US 12,079,705 B2
Deep learning for credit controls
Ari Studnitzer, Northbrook, IL (US); David Geddes, Belfast (GB); and Inderdeep Singh, Palatine, IL (US)
Assigned to Chicago Mercantile Exchange Inc., Chicago, IL (US)
Filed by Chicago Mercantile Exchange Inc., Chicago, IL (US)
Filed on Feb. 22, 2023, as Appl. No. 18/112,582.
Application 18/112,582 is a continuation of application No. 15/467,632, filed on Mar. 23, 2017, granted, now 11,625,569.
Prior Publication US 2023/0196066 A1, Jun. 22, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/04 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/084 (2023.01); G06Q 40/04 (2012.01)
CPC G06N 3/04 (2013.01) [G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/084 (2013.01); G06Q 40/04 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A computer implemented method comprising:
identifying, by a processor coupled with a data transaction processing system, using a structured neural network comprising a layered plurality of interconnected processing nodes, one or more patterns in historic participant transaction data for a participant in the data transaction processing system and historic external market factor data including data indicative of characteristics of a financial derivative product traded on an exchange for a time period that corresponds to the historic participant transaction data that occurs during the time period, the one or more patterns indicative of a historical normal activity by the participant in relation to the historic external market factor data, wherein at least a subset of the interconnections of the layered plurality of interconnected processing nodes are dynamically weighted;
receiving, by the processor, from the participant, data indicative of a new transaction;
calculating, by the processor, current external market factor data;
comparing, by the processor, the data indicative of the new transaction and the current external market factor data with the one or more patterns;
generating, by the processor, an abnormality score for the new transaction based on the comparison; and
generating, by the processor, an alert when the abnormality score exceeds a first threshold.