IP Library Granted Patent US 11,704,392
Granted Patent B2
US 11,704,392 · App. 17/056,755 · Granted Jul 18, 2023

Fraud estimation system, fraud estimation method and program

Inventor: Kyosuke Tomoda (Tokyo, JP)
Assignee: RAKUTEN GROUP, INC.
G06F21/31
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Quick Facts
Patent No.
US 11,704,392
App. No.
17/056,755
Granted
Jul 18, 2023
Kind
B2
Abstract

Relevance information obtaining means of a fraud estimation system is configured to obtain relevance information about relevance between one service and another service. Comparison result obtaining means is configured to obtain a comparison result of a comparison between user information of a target user in the one service and user information of a fraudulent user or an authentic user in the another service. Estimation means is configured to estimate fraudulence of the target user based on the relevance information and the comparison result.

Claims (49)

1. A fraud estimation system, comprising at least one processor configured to:

set relevance information based on an aggregated count of the number of matches between user information of fraudulent users of a first service and user information of fraudulent users of a second service, such that a numerical value indicated by the relevance information is larger when the aggregated count is higher;

obtain the relevance information about a relevance between the first service and the second service;

obtain a comparison result of a comparison between user information of a target user in the first service and user information of a fraudulent user or an authentic user in the second service; and

estimate fraudulence of the target user based on the relevance information and the comparison result.

2. The fraud estimation system according to claim 1 ,

wherein the at least one processor is configured to obtain a plurality of pieces of relevance information each corresponding to a plurality of other services,

wherein the at least one processor is configured to obtain a plurality of comparison results each corresponding to the plurality of other services, and

wherein the at least one processor is configured to estimate fraudulence of the target user based on the plurality of pieces of relevance information and the plurality of comparison results.

3. The fraud estimation system according to claim 1 ,

wherein, in the first service, fraudulence is estimated based on user information of a predetermined item, and

wherein the at least one processor is configured to obtain a comparison result of a comparison between the target user's user information of the predetermined item in the first service and fraudulent user's or authentic user's user information of the predetermined item in the second service.

4. The fraud estimation system according to claim 1 ,

wherein, in the second service, fraudulence is estimated based on user information of a first item, and

wherein the at least one processor is configured to obtain a comparison result of a comparison between the target user's user information of a second item in the first service and fraudulent user's or authentic user's user information of the second item in the second service.

5. The fraud estimation system according to claim 4 ,

wherein the at least one processor is configured to obtain relevance information about relevance between the first item and the second item in the second service, and

wherein the at least one processor is configured to estimate fraudulence of the target user based on the relevance information about the relevance between the first item and the second item in the second service.

6. The fraud estimation system according to claim 4 ,

wherein, in the second service, fraudulence is estimated based on user information of each of a plurality of first items,

wherein the at least one processor is configured to obtain relevance information about relevance of each of the plurality of first items in the second service, and

wherein the at least one processor is configured to estimate fraudulence of the target user based on the relevance information about the relevance of each of the plurality of first items in the second service.

7. The fraud estimation system according to claim 1 ,

wherein the at least one processor is configured to obtain a comparison result of a comparison between the target user's user information of each of a plurality of items in the first service and fraudulent user's or authentic user's user information of each of the plurality of items in the second service, and

wherein the at least one processor is configured to estimate fraudulence of the target user based on a plurality of comparison results each corresponding to the plurality of items.

8. The fraud estimation system according to claim 7 ,

wherein the at least one processor is configured to obtain a plurality of pieces of relevance information each corresponding to the plurality of items, and

wherein the at least one processor is configured to estimate fraudulence of the target user based on the plurality of pieces of relevance information.

9. The fraud estimation system according to claim 1 ,

wherein, in the second service, a comparison is made between user information of the target user in the first service and user information of a fraudulent user or an authentic user in the second service, and

wherein the at least one processor is configured to obtain a result of the comparison from the second service.

10. The fraud estimation system according to claim 1 , wherein the at least one processor is configured to receive user registration in the first service,

wherein the target user is a user who performs the user registration, and

wherein the at least one processor is configured to estimate fraudulence of the target user when the user registration is received.

11. A fraud estimation system, comprising at least one processor configured to:

set relevance information based on an aggregated count of the number of matches between user information of fraudulent users of a first service and user information of fraudulent users of a second service, such that a numerical value indicated by the relevance information is larger when the aggregated count is higher;

obtain the relevance information about relevance between the first service and the second service;

obtain a comparison result of a comparison between target user's user information of a predetermined item in the first service and fraudulent user's or authentic user's user information of the predetermined item in the second service, in which fraudulence is estimated based on user information of another item; and

estimate fraudulence of the target user based on the relevance information and the comparison result.

12. A fraud estimation method, comprising:

setting relevance information based on an aggregated count of the number of matches between user information of fraudulent users of a first service and user information of fraudulent users of a second service, such that a numerical value indicated by the relevance information is larger when the aggregated count is higher;

obtaining the relevance information about a relevance between the first service and the second service;

obtaining a comparison result of a comparison between user information of a target user in the first service and user information of a fraudulent user or an authentic user in the second service; and

estimating fraudulence of the target user based on the relevance information and the comparison result.

13. A non-transitory computer-readable information storage medium for storing a program for causing a computer to:

set relevance information based on an aggregated count of the number of matches between user information of fraudulent users of a first service and user information of fraudulent users of a second service, such that a numerical value indicated by the relevance information is larger when the aggregated count is higher;

obtain the relevance information about a relevance between the first service and the second service;

obtain a comparison result of a comparison between user information of a target user in the first service and user information of a fraudulent user or an authentic user in the second service; and

estimate fraudulence of the target user based on the relevance information and the comparison result.

Assignments (2)
CHANGE OF NAME Recorded Jul 13, 2021
From: RAKUTEN, INC.
To: RAKUTEN GROUP, INC.
Reel/Frame 056845/0831 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 18, 2020
From: TOMODA, KYOSUKE
To: RAKUTEN, INC.
Reel/Frame 054413/0188 →
Continuity (1)
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