Identity Theft: Making the Known Unknowns Known

Abstract:     
There is widespread agreement that identity theft causes financial damage to consumers, lending institutions, retail establishments, and the economy as a whole. Surprisingly, there is little good public information available about the scope of the crime and the actual damages it inflicts. The publicly available data on identity theft come mainly from survey research. Methodologically, these survey polls of the public suffer from being both under and over-inclusive in measuring the problem. As a result, low estimates attribute tens of billions of dollars in costs to the economy and consumers, the highest estimates place losses in the hundreds of billions.

To identify proper interventions and appropriately allocate resources we need comprehensive, hard data on the scope and effect of identity theft. One way to provide concrete data is to require lending institutions to publicly report figures on identity theft. Such public reporting will help identify the relative need for intervention and the likely efficacy of interventions. These disclosures are necessary to provide a sound baseline for investment by businesses and action by regulators. They are also warranted because the public pays the price of identity theft directly when they are the victim, and indirectly through higher fees, interest rates, and because the losses are tax subsidized.

The author hypothesizes that if lending institutions reported limited information about identity theft, it would reveal that identity theft is both more prevalent and economically damaging than currently acknowledged, in part because of the rise of synthetic identity theft, a form that cannot be measured by victim surveys because they are unaware of the crime. Furthermore, the disclosure requirement would birth an anti-identity theft market, and the prevalence and severity of the crime would decrease dramatically as institutions compete to offer the safest financial products to consumers.

Keywords: identity theft, fraud, reporting, privacy, synthetic, fictitious, information security, social security numbers

JEL Classifications: K20, K42

Accepted Paper Series