  Goal recognition aims at predicting human intentions from a trace of
observations. This ability allows people or organizations to anticipate future
actions and intervene in a positive (collaborative) or negative (adversarial)
way. Goal recognition has been successfully used in many domains, but it has
been seldom been used by financial institutions. We claim the techniques are
ripe for its wide use in finance-related tasks. The main two approaches to
perform goal recognition are model-based (planning-based) and model-free
(learning-based). In this paper, we adapt state-of-the-art learning techniques
to goal recognition, and compare model-based and model-free approaches in
different domains. We analyze the experimental data to understand the
trade-offs of using both types of methods. The experiments show that
planning-based approaches are ready for some goal-recognition finance tasks.
