Who falls for what?
Many of the early fraud studies attempted to profile fraud victims as a whole, without regard to which type of fraud was being committed. The results of these approaches were decidedly mixed (Pak & Shadel, 2007). More recent efforts to determine who is falling for fraud have focused on profiling victims by scam type. The most profiling work of this kind has been done on lottery and investment fraud victims.
The primary method for profiling these victims was as follows: law enforcement agencies provided researchers with lists of individuals whose status as a lottery or investment fraud victim had been verified. They were then surveyed along with a control group of randomly-selected members of the general public in order to see how responses differed between groups.
Studies employing this methodology found some key differences between lottery victims and the general population. Lottery victims were more likely to be over 70, female, lower education, lower income, living alone, less financially literate, had an external locus of control and had experienced more negative life events (AARP, 2003; FINRA/WISE, 2006). There is also some preliminary evidence from pilot studies that lottery victims might have a higher incidence of cognitive impairment.
Studies of investment fraud victims found they too had characteristics that differed from the general population. Investment victims were more likely to be between 55-62 years old, male, married, wealthier, more financially literate, more open to sales pitches used by con men, more likely to have invested in high-risk investments and not likely to have checked the background of a broker before investing (FINRA/WISE, 2006; AARP Washington, 2007; FINRA Risk Behavior Study, 2007 and AARP Foundation, 2008).
This profiling of lottery and investment fraud victims has revealed discrete demographic, psychological and situational differences that further research may wish to explore. Little work has been done to profile victims of scams other than lottery and investment fraud. A clear understanding of what type of people fall for which scams would help policy makers decide where to invest limited resources.
While several national surveys have profiled those who self-reported having been taken by various scams, such data clearly doesn’t represent all victims and may be skewed by demographic differences between those who report and those who don’t (FTC, 2007; Office of Fair Trade – UK, 2006; and Consumer Authority – Netherlands, 2009). The method of using lists of known victims has its limitations since they are not random samples. But we believe it is a better strategy than relying on individual self-reports.