System and Method for Fraud Detection in a Mobile Device

Patent metadata

Field	Value
Publication number	US-2016307199-A1
Application number	US-201514962365-A
Country	US
Kind code	A1
Filing date	Dec 8, 2015
Priority date	Apr 14, 2015
Publication date	Oct 20, 2016
Grant date	—

Abstract

A user device comprising: i) transmit path circuitry and receive path circuitry configured to communicate with a payment server; and ii) processing circuitry configured to control the transmit path circuitry and receive path circuitry. The processing circuitry is further configured to: a) receive a user input related to a payment process; b) calculate a risk score indicative of a likelihood of fraudulent activity associated with the payment process, wherein the risk score calculation is based on confidential information associated with the user that is stored on the user device; and c) transmit to the payment server a payment action and the risk score associated with the payment action without disclosing the confidential information. The confidential information comprises personally identifiable information and/or private information of the user. The processing circuitry calculates the risk score using a risk base model received from a model server.

First claim

What is claimed is:

1 . A user device comprising:
transmit path circuitry and receive path circuitry configured to communicate with a payment server; and processing circuitry configured to control the transmit path circuitry and receive path circuitry and further configured to:
receive a user input related to a payment process;
calculate a risk score indicative of a likelihood of fraudulent activity associated with the payment process, wherein the risk score calculation is based on confidential information associated with the user that is stored on the user device; and
transmit to the payment server a payment action and the risk score associated with the payment action without disclosing the confidential information.

2 . The user device as set forth in claim 1 , wherein the confidential information comprises personally identifiable information of the user.

3 . The user device as set forth in claim 1 , wherein the confidential information comprises private information of the user.

4 . The user device as set forth in claim 1 , wherein the processing circuitry calculates the risk score using a risk base model received from a model server.

5 . The user device as set forth in claim 4 , wherein the processing circuitry is further configured to transmit to the model server a suggested parameter update usable by the model server to improve the accuracy of the risk base model.

6 . The user device as set forth in claim 4 , wherein the risk base model is based on a neural network.

7 . The user device as set forth in claim 4 , wherein the risk base model is based on a decision tree.

8 . The user device as set forth in claim 1 , wherein the processing circuitry is further configured to transmit to the payment server a justification corresponding to each risk score.

9 . For use in a user device, a method comprising:
communicating with a payment server; receiving a user input related to a payment process; calculating a risk score indicative of a likelihood of fraudulent activity associated with the payment process, wherein the risk score calculation is based on confidential information associated with the user that is stored on the user device; and transmitting to the payment server a payment action and the risk score associated with the payment action without disclosing the confidential information.

10 . The method as set forth in claim 9 , wherein the confidential information comprises personally identifiable information of the user.

11 . The method as set forth in claim 9 , wherein the confidential information comprises private information of the user.

12 . The method as set forth in claim 9 , wherein calculating the risk score comprises calculating the risk score using a risk base model received from a model server.

13 . The method as set forth in claim 12 , further comprising:
transmitting to the model server a suggested parameter update usable by the model server to improve the accuracy of the risk base model.

14 . The method as set forth in claim 12 , wherein the risk base model is based on a neural network.

15 . The method as set forth in claim 12 , wherein the risk base model is based on a decision tree.

16 . The method as set forth in claim 9 , further comprising:
transmitting to the payment server a justification corresponding to each risk score.

17 . A non-transitory computer readable medium configured to control a processor to perform a method of processing payments, the method comprising:
communicating with a payment server; receiving a user input related to a payment process; calculating a risk score indicative of a likelihood of fraudulent activity associated with the payment process, wherein the risk score calculation is based on confidential information associated with the user that is stored on the user device; and transmitting to the payment server a payment action and the risk score associated with the payment action without disclosing the confidential information.

18 . The non-transitory computer readable medium as set forth in claim 17 , wherein the confidential information comprises personally identifiable information of the user.

19 . The non-transitory computer readable medium as set forth in claim 17 , wherein the confidential information comprises private information of the user.

20 . The non-transitory computer readable medium as set forth in claim 17 , wherein calculating the risk score comprises calculating the risk score using a risk base model received from a model server and further comprising.
transmitting to the model server a suggested parameter update usable by the model server to improve the accuracy of the risk base model.

Assignees

• Samsung Electronics Co Ltd

Inventors

• Patel Kunal M
• Kang Abraham J
• Kasman Bulent
• Ning Peng
• Grace Michael C

Classifications

• G06Q20/322

  Aspects of commerce using mobile devices [M-devices] · CPC title

• G06F21/6245 Primary

  Protecting personal data, e.g. for financial or medical purposes · CPC title

• G06Q20/4016 Primary

  involving fraud or risk level assessment in transaction processing · CPC title

• G06N3/02

  Neural networks · CPC title

• G06Q40/00

  Finance; Insurance; Tax strategies; Processing of corporate or income taxes · CPC title