CFE-Investigation Exam Dumps - Certified Fraud Examiner (CFE) - Investigation

The Fraud Examiners Manual advises:

“Do not slow down the interview process for note-taking. Instead, jot down key words … In general, it is better to err on the side of taking too few notes rather than too many.”

Therefore, the best practice is C.

The 2014 International Fraud Examiners Manual provides examples of data analysis for accounts payable:

“Examples of data analysis queries that can be performed to help detect fraud through examination of accounts payable include:

• Audit paid invoices for manual comparison with actual invoices.

• Summarise large invoices by amount, vendor, etc.

• Identify debits to expense accounts outside of set default accounts.

• Reconcile cheque registers to disbursements by vendor invoice.”

While other functions apply to payroll or asset management, the most effective function for accounts payable fraud detection is to summarize large invoices by amount and vendor.

Explanation with Extracts = The Fraud Examiners Manual explains:

“Forensic analysis should not be performed on suspect devices directly because doing so can alter or damage digital evidence, and imaging the data… allows a fraud examiner to view and analyse the contents of a computer without altering the original data in any way”.

Imaging creates an exact, sector-by-sector copy of the drive.

Thus, imaging protects original data integrity.

Within CFE investigation guidance on data analysis, predictive approaches are framed as using analytical techniques (including statistical processes and machine learning) to identify patterns and outliers that can indicate fraud risk and, importantly, help detect a fraudulent situation earlier than traditional methods. Predictive analytics is not limited to one specific technology such as layered neural networks (A); neural networks can be one possible technique, but predictive analytics is broader than that. Option B describes robotic process automation (RPA)—software that imitates human actions to automate routine tasks—rather than predictive analytics. Option D describes artificial intelligence in a general sense (systems performing “human tasks” like perception and decision-making), which is also broader and not specific to predictive modeling. The most accurate description is therefore C: predictive analytics uses historical data along with statistical analysis and machine learning methods to build models that reflect patterns/trends and generate projections about likely future outcomes (e.g., predicting which transactions, vendors, employees, or accounts are higher-risk).

Before pursuing litigation, fraud examiners are advised to consider whether nonlegal remedies (e.g., restitution agreements, settlements, insurance recovery) are possible, since legal actions can be costly, slow, and uncertain. Statutes of limitation and guilt are legal issues for attorneys and courts, but examiners must weigh recovery options first.

When a suspect becomes withdrawn or emotional after alibis are diffused:

“The interviewer should not remain silent or discourage the accused from showing emotion. Such reactions often indicate consideration of confession. At this point, the interviewer should present an alternative question and continue reinforcing guilt with evidence”.

Thus, Scarlett should continue presenting evidence.

Nonverbal deception cues include feigned unconcern, slouching, chewing objects, and manipulating clothing. Michelle’s conduct (chewing pen cap, picking at fabric, appearing unconcerned) matches these signs of deception.

Best practices emphasize separation:

“Interviews should be conducted one-on-one to reduce the possibility of collusion, contamination, or intimidation of witnesses” (Fraud Examiners Manual – Interviewing Guidelines).This ensures independent and uncontaminated statements.