In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...

Financial exploitation, and particularly thefts and scams, are increasing at an alarming rate. In this study we (a) determined the national prevalence of older adults who report having been a victim of fraud, (b) created a population-based model for the prediction of fraud, and (c) examined how fraud is experienced by the most psychologically vulnerable older adults. The older adults studied we...

Using cross sectional data Psychological vulnerability was identified as a correlate of older adult's being defrauded. We extend that research by examining fraud prevalence using longitudinal data from the Health and Retirement Study, and to identify the best predictors of fraud longitudinally across a 4-year time frame. Whereas reported fraud prevalence was 5.0% in a 5-year look-back period in...

Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier...

a r t i c l e i n f o Occupational fraud is a $652 billion problem to which disgruntled employees are a major contributor. Much security research addresses reducing fraud opportunity and increasing fraud detection, but detecting motivational factors like employee disgruntlement is less studied. The Sarbanes–Oxley Act requires that companies archive email, creating an untapped resource for deter...

Abstract This study investigates the predictive power of CEO characteristics on accounting fraud utilizing a machine learning approach. Grounded in upper echelons theory, we show value widely neglected for learning-based detection isolation and as part novel combination with raw financial data items. We employ five models well-established literature. Diverging from prior studies, introduce mode...