Transaction Risk Detection

A method comprising:

• receiving historical data comprising data associated with transactions between a first set of one or more transacting partners and a first set of one or more transacting entities;
• generating, from the historical data, characteristics characterizing words;
• obtaining a numerical value of a number of topics desired to be determined;
• determining the numerical value number of topics that are associated with the one or more transacting entities;
• associating the topics with the words in a topic model;
• and
• generating a topic probability mixture vector by using the topic model, the topic vector being updated in run-time to characterize risk associated with subsequent transactions in the run-time.

A method comprising:

• receiving data characterizing at least one transaction;
• calculating, using a topic probability mixture vector that is updated when the data is received and that is generated by a latent Dirichlet allocation (LDA) model, values of one or more predictive features;
• and
• scoring, based on the values of the one or more predictive features, the at least one transaction.