Would you run a stock-picker algorithm that never did backtesting? Real data-science is much more complex than just applying "this should work" reasoning and jumping to a really controversial conclusion with zero data to back it up.

This is a good hypothesis as it stands, and may be valuable as a predictor of fraud. However the conclusion is meaningless without sufficient evidence of predictivity to back it up.

Take historical examples of elections with known fraud and run this analysis to see if it can correctly label the fraud/no-fraud categories. _Then_ after we can agree that it works well, only then will I care about the results of any specific analysis.