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Two
self-learning methods for
automatic fraud detection are
presented. In both cases a
classification into two
categories (correct YES/NO) is
carried out. Both methods show
good forecast results. In a test
data set hit rates are above 80%
with false alarm rates ranging
from 15% to 23%.
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