Fraud and Factors

Unveiling Socioeconomic Influences on Occupational Fraud

Overview

Starting in 1996, the Association of Certified Fraud Examiners (ACFE) has published the Report to the Nations on Occupational Fraud every two years. The report has expanded globally since 2010 and has been comprehensive data on reported occupational fraud cases over the past two years. The report covers the number of cases reported by country, loss per case, fraud schemes, detection methods, and the perpetrator's profiles.

Higher number of reported fraud cases does not necessarily mean a greater risk of fraud or weaker internal controls. In fact, strong internal controls and heightened awareness often lead to better detection and reporting, leading to higher reported numbers. Therefore, it is important to avoid drawing conclusions on fraud risk based on case counts.

This website explores alternative ways of representing the number of reported fraud cases. By analyzing correlation between the reported fraud cases and indicators such as corruption perception scores, GDP per capita, and average years of education, we aim to offer a more comprehensive view of the factors that influence fraud reporting and detection.

The Role of Governance in Shaping Internal Control

Fraud vs CPI

This section examines the relationship between the Corruption Perceptions Index (CPI) and the number of cases per 100,000 people across different global regions. The data suggests lower corruption levels (higher CPI) correlate to reduced cases, with regional variations providing additional context. This section facilitates the understanding of how quality of governance can positively influence the likelihood of fraud detection and reporting.

Unpacking the Economic Drivers of Integrity

Fraud vs GDP

In this section, we analyze the economic factors that drive occupational fraud occurrence and detection. Higher economic prosperity often leads to better resources for fraud detection and prevention, and fosters more sophisticated fraud schemes. This section explores how disparities in wealth and access to resources can influence the prevalence and detection of fraud.

How Learning Affects Ethical Behavior and Fraud

Fraud vs Years of Schooling

Here, we explore the relationship between educational attainment and reported occupational fraud cases. Increase in years of schooling leads to a rise in fraud cases, suggesting that education may be linked to both higher awareness and reporting of fraud. Beyond a certain threshold, however, the relationship between education and fraud cases begins to plateau or even reverse. This section examines how education shapes ethical behavior and how the findings may vary across regions and time periods.

Understanding the Links Between Socioeconomic Factors and Occupational Fraud

Conclusion

User of the report should always consider the following when reading the number of fraud cases

Higher case numbers may indicate a healthier fraud detection ecosystem rather than higher fraud risks
Always look at fraud cases in context by taking into account the differences in population size, market activity, and workforce knowledge (e.g., cases per 100,000 population, cases per $1 billion GDP, or cases per ACFE member in a given region/country)
Consider contextual indicators such as:
- Corruption Perceptions Index (CPI): Provides insights into perceived corruption levels for each country
- GDP per Capita: Contextualizes economic activity and resources available for fraud detection
- Fraud Detection Methods: Offers insights on awareness and capacity to commit and report fraud

Refrences

Glossary

ACFE: Association of Certified Fraud Examiners - A professional organization that provides training and certification for fraud examiners, and publishes reports on occupational fraud globally.
GDP: Gross Domestic Product - A measure of the total economic output of a country or region, often used to indicate economic health.
CPI: Corruption Perceptions Index - A score published by Transparency International that ranks countries by their perceived levels of corruption in the public sector.
Occupational Fraud: Fraud committed by individuals in their professional capacity, typically within an organization, involving the misuse of their position for personal gain.
Fraud Risk: The likelihood or potential for fraud to occur within a specific region or organization, often influenced by factors such as governance, internal controls, and societal awareness of fraud.
Logged Cases: A mathematical transformation of the number of fraud cases, where data are logarithmically scaled to make patterns easier to analyze, especially when data vary over large ranges.
Fraud Schemes: The various methods or types of fraudulent activity that are observed in occupational fraud, including asset misappropriation, corruption, and financial statement fraud.
Detection Methods: Techniques or practices used to identify fraud, such as audits, whistleblower reports, data analytics, and tip-off systems.
Perpetrator’s Profiles: Characteristics of individuals who commit fraud, such as their demographic information, positions, or behavioral traits.
GDP per Capita: A measure of the average economic output per person in a country or region, often used as an indicator of living standards or economic development.
Corruption Perception Score: The specific numerical value assigned to a country on the Corruption Perceptions Index (CPI), representing the perceived level of corruption in the public sector.
Education Variable (Average School Years): A metric that represents the average number of years a person in a country or region spends in formal education. Used to explore correlations with occupational fraud levels.
Fraud Awareness: The extent to which individuals and organizations are conscious of the possibility of fraud and have the knowledge to detect or prevent it.
Risk Assessment Professionals: Experts who analyze and evaluate risks, including fraud risks, within organizations, typically providing guidance on how to mitigate or manage these risks.
Anti-Fraud Policies: Policies developed by organizations or governments aimed at preventing and detecting fraud, often incorporating internal controls, regulations, and public awareness campaigns.
Scatter Plot: A type of data visualization used to show the relationship between two variables, where each point on the plot represents an individual data point.
Log-Transformation: A mathematical transformation where values are converted using the logarithmic scale, often used to deal with large ranges of data and to make patterns easier to identify.
Regional Variation: Differences in fraud-related data across geographical regions, which may include different trends, causes, or methods of detection due to varying economic, social, and cultural factors.
Internal Controls: The processes and systems put in place within organizations to prevent or detect fraud, including financial oversight, auditing procedures, and ethical guidelines.
Fraud Cases by Country Visualization: A graphical representation that shows the number of reported fraud cases in different countries, often used to analyze trends and patterns in occupational fraud across the globe.
Investors & Regulators: Stakeholders in the fraud risk landscape. Investors seek insights on where to place capital safely, while regulators aim to create policies that mitigate fraud risks and ensure legal compliance.
Percentiles (Fraud by GDP): Statistical measures used to divide data into intervals that represent different ranges of values. In this context, percentiles show the distribution of fraud cases relative to GDP levels.
Mean Occupational Fraud Cases: The average number of reported occupational fraud cases across various regions or countries, often broken down by economic indicators such as GDP.
Data Slider (Year/Region Selection): A user interface tool that allows users to select a specific range or set of data (e.g., year, country, or region) for more targeted analysis within a visualization.
Outliers: Data points that differ significantly from the rest of the dataset, often indicating unusual behavior or errors in data collection. They can significantly affect the interpretation of trends in fraud data.
Fraud Detection and Reporting: The process by which fraud cases are identified and documented, often through audits, whistleblower systems, or data analytics.

Meet Our Team

Zakaria Ali

Zakaria has a passion for uncovering insights from complex datasets. He excels at analyzing data patterns to deliver actionable results that inform decision-making. Zakaria played a key role in analyzing fraud cases, creating the project glossary, and providing valuable support to his colleagues, including assistance with chart development and statistical analysis.

Mohamed Bakr

Mohamed was responsible for creating charts that analyzed the relationship between the number of occupational fraud cases and currption perception index and the worked on the overview dashboard.
He also acted as a subject matter expert as Certified Fraud Examiner (CFE) and a member of the ACFE

Subhasis Das

Subhasis specializes in frontend development and is an experienced backend developer with expertise in Data Engineering.
He worked on consolidating all the data sources into a single csv file by applying various transformations and data filter.

Hyung Jin Kim

Hyung Jin was responsible for creating charts that analyzed the relationship between the number of occupational fraud cases and years of schooling.
During the first half of the project, she also handled administrative tasks such as uploading materials, scheduling meetings, and distributing Zoom links.

Eun-Hae Ko

Eun-Hae specializes in statistical data analysis and modeling.
She was responsible for creating charts that analyzed the relationship between the number of occupational fraud cases and GDP.