公开(公告)号：US20240211732A1

公开(公告)日：2024-06-27

申请号：US18213624

申请日：2023-06-23

Applicant: THE TORONTO-DOMINION BANK

Inventor： MATTHEW CARLTON FREDERICK WANDER ,  HARSHUL VARMA ,  HOLLY HEGLIN ,  MING JIAN PAN ,  ABINAV RAMESH SUNDARARAMAN

IPC: G06N3/0455 ,  G06N3/0442 ,  G06N3/0464

CPC classification number: G06N3/0455 ,  G06N3/0442 ,  G06N3/0464

Abstract: Methods, systems and techniques for multivariate time series forecasting are provided. A dataset is obtained that corresponds to a multivariate time series data for a multivariate time series forecasting task. A particular machine learning architecture is used for the forecasting using an artificial neural network and deep learning. The machine learning architecture includes an autoencoder configured and trained on itself moved forward in time to generate autoencoder layers to analyse seasonality and co-variance information of the multivariate input dataset in a future time frame and an autoregressor to generate autoregressor layers to analyse trend information of the multivariate input dataset in a future time frame; and a layer merger for merging the one or more autoregressor layers and one or more autoencoder layers to form a set of merged layers representative of a multivariate time series forecast using the machine learning model in the future time frame.

公开(公告)号：US20250014052A1

公开(公告)日：2025-01-09

申请号：US18764805

申请日：2024-07-05

Applicant: THE TORONTO-DOMINION BANK

Inventor： BARNALI BHATTACHARJEE ,  DAERIAN ASHAN DILKUMAR ,  HARSHUL VARMA ,  KEVIN JOE AKAOKA ,  MICHELLE ERICA LEVINE ,  YUQIN SABRINA SUN ,  CHAD A. KOZIEL

IPC: G06Q30/018 ,  G06N5/025

Abstract: A model engine is provided for managing transactions for one or more computing devices over a network. There is provided a machine learning model trained and tested on historical transaction data including labelled fraud data, to provide a target signal indicative of a likelihood of fraud within a given transaction, generating an ensemble of decision trees. The engine further includes extracting a set of rules by traversing each tree in the ensemble of decision trees from a root node to each leaf node of the tree, each path from the root node to a particular leaf node including splitting criterion providing a rule to form a set of rules. A proactive risk management system is also provided for applying the set of rules to a new transaction and when at least one rule is met, triggering predetermined actions on at least one computing device on the network.