1. A system for detecting fraud, the system comprising: a self-calibrating outlier model hosted by a computing system, the self-calibrating outlier model receiving transaction data representing transactions, and being configured to calculate transaction-based variables, profiles and calibration parameters, and to produce a baseline score based on the transaction data according to the transaction-based variables, profiles and calibration parameters; and an adaptive cascade model hosted by the computing system, the adaptive cascade model being configured to generate a secondary score for the transaction data based on profile information from the variables and/or profiles calculated by the self-calibrating outlier model, and based on a comparison with labeled transactions from a human analyst of historical transaction data; wherein the self-calibrating outlier model takes a form of: S b = ∑ i = 1 p ⁢ w i ⁢ q ⁡ ( x i ❘ θ ) given its associated parameter (θ 1 ,. .. , θ l )εθ, where l is total number of parameters, S b is a transaction score based on a linearly weighted sum, with the weights w i , of p transformed predictive profile variables (x 1 ,. .. , x p )εX, wherein q(x i |⊖) is a scaled value of corresponding profile variable x i .