  Financial institutions mostly deal with people. Therefore, characterizing
different kinds of human behavior can greatly help institutions for improving
their relation with customers and with regulatory offices. In many of such
interactions, humans have some internal goals, and execute some actions within
the financial system that lead them to achieve their goals. In this paper, we
tackle these tasks as a behavior-traces classification task. An observer agent
tries to learn characterizing other agents by observing their behavior when
taking actions in a given environment. The other agents can be of several types
and the goal of the observer is to identify the type of the other agent given a
trace of observations. We present CABBOT, a learning technique that allows the
agent to perform on-line classification of the type of planning agent whose
behavior is observing. In this work, the observer agent has partial and noisy
observability of the environment (state and actions of the other agents). In
order to evaluate the performance of the learning technique, we have generated
a domain-independent goal-based simulator of agents. We present experiments in
several (both financial and non-financial) domains with promising results.
