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Continuing Education
Coming Soon
CE Fraud Examination Forensic Accounting
The course teaches fraud theory, occupational schemes, Benford’s Law, data analytics, and forensic interview techniques, enabling CPAs and forensic accountants to detect, investigate, and prevent financial fraud efficiently.
Who Should Take This
Licensed CPAs, CFEs, and senior forensic accountants who regularly assess internal controls and audit financial statements are ideal candidates. They seek advanced, practical skills to apply statistical analysis, data‑driven detection, and interview protocols for effective fraud investigations and compliance reporting.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
Fraud Theory and Framework
2 topics
Fraud models and typology
- Recognize the elements of the fraud triangle — pressure, opportunity, and rationalization — and their role in understanding why fraud occurs.
- Comprehend the fraud diamond model's addition of capability as a fourth element and how individual traits influence fraud perpetration.
- Analyze a workplace scenario using the fraud triangle and diamond frameworks to assess the likelihood of fraud and identify the dominant risk factors.
- Recognize the ACFE occupational fraud classification system including asset misappropriation, corruption, and financial statement fraud categories.
Fraud risk assessment
- Comprehend the fraud risk assessment process including risk identification, likelihood and impact evaluation, and the design of mitigating controls.
- Analyze an organization's fraud risk profile to identify high-risk areas and recommend targeted fraud detection and prevention measures.
- Synthesize a comprehensive fraud risk assessment for an organization integrating industry-specific risks, control environment evaluation, and management override scenarios.
2
Occupational Fraud Schemes
3 topics
Asset misappropriation schemes
- Recognize common asset misappropriation schemes including skimming, cash larceny, billing schemes, payroll fraud, expense reimbursement fraud, and check tampering.
- Comprehend the red flags and detection methods for asset misappropriation including anomalous transactions, lifestyle indicators, and analytical procedures.
- Analyze financial records to detect indicators of specific asset misappropriation schemes including ghost employees, fictitious vendors, and unauthorized disbursements.
- Synthesize internal control recommendations to prevent and detect asset misappropriation addressing segregation of duties, authorization limits, and reconciliation procedures.
Financial statement fraud
- Recognize common financial statement fraud techniques including revenue overstatement, expense understatement, improper asset valuation, and inadequate disclosures.
- Comprehend the motivations for financial statement fraud including earnings management, covenant compliance, executive compensation targets, and market expectations.
- Analyze financial statements and disclosures to identify indicators of financial statement fraud including unusual trends, ratio anomalies, and aggressive accounting choices.
- Synthesize a financial statement fraud detection program addressing analytical procedures, journal entry testing, management override controls, and whistleblower integration.
Corruption and conflicts of interest
- Recognize corruption schemes including bribery, kickbacks, bid rigging, economic extortion, and conflicts of interest in procurement and vendor relationships.
- Analyze a procurement or vendor relationship to identify corruption indicators and determine appropriate investigation steps.
- Synthesize an anti-corruption compliance program addressing vendor due diligence, conflict of interest disclosures, gift policies, and FCPA compliance.
3
Benford's Law and Statistical Analysis
2 topics
Benford's Law application
- Recognize the mathematical basis of Benford's Law including the expected digit frequency distribution and the conditions under which it applies to financial data.
- Comprehend the application of Benford's Law to fraud detection including first-digit, second-digit, and first-two-digit tests and their statistical significance thresholds.
- Analyze a financial dataset using Benford's Law to identify anomalous digit patterns that may indicate data manipulation or fraudulent transactions.
- Synthesize a Benford's Law testing protocol for fraud examination addressing dataset selection, significance testing, and investigative follow-up procedures.
Statistical sampling and analytical procedures
- Comprehend the statistical sampling methods used in fraud examination including attribute sampling, variable sampling, and stratified sampling techniques.
- Analyze financial data using statistical techniques including trend analysis, ratio analysis, and regression analysis to identify patterns indicative of fraud.
- Synthesize a statistical sampling plan for a fraud examination addressing population definition, sample size calculation, evaluation criteria, and projection methodology.
4
Data Analytics for Fraud Detection
2 topics
Digital forensics and data analytics
- Recognize the types of data analytics techniques used in fraud detection including continuous monitoring, anomaly detection, text mining, and network analysis.
- Comprehend the data acquisition and preservation process for forensic analysis including chain of custody, imaging, hashing, and metadata extraction.
- Analyze a dataset using data analytics techniques to identify duplicate payments, fictitious vendors, unusual transaction patterns, and potential fraud indicators.
- Synthesize a data analytics strategy for continuous fraud monitoring addressing data sources, analytical tests, alert thresholds, and investigation triggers.
AI and machine learning in fraud detection
- Recognize the machine learning techniques applied to fraud detection including supervised classification, unsupervised clustering, and neural network anomaly detection.
- Comprehend the challenges and limitations of AI-based fraud detection including false positive management, model explainability, and data quality requirements.
- Analyze the appropriateness of deploying AI-based fraud detection tools for a specific organizational context considering data availability, risk profile, and resource constraints.
5
Forensic Interview Techniques
1 topic
Interview planning and execution
- Recognize the types of forensic interviews including informational, assessment, and admission-seeking interviews and the appropriate context for each.
- Comprehend the forensic interview planning process including background research, question development, interview sequence strategy, and documentation requirements.
- Analyze verbal and nonverbal cues during a forensic interview to assess credibility and determine appropriate follow-up questioning strategies.
- Synthesize an interview plan for a fraud investigation addressing witness sequencing, question frameworks, admission-seeking techniques, and legal compliance requirements.
6
Expert Witness Testimony
1 topic
Expert testimony standards and preparation
- Recognize the Daubert standard for admissibility of expert testimony including the relevance, reliability, and methodology requirements for forensic accounting experts.
- Comprehend the expert witness engagement process including retention, scope definition, report preparation, deposition, and trial testimony requirements.
- Analyze a forensic accounting report to evaluate whether it meets the Daubert criteria for admissibility and identify potential challenges to the expert's methodology.
- Synthesize an expert report for a forensic accounting engagement addressing methodology documentation, damages calculation, and opinion support for litigation.
- Recognize the differences between consulting expert and testifying expert roles and the implications for work product protection and discovery obligations.
7
Anti-Fraud Controls and Programs
1 topic
Internal controls for fraud prevention
- Recognize the COSO internal control framework components — control environment, risk assessment, control activities, information and communication, and monitoring — as they relate to fraud prevention.
- Comprehend the design of anti-fraud controls including segregation of duties, authorization limits, physical safeguards, and management review procedures.
- Analyze an organization's internal control system to identify fraud control weaknesses and recommend specific control enhancements.
- Synthesize a comprehensive anti-fraud program addressing tone at the top, risk assessment, preventive and detective controls, reporting mechanisms, and response protocols.
8
Whistleblower Programs and Reporting
1 topic
Whistleblower frameworks and protections
- Recognize the whistleblower protection statutes including Sarbanes-Oxley Section 806, Dodd-Frank Section 922, and the False Claims Act qui tam provisions.
- Comprehend the design of effective whistleblower hotline programs including anonymity protections, intake procedures, triage protocols, and investigation triggers.
- Analyze a whistleblower complaint to determine its credibility, assess the reported scheme, and develop an investigation plan.
- Synthesize a whistleblower program policy addressing hotline operation, anti-retaliation protections, investigation procedures, and board reporting requirements.
9
Fraud Investigation Management
1 topic
Investigation planning and execution
- Recognize the phases of a fraud investigation including predication, planning, evidence gathering, analysis, reporting, and resolution.
- Comprehend the legal and ethical considerations in fraud investigations including attorney-client privilege, self-incrimination, evidence admissibility, and cooperation with law enforcement.
- Analyze investigation evidence to build a fraud case including tracing funds, documenting the scheme, quantifying losses, and establishing perpetrator culpability.
- Synthesize a fraud investigation report addressing findings of fact, scheme mechanics, loss quantification, and remediation recommendations for the audit committee.
10
Industry-Specific Fraud Risks
2 topics
Healthcare and insurance fraud
- Recognize common healthcare fraud schemes including upcoding, unbundling, phantom billing, kickbacks, and medically unnecessary services.
- Analyze healthcare billing data to identify patterns indicative of fraud including statistical outliers, duplicate claims, and service-to-diagnosis mismatches.
- Synthesize a healthcare fraud investigation plan addressing data analytics on claims data, provider profiling, and coordination with regulatory authorities.
Cybercrime and emerging fraud schemes
- Recognize emerging fraud schemes including business email compromise, invoice manipulation, synthetic identity fraud, and cryptocurrency-related fraud.
- Comprehend the forensic accounting considerations for investigating cyber-enabled fraud including digital evidence preservation, transaction tracing, and loss quantification.
- Analyze a business email compromise scenario to determine the scope of the fraud, trace the funds, and recommend preventive controls.
- Synthesize a cyber fraud response protocol addressing incident containment, digital evidence preservation, financial loss tracing, and regulatory notification requirements.
Scope
Included Topics
- Fraud theory including the fraud triangle, fraud diamond, and occupational fraud classification.
- Asset misappropriation schemes including skimming, billing fraud, payroll fraud, and expense fraud.
- Financial statement fraud including revenue manipulation, expense understatement, and improper disclosures.
- Benford's Law and statistical analysis for fraud detection.
- Data analytics and digital forensics including continuous monitoring, anomaly detection, and AI-based fraud detection.
- Forensic interview techniques including interview planning, credibility assessment, and admission-seeking interviews.
- Expert witness testimony including Daubert standards, report preparation, and litigation support.
- Anti-fraud controls including COSO framework, segregation of duties, and comprehensive anti-fraud programs.
- Whistleblower programs and protections including SOX, Dodd-Frank, and hotline design.
- Fraud investigation management including predication, evidence gathering, loss quantification, and reporting.
- Industry-specific fraud including healthcare fraud, insurance fraud, and cyber-enabled fraud schemes.
Not Covered
- Detailed criminal law and prosecution procedures beyond their intersection with fraud investigation.
- Computer forensics hardware and software tools at a technical implementation level.
- Regulatory examination procedures for banks and financial institutions.
- Detailed insurance claim adjusting and underwriting beyond fraud detection.
- Academic criminology and sociology of white-collar crime beyond practical fraud examination application.
CE Fraud Examination Forensic Accounting is coming soon
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