Learning the normal variance distribution per account, vendor or transaction type from historically confirmed matches, and setting tolerance from it rather than using a fixed threshold.
Mean and standard deviation per group from confirmed-correct matches, with outliers excluded; tolerance set at mean plus two standard deviations, subject to a hard ceiling
pandas groupby with mean/std; Postgres AVG/STDDEV; scikit-learn or statsmodels regression for the sparse-bucket case