  Many business applications involve adversarial relationships in which both
sides adapt their strategies to optimize their opposing benefits. One of the
key characteristics of these applications is the wide range of strategies that
an adversary may choose as they adapt their strategy dynamically to sustain
benefits and evade authorities. In this paper, we present a novel way of
approaching these types of applications, in particular in the context of
Anti-Money Laundering. We provide a mechanism through which diverse, realistic
and new unobserved behavior may be generated to discover potential unobserved
adversarial actions to enable organizations to preemptively mitigate these
risks. In this regard, we make three main contributions. (a) Propose a novel
behavior-based model as opposed to individual transactions-based models
currently used by financial institutions. We introduce behavior traces as
enriched relational representation to represent observed human behavior. (b) A
modelling approach that observes these traces and is able to accurately infer
the goals of actors by classifying the behavior into money laundering or
standard behavior despite significant unobserved activity. And (c) a synthetic
behavior simulator that can generate new previously unseen traces. The
simulator incorporates a high level of flexibility in the behavioral parameters
so that we can challenge the detection algorithm. Finally, we provide
experimental results that show that the learning module (automated
investigator) that has only partial observability can still successfully infer
the type of behavior, and thus the simulated goals, followed by customers based
on traces - a key aspiration for many applications today.
