Abstract

Sam Han*

In 2016, Emmy Award winning host John Oliver reported on the debt-collection industry, in which he exposed fraud being committed on a massive scale. That same fraud-prevalent industry has been the subject of numerous investigations and penalties imposed by the Consumer Financial Protection Bureau (CFPB). Because the fraudulent behaviors of debt collectors have been exposed through lawsuits and reports from various media outlets, debt collectors now employ more sophisticated evidence manufacturing techniques in pursuit of their collection efforts. Those techniques are so convincing that alleged debtors face resistance from courts that routinely enter adverse judgments based on the manufactured evidence. Insofar as judges are less familiar with metadata in electronic documents but more familiar with traditional mathematical concepts, this paper introduces procedures that use traditional (and relatively simple) mathematics to reliably detect anomalies in manufactured electronic evidence. Specifically, this paper introduces how mathematical positional notation can be exploited in consumer debt-collection cases to uncover fraud in electronic banking documents without relying on metadata.