Systems and methods for dynamically updating models using machine learning

A computing system for detecting and preventing fraudulent network events in a payment card network, said computing system comprising:

• a fraud model engine configured to:
• receive, from a database, an initial dataset including historical transaction data for a first time period;
• segment the initial dataset into a plurality of subsets, each subset associated with a second time period that is smaller than the first time period;
• assign a weight to each of the plurality of subsets, each weight assigned based at least in part on an age of the associated subset;
• train a fraud scoring model on each subset of the plurality of subsets separately, wherein for each subset, the fraud scoring model is trained on that subset in accordance with the assigned weighting for that subset;
• receive, from a merchant computing device, a payment card transaction request;
• analyze the payment card transaction request using the trained fraud scoring model;
• and
• assign a score to the payment card transaction request based on the analysis;
• and
• a fraud rules engine communicatively coupled to the fraud model engine and configured to:
• receive the payment card transaction request and the corresponding score from the fraud model engine;
• and
• generate and output, based at least in part on the score, a decision whether to approve or decline a transaction associated with the payment card transaction request.

A computer-implemented method for detecting and preventing fraudulent network events in a payment card network, said method comprising:

• receiving, at a fraud model engine, from a database, an initial dataset including historical transaction data for a first time period;
• segmenting the initial dataset into a plurality of subsets, each subset associated with a second time period that is smaller than the first time period;
• assigning a weight to each of the plurality of subsets, each weight assigned based at least in part on an age of the associated subset;
• training a fraud scoring model on each subset of the plurality of subsets separately, wherein for each subset, the fraud scoring model is trained on that subset in accordance with the assigned weighting for that subset;
• receiving, from a merchant computing device, a payment card transaction request;
• analyzing the payment card transaction request using the trained fraud scoring model;
• assigning a score to the payment card transaction request based on the analysis;
• receiving, at a fraud rules engine communicatively coupled to the fraud model engine, the payment card transaction request and the corresponding score from the fraud model engine;
• and
• generating and outputting, based at least in part on the score, a decision whether to approve or decline a transaction associated with the payment card transaction request.