Benford’s Law assumes that in many naturally occurring datasets, the distribution of first digits follows a predictable logarithmic pattern. Deviations from this pattern may suggest anomalies. However, the challenge is to determine whether these deviations actually indicate fraud, or if they simply reflect natural variations in the data.
To evaluate this properly, I am using a multi-layered approach:
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Quantitative tests (χ², MAD, K-S) to measure deviations.
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Benchmarking against labelled or synthetic fraud datasets.
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Cross-validation across multiple datasets to test reproducibility.
This critical stance recognises a limitation: Benford’s Law is a useful red flag, but it cannot prove fraud on its own.
