Auditors assure the fairness of financial statements for company stakeholders. Audit effort to minimise errors leads to higher audit fees. The reduced audit fees in the United States cause company stakeholders’ concern on audit quality. A reliable method used in other fields, Benford’s Law, may assist auditors to identify errors from data manipulation and accounting irregularities. This research investigates whether using Benford’s Law can reduce audit risk and improve audit outcomes, using a sample of U.S. companies between 2000 and 2014. I empirically examine whether the FSD_Score based on the Kolmogorov-Smirnoff statistic (maximum deviation between empirical digit distribution and Benford’s Law distribution) and the mean absolute deviation is an important determinant of audit fees. Validation tests are conducted to test the conformity with Benford’s Law in my samples. Audit fee models are used to investigate the association between FSD_Score and audit fees. Evidence from validation tests indicates that numbers in financial statements closely conform to Benford’s Law. I find a negative and significant association between FSD_Score and audit fees, inconsistent with the hypothesis that audit fees increase with FSD_Score because high FSD_Scores reflect high litigation risks. I also find that the association between FSD_Score is stronger in smaller firms than in larger firms. Results from audit fee models and additional tests suggest that FSD_Score can be used as a measure of audit quality instead of litigation risk.