Algorithm for detection of illegal discounting in North Carolina Education Lottery

TL;DR

This paper presents a new statistical and clustering-based model to detect illegal discounting behaviors in the North Carolina Education Lottery, focusing on unusual buying patterns and net gains.

Contribution

It introduces a novel combined statistical and clustering approach for identifying illegal discounting in lottery data, specifically applied to North Carolina's lottery system.

Findings

Nine outlying players identified and analyzed

Unusual buying patterns confirmed through clustering analysis

Proposed stochastic model estimates players' potential losses

Abstract

The lottery is a very lucrative industry. Popular fascination often focuses on the largest prizes. However, less attention has been paid to detecting unusual lottery buying behaviors at lower stakes. Our paper introduces a new model to detect illegal discounting in the North Carolina Education Lottery using statistical analysis of net gains and ticket buying habits. Nine outlying players are flagged and are further examined using a proposed stochastic model to calculate the range of their possible losses in the lottery. The unusual buying patterns of the players flagged as outliers are further confirmed using a K-means clustering analysis of lottery store visiting behaviors.