering networks and conflicts of interest.
- Flowcharts - how a process or a flow of funds works and where controls failed.
- Geospatial maps - plotting addresses, locations of transactions or shipments to reveal impossible or suspicious patterns.
- Heat maps - intensity of activity across two dimensions (day of week versus hour, location versus product).
- Dashboards - interactive summaries combining multiple visuals, often used for continuous monitoring.
Tools. Spreadsheets (Excel), business intelligence tools (Tableau, Power BI, Qlik), audit analytics tools (ACL/Galvanize, IDEA), link analysis software (i2 Analyst's Notebook and similar), and statistical/programming environments (R, Python). The exam tests concepts rather than specific products.
Principles of good visualization:
- Match the chart to the message (trend, comparison, distribution, relationship, composition).
- Keep it simple; eliminate clutter, 3-D effects and unnecessary decoration.
- Label axes, units, periods, sources and legends clearly.
- Use consistent scales and colors; start quantitative axes at zero unless clearly noted.
- Highlight the key finding, but do not hide contradictory data.
- Tailor to the audience: executives need high-level summaries; counsel and auditors need detail and traceability.
Avoid misleading visuals. Truncated or broken axes that exaggerate differences, cherry-picked time ranges, inconsistent intervals, dual axes that imply false relationships, and cumulative charts that disguise declines all compromise objectivity. A fraud examiner's visuals must be accurate, complete and fair, because they may become trial exhibits and are subject to cross-examination. Every visual should be traceable back to the underlying data.
How It Works: Sharing Results
1. Know the audience. Results may be shared with management, the audit committee, legal counsel, law enforcement, regulators, insurers or a court. Each needs a different level of detail and technical language. Non-technical readers need plain language, defined terms and visuals; technical reviewers need methodology, parameters and data lineage.
2. Written reports. Data analysis findings are generally incorporated into the fraud examination report. Report standards require that findings be accurate, clear, impartial, relevant and timely. The report should state the scope and objectives, the data sources and periods, the methodology and tests performed, the limitations, the results (with exhibits), and the follow-up performed. State facts; do not express opinions on guilt or innocence. Avoid speculation and inflammatory language.
3. Methodology transparency and reproducibility. Document how data was obtained, how it was cleansed, which tests and parameters were used, and how exceptions were resolved, so another examiner could reproduce the results. This supports admissibility and credibility and protects the examiner's reputation.
4. Chain of custody and data integrity. Electronic data used in analysis is evidence. Preserve original data sets, work on copies, use hash values where appropriate, and maintain a log of who accessed and modified data. Results shared externally should be based on preserved, verifiable data.
5. Confidentiality, privacy and privilege. Share results only with those who have a legitimate need to know. Protect personal data in compliance with privacy laws (for example, GDPR or sector-specific rules). When the examination is conducted at the direction of counsel, results may be protected by attorney-client privilege or the work-product doctrine; careless distribution can waive that protection. Mark reports appropriately and control distribution.
6. Presentations and testimony. When presenting orally, lead with the conclusion, support it with a small number of clear visuals, and be prepared to explain how each number was derived. In court, visuals used as demonstrative evidence must fairly represent the underlying data, and the examiner must be able to explain the methodology in lay terms.
7. Continuous monitoring and feedback. Findings should feed back into fraud risk assessment and control improvement. Results from data analysis may be used to refine future tests, build dashboards for ongoing monitoring, and adjust thresholds to reduce false positives.
Key Terms to Remember
- Anomaly / Exception / Red flag - an item flagged by analysis that requires follow-up, not proof of fraud.
- False positive - a flagged item that turns out to be legitimate.
- False negative - a fraudulent item the test failed to flag.
- Benford's Law - expected distribution of leading digits in natural data; deviations suggest manipulation but require context.
- Link analysis - visualization of relationships among entities.
- Timeline analysis - chronological visualization of events.
- Dashboard - interactive summary of key metrics, often for continuous monitoring.
- Reproducibility - ability of another examiner to repeat the analysis and obtain the same results.
- Demonstrative evidence - charts and visuals used to illustrate testimony.
Exam Tips: Answering Questions on Interpreting, Visualizing and Sharing Results
Tip 1 - Anomaly does not equal fraud. Whenever an answer choice says that a flagged item, Benford deviation or duplicate match proves or establishes fraud, it is wrong. The correct answer will say the item should be investigated further, corroborated, or treated as a red flag.
Tip 2 - Know when Benford's Law does and does not apply. Expect questions testing that it does not work for assigned numbers (check numbers, invoice numbers, ZIP codes), numbers with set minimums or maximums, small data sets, or fixed-price items. Also know the first-digit expectation: 1 appears about 30 percent of the time, 9 about 4.6 percent.
Tip 3 - Match the visualization to the purpose. If the question asks how to show relationships among people and entities, the answer is link analysis. Sequence of events equals timeline. Trend over time equals line chart. Distribution or clustering equals histogram. Comparison of categories equals bar chart. Geographic patterns equals map.
Tip 4 - The best visual is accurate, simple and audience-appropriate. Reject choices that favor complexity, decoration, truncated axes or selective time periods. The CFE emphasizes objectivity; a visual that exaggerates or hides data is never the best answer.
Tip 5 - Reports state facts, not opinions on guilt. If a choice has the examiner concluding in a report that the suspect is guilty or committed fraud, eliminate it. Report findings should be factual, impartial, relevant, clear and timely, and should describe methodology and limitations.
Tip 6 - Documentation and reproducibility are always correct priorities. Choices emphasizing recording test parameters, retaining original data, reconciling to source totals, and enabling another examiner to repeat the work are usually right.
Tip 7 - Watch for false positive and false negative wording. Overly broad filters generate many false positives (wasted effort); overly narrow filters produce false negatives (missed fraud). The examiner refines parameters using business context rather than ignoring results.
Tip 8 - Sharing results follows need-to-know, privilege and privacy rules. Distribute only to authorized parties, protect personal data, preserve attorney-client privilege and work product when counsel directs the engagement, and maintain chain of custody over data and exhibits.
Tip 9 - Context matters more than the raw number. Questions may describe a result (for example, a spike in expenses in December) and ask the best next step. The right answer involves understanding the business explanation, verifying the data, and following up with documents or interviews, not immediately reporting fraud.
Tip 10 - Read for the phase of the data analysis process. Remember the sequence: plan, obtain data, prepare and cleanse data, perform analysis, then interpret, visualize and report. If a question asks what comes after running the tests, the answer centers on reviewing results for anomalies and false positives, following up, and communicating findings.
Quick Review Summary
- Analytical output identifies red flags; follow-up evidence establishes facts.
- Expect false positives; refine, do not abandon, tests.
- Know each technique's limitations, especially Benford's Law.
- Choose visuals by message: trend, comparison, distribution, relationship, sequence, geography.
- Keep visuals accurate, simple, labeled and traceable to data; never mislead.
- Reports must be factual, objective, clear, relevant and timely, with methodology and limitations disclosed.
- Protect data integrity, chain of custody, confidentiality and privilege when sharing.
- Document everything so results are reproducible and defensible in court.
Test mode:
CFE - Interpreting, Visualizing and Sharing Results Example Questions
Test your knowledge of Interpreting, Visualizing and Sharing Results
Question 1
A fraud examiner's data analysis shows that one retail branch processes far more refunds than any other branch in the organization. Before reporting the result, the examiner learns that this branch is the only location that handles returns of online orders. Which of the following is the examiner's BEST course of action in interpreting this result?
Question 2
When a fraud examiner interprets the output of data analysis tests, which of the following BEST describes a false positive?
Question 3
A fraud examiner runs a duplicate payment test on a year of vendor disbursements, matching on vendor number and invoice amount. The test returns several hundred matches. A sample of the matches shows that most are monthly lease installments and fixed service fees billed at the same amount each month. What should the examiner do NEXT to interpret the results reliably?
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