Introduces the use of account activity relative to client peer groups as a means of identifying unusual behaviour, as part of money laundering detection; assumptions can be made about what constitutes normal transactional behaviour for an individual, so that deviations from this can generate risk factors. Analyses an account history as a time series of asset movements that can be characterised by a function whose Fourier transform can be computed, yielding a function of a frequency; the so‐called Power Spectrum can be derived, and this specifies the “power” or magnitude of asset movement in the account as a function of frequency. Points out that the method also allows detailed inter‐institutional comparisons of account activity.
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