  In modern litigation, fraud investigators often face an overwhelming number
of documents that must be reviewed throughout a matter. In the majority of
legal cases, fraud investigators do not know beforehand, exactly what they are
looking for, nor where to find it. In addition, fraudsters may use deception to
hide their behaviour and intentions by using code words. Effectively, this
means fraud investigators are looking for a needle in the haystack without
knowing what the needle looks like.
  As part of a larger research program, we use a framework to expedite the
investigation process applying text-mining and machine learning techniques. We
structure this framework using three well-known methods in fraud
investigations: (i) the fraud triangle (ii) the golden ("W") investigation
questions, and (iii) the analysis of competing hypotheses. With this framework,
it is possible to automatically organize investigative data, so it is easier
for investigators to find answers to typical investigative questions.
  In this research, we focus on one of the components of this framework: the
identification of the usage of code words by fraudsters. Here for, a novel
(annotated) synthetic data set is created containing such code words, hidden in
normal email communication. Subsequently, a range of machine learning
techniques are employed to detect such code words. We show that the
state-of-the-art BERT model significantly outperforms other methods on this
task. With this result, we demonstrate that deep neural language models can
reliably (F1 score of 0.9) be applied in fraud investigations for the detection
of code words.
