Artificial intelligence modeling to predict electronic account data

Patent metadata

Field	Value
Publication number	US-11798059-B2
Application number	US-202117225503-A
Country	US
Kind code	B2
Filing date	Apr 8, 2021
Priority date	Apr 16, 2020
Publication date	Oct 24, 2023
Grant date	Oct 24, 2023

Abstract

Disclosed methods and system describe a server that uses AI modeling to predict negative cash flow at a user level. The server periodically retrieves data associated with the user, the data comprising monetary attributes associated with one or more accounts of the user; executes a deep neural network model trained based upon historical data associated with at least a subset of the users configured to predict a negative cash flow in one or more accounts of the user, a depth of the negative cash flow, and a duration of the negative cash flow; transmits, to a second server, the predicted values, whereby when the second server determines that a likelihood of account needs satisfies a threshold, the second server establishes an electronic communication session with an electronic device of the user; trains the deep neural network when the second server establishes the electronic communication session.

First claim

What we claim is:

1. A method comprising:
periodically retrieving, by a server, data associated with a user from a set of users, the data comprising monetary attributes associated with one or more accounts of the user;
executing, by the server, an artificial intelligence model to predict a first value indicating a negative cash flow in one or more accounts of the user, a second value indicating a depth of the negative cash flow, and a third value indicating a duration of the negative cash flow, wherein the artificial intelligence model is trained based upon historical data associated with at least a subset of users within the set of users;
transmitting, by the server to a second server, the first value, the second value, and the third value, whereby the second server:
executes an analytical model to determine a likelihood of account needs associated with the user;
determines that the likelihood of account needs satisfies a threshold;
establishes an electronic communication session with an electronic device of the user;

identifying, by the server, the first value, the second value, and the third value as correct predictions; and
training, by the server, the artificial intelligence model based on the correct predictions.

2. The method of claim 1 , further comprising:
outputting, by the server, at least one of the first, the second, or the third value onto a computing device of an administrator during the electronic communication session.

3. The method of claim 1 , wherein the server further retrieves user attributes associated with the user other than the monetary attributes associated with one or more accounts of the user, wherein the server applies the retrieved user attributes to the artificial intelligence model.

4. The method of claim 3 , wherein the user attributes comprise at least one of user's demographic data, user's income data, or the user's account type.

5. The method of claim 1 , wherein the artificial intelligence model is a deep neural network.

6. The method of claim 1 , wherein the second server identifies a product to be offered to the user based on the likelihood of account needs.

7. The method of claim 1 , wherein the second server is associated with a call center, and wherein the second server establishes the electronic communication session with the electronic device of the user by routing a call received from the electronic device of the user based on at least one of the first value, the second value, or the third value.

8. The method of claim 1 , wherein the server or the second server denies at least one transaction associated with the user.

9. A system comprising:
a server in communication with a second server in communication with an electronic device of a user, the server configured to:
periodically retrieve data associated with the user from a set of users, the data comprising monetary attributes associated with one or more accounts of the user;
execute an artificial intelligence model to predict a first value indicating a negative cash flow in one or more accounts of the user, a second value indicating a depth of the negative cash flow, and a third value indicating a duration of the negative cash flow, wherein the artificial intelligence model is trained based upon historical data associated with at least a subset of users within the set of users;
transmit, to the second server, the first value, the second value, and the third value, whereby the second server is configured to:
execute an analytical model to determine a likelihood of account needs associated with the user;
determine that the likelihood of account needs satisfies a threshold;
establish an electronic communication session with the electronic device of the user;

identify the first value, the second value, and the third value as correct predictions; and
train the artificial intelligence model based on the correct predictions.

10. The system of claim 9 , wherein the server is further configured to:
output at least one of the first, the second, or the third value onto a computing device of an administrator during the electronic communication session.

11. The system of claim 9 , wherein the server further retrieves user attributes associated with the user other than the monetary attributes associated with one or more accounts of the user, wherein the server applies the retrieved user attributes to the artificial intelligence model.

12. The system of claim 11 , wherein the user attributes comprise at least one of user's demographic data, user's income data, or the user's account type.

13. The system of claim 9 , wherein the artificial intelligence model is a deep neural network.

14. The system of claim 9 , wherein the second server is further configured to identify a product to be offered to the user based on the likelihood of account needs.

15. The system of claim 9 , wherein the second server is associated with a call center, and wherein the second server establishes the electronic communication session with the electronic device of the user by routing a call received from the electronic device of the user based on at least one of the first value, the second value, or the third value.

16. The system of claim 9 , wherein the server or the second server denies at least one transaction associated with the user.

Assignees

• Bank Of Montreal

Inventors

• Nosrati Seyed Masoud
• Vahlis Evgene
• Shahir Seyed Hamed Yaghoubi
• Zhao Bo
• Langballe Nicole
• Poon Peter

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/098

  Distributed learning, e.g. federated learning · CPC title

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title