Royal Bank of Canada

Yuanqiao Wu

Yik Chau Lui

Janahan Mathuran Ramanan

Jaspreet Sahota

Cathal Smyth

A system receives transaction data over time, and creates structured data based on the received transaction data. Purchase transactions that are associated with a purchase category are identified in the structured data and labeled. A recurrent neural network such as a long short-term memory (LSTM) network, in particular, a k-LSTM architecture using weighted averages to update hidden states and cell states, is trained to build a model. The model is used to predict the likelihood of a purchase transaction.

K-LSTM architecture for purchase prediction

An electronic device and method of correcting bias for supervised machine learning data is provided. The electronic device comprises a processor and memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises training an auto-encoder with an unbiased subset of historical data, and applying the auto-encoder to correct historical data.

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Toronto, Canada

Yuanqiao Wu