Date

8-2019

Department

Graduate School of Business

Degree

Doctor of Business Administration (DBA)

Chair

Scott Stultz

Keywords

Financial Fraud Predictors, Fraud Risk, Nonprofit Organizations, Nonprofit Fraud

Disciplines

Accounting | Business

Abstract

Nonprofit organizations are especially vulnerable to fraud. Incidents of fraud can have devastating consequences on these organizations and the nonprofit sector overall. This applied doctoral research project examined the use of financial predictors for reported fraud in U.S. nonprofit organizations. The study utilized financial data from 2017 IRS Form 990 filings of 644 U.S. nonprofit organizations with a 501(c)(3) tax exempt status. The researcher performed logistic regression analysis to determine and evaluate any associations between the financial variables and the existence of reported fraud. Three of the financial variables, cash growth rate (p=.001), asset growth rate (p=.046), and the ratio of disqualified compensation to total compensation (p=.033), were found to be statistically significant as individual predictors for reported fraud in the sample analyzed. The prediction model using seven financial variables (revenue growth rate, program expense ratio, cash growth rate, the ratio of cash to total assets, asset growth rate, the ratio of top compensation to total expenses, and the ratio of disqualified compensation to total compensation) was found to be a significant prediction model (p=.001) for reported fraud in the sample analyzed. The model explained five percent (5%) of the variance in the likelihood of fraud and correctly classified 66.7% of the cases analyzed. The findings of this research are useful to auditors, policymakers, management, board members, donors, creditors, and other stakeholders of nonprofit organizations for evaluation of fraud risk, analysis, and development of effective internal controls to protect against fraud.

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