# US 2021/0174366 A1 · Methods and apparatus for electronic detection of fraudulent transactions

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2021/0174366 A1
- Document ID: US20210174366A1
- Status: Published application
- Kind: A1
- Application number: 16/704,188
- Filed: 2019-12-05
- Published: 2021-06-10
- Earliest claimed: 2019-12-05
- Assignees: Walmart Apollo, LLC
- Applicants: Walmart Apollo, LLC
- Inventors: Yiyi ZENG; Linhong KANG; Xu Si; Arthi VIJAYAKUMAR
- Web page: https://patentgrep.com/patent/US20210174366A1

## CPC

- G06Q 20/4016: {involving fraud or risk level assessment in transaction processing}
- G06N 20/00: Machine learning
- G06Q 20/12: specially adapted for electronic shopping systems
- G06Q 20/4015: {using location information}
- G06Q 20/405: {Establishing or using transaction specific rules}
- G06Q 20/407: {Cancellation of a transaction}
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

This application relates to apparatus and methods for identifying fraudulent transactions. In some examples, a computing device trains a machine learning process with labelled historical transactions. The computing device may then receive transaction data identifying a purchase transaction, such as at a store or on a website. The computing device may execute the trained machine learning process based on the transaction data to generate a trust score. The machine learning process may determine whether the transaction is being made with a trusted device and trusted payment form, for example, to generate the trust score. The trust score may be used to determine whether the purchase transaction is to be allowed. In some examples, the transaction is allowed if the generated trust score is beyond a threshold. In some examples, the computing device may distrust a trusted device or payment form based on one or more events.

## Claims

### Claim 1

A system comprising:
a computing device configured to:
receive purchase data identifying a purchase attempt using a first device and a first payment form;
determine whether the first device is trusted to the first payment form based on first trust data obtained from a database, wherein:
if the first device is trusted to the first payment form, generate a first trust value; and
if the first device is not trusted to the second payment form:
execute a machine learning process based on the purchase data; and
generate a second trust value based on execution of the machine learning process;
generate trust score data based on at least one of the first trust value or the second trust value; and
transmit the trust score data to another computing device.

### Claim 2 (depends on 1)

The system of claim 1, wherein determining that the first device is trusted to the first payment form comprises:
determining that a first previous purchase using the first device and the first payment form was completed earlier than at least a threshold amount of time from receiving the purchase data;
generating the first trust data indicating that the first device is trusted to the first payment form; and
storing the first trust data in the database.

### Claim 3 (depends on 2)

The system of claim 2, wherein the computing device is configured to:
determine a second previous purchase using a second device and the first payment form; and
generate second trust data indicating that the second device is trusted to the first payment form.

### Claim 4 (depends on 2)

The system of claim 2, wherein determining that the first device is trusted to the first payment form comprises determining that no chargeback occurred on the first previous purchase.

### Claim 5 (depends on 2)

The system of claim 2, wherein determining that the first device is trusted to the first payment form comprises determining that no unauthorized transaction complaint was received for the first previous purchase.

### Claim 6 (depends on 1)

The system of claim 1, wherein the first device is not trusted to the first payment form, wherein the computing device is configured to:
receive, from the other computing device, response data indicating that at least one transaction requirement was satisfied;
update the first trust data to indicate that the first device is trusted to the first payment form; and
store the first trust data in the database.

### Claim 7 (depends on 1)

The system of claim 1, wherein the first trust score indicates that the purchase attempt is trustworthy.

### Claim 8 (depends on 1)

The system of claim 1, wherein the computing device is configured to train the machine learning process with labelled historical data indicating a plurality of historical transactions, where each historical transaction is labeled as fraudulent or not fraudulent.

### Claim 9 (depends on 1)

The system of claim 1, wherein the machine learning process is based on decision trees.

### Claim 10 (depends on 1)

The system of claim 1, wherein generating the trust score data comprises:
determining whether the second trust score is beyond a threshold, wherein:
if the second trust score is beyond the threshold, the trust score data indicates that the purchase attempt is to be allowed; and
if the second trust score is not beyond the threshold, the trust score data indicates that the purchase attempt is not to be allowed.

### Claim 11 (depends on 1)

The system of claim 1, wherein executing the machine learning process comprises:
generating features based on the purchase data; and
providing the generated features as input to the machine learning process.

### Claim 12

A method comprising:
receiving purchase data identifying a purchase attempt using a first device and a first payment form;
determining whether the first device is trusted to the first payment form based on first trust data obtained from a database, wherein:
if the first device is trusted to the first payment form, generating a first trust value; and
if the first device is not trusted to the second payment form:
executing a machine learning process based on the purchase data; and
generating a second trust value based on execution of the machine learning process;
generating trust score data based on at least one of the first trust value or the second trust value; and
transmitting the trust score data to another computing device.

### Claim 13 (depends on 12)

The method of claim 12 wherein determining that the first device is trusted to the first payment form comprises:
determining that a first previous purchase using the first device and the first payment form was completed earlier than at least a threshold amount of time from receiving the purchase data;
generating the first trust data indicating that the first device is trusted to the first payment form; and
storing the first trust data in the database.

### Claim 14 (depends on 13)

The method of claim 13 comprising:
determining a second previous purchase using a second device and the first payment form; and
generating second trust data indicating that the second device is trusted to the first payment form.

### Claim 15 (depends on 12)

The method of claim 12 wherein the first device is not trusted to the first payment form, wherein the method comprises:
receiving, from the other computing device, response data indicating that at least one transaction requirement was satisfied;
updating the first trust data to indicate that the first device is trusted to the first payment form; and
storing the first trust data in the database.

### Claim 16 (depends on 12)

The method of claim 12 wherein generating the trust score data comprises:
determining whether the second trust score is beyond a threshold, wherein:
if the second trust score is beyond the threshold, the trust score data indicates that the purchase attempt is to be allowed; and
if the second trust score is not beyond the threshold, the trust score data indicates that the purchase attempt is not to be allowed.

### Claim 17 (depends on 12)

The method of claim 12, wherein the first trust score indicates that the purchase attempt is trustworthy.

### Claim 18

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations comprising:
receiving purchase data identifying a purchase attempt using a first device and a first payment form;
determining whether the first device is trusted to the first payment form based on first trust data obtained from a database, wherein:
if the first device is trusted to the first payment form, generating a first trust value; and
if the first device is not trusted to the second payment form:
executing a machine learning process based on the purchase data; and
generating a second trust value based on execution of the machine learning process;
generating trust score data based on at least one of the first trust value or the second trust value; and
transmitting the trust score data to another computing device.

### Claim 19 (depends on 18)

The non-transitory computer readable medium of claim 18 further comprising instructions stored thereon that, when executed by at least one processor, further cause the device to perform operations comprising:
determining that a first previous purchase using the first device and the first payment form was completed earlier than at least a threshold amount of time from receiving the purchase data;
generating the first trust data indicating that the first device is trusted to the first payment form; and
storing the first trust data in the database.

### Claim 20 (depends on 19)

The non-transitory computer readable medium of claim 19 further comprising instructions stored thereon that, when executed by at least one processor, further cause the device to perform operations comprising:
determining a second previous purchase using a second device and the first payment form; and
generating second trust data indicating that the second device is trusted to the first payment form.