This research highlights the problem of trade based market manipulation in financial markets, where an individual or party aim to distort the pricing mechanism and gain profit at the expense of law abiding investors. This research evaluates data mining approaches applied to financial market surveillance and addresses a current deficit in literature with regards to modelling traders at an entity level. A system is proposed, named the Evolving Bayesian Network (EBN), to model an individual trader's behaviour using transaction order data generated by the participant. The aim of the model is to infer the individual's intentions as order sequences are generated throughout a trading day, to then detect when manipulation attempts are being made for personal gain.