1. A computer-implemented method comprising: generating a data structure, the data structure having nodes representing fraud or non-fraud conditions, and having associations between nodes, the data structure being generated from fraud and non-fraud data collected in an adaptive modeling process that determines the fraud and non-fraud conditions from past transactions; determining all unique paths between nodes of the data structure to define a rule for each path, each rule having one or more preconditions; converting each rule to a binary indicator variable to generate a set of binary indicator variables; deriving one or more complex variables from the set of binary indicator variables; determining, by an adaptive weights adaptor, an adaptive weight for each binary indicator variable to obtain weighted values of the set of binary indicator variables and an adaptive weight for each complex variable to obtain weighted values of the one or more complex variables; and providing the weighted values of the set of binary indicator variables and the weighted values of the one or more complex variables to an adaptive scoring engine to generate a score for new transactions, the score representing a likelihood whether each new transaction is fraudulent.