Chicago Mercantile Exchange Inc.

David John Geddes

Ari L Studnitzer

Inderdeep Singh

Bernard Pieter Hosman

Arjun Parmar

Steven Hutt

Frederic Malabre

Systems and methods for pre-processing data to facilitate efficient and accurate machine learning are provided. The data may include market data. The pre-processing may include partitioning the data into windows assigning categories to windows generate a series of vectors. The series of vectors then being input into a computer system that executes a machine learning algorithm to efficiently train a neural network used to identify structure or patterns therein.

Pre-processing financial market data prior to machine learning training

Systems and methods are provided to identify abnormal transaction activity by a participant that is inconsistent with current conditions. Historical participant and external data are identified. A recurrent neural network identifies patterns in the historical participant and external data. A new transaction by the participant is received. The new transaction is compared using the patterns to the historical participant and external data. An abnormality score is generated. An alert is generated if the abnormality score exceeds a threshold.

Deep learning for credit controls

A decision tree based processing system implements a decision tree data structure to process publicly available files, such as websites suspected to contain data generated by a data transaction processing system which may be constantly fluctuating and varying. The data transaction processing system generates the data based on processing electronic data transaction request messages received over a network.

Decision tree data structure based processing system

Systems and methods are provided to identify abnormal transaction activity by a participant that is inconsistent with current conditions. Historical participant and external data is identified. A recurrent neural network identifies patterns in the historical participant and external data. A new transaction by the participant is received. The new transaction is compared using the patterns to the historical participant and external data. An abnormality score is generated. An alert is generated if the abnormality score exceeds a threshold.

Systems and methods are provided for message transmission timing optimization. The system receives a large market disrupting transaction and provides a transaction placement strategy that splits up the transaction to maximizes favorable execution value and minimizes time required to execute the entire transaction.

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Belfast, United Kingdom

David John Geddes