IP Library › Granted Patent US 12,307,465
Granted Patent B2
US 12,307,465 · App. 18/586,459 · Granted May 20, 2025

Systems and methods for a data connector integration framework

Inventors: Vinay Dwivedi (Hyderabad, IN); Magandeep Singh (Chandigarh, IN)
Assignee: PayPal, Inc.
G06Q20/4016G06F9/44505G06F9/45558G06F21/6227G06N3/04G06F2009/45579G06F2009/45595
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Quick Facts
Patent No.
US 12,307,465
App. No.
18/586,459
Granted
May 20, 2025
Kind
B2
Abstract

There are provided systems and methods for a data integration framework that provides an institutional or organizational user data enrichment capability locally. Specifically, instead of relying on the fraud detection platform to constantly updating and/or building new data connectors to intake data from updated or a new data provider, an institutional user, such as a financial institution, may receive a software development kit (SDK) from the fraud detection platform, using which the institutional user may build its own data connector deployed at the institutional user.

Claims (59)

1. A method comprising:

receiving, at a server and from a client device, a data integration request relating to a data provider;

generating, by the server, a data integration package file that is embedded with a building feature for building a data connector at the client device, wherein the data connector is configured to intake data from the data provider into a compliant data format for fraud detection;

sending, via a communication interface at the server, from the server to the client device, the data integration package file to be instantiated at the client device thereby causing the data connector to be built and to convert data from the data provider into the compliant data format at the client device;

obtaining, at the server and from the client device, the converted data having the compliant data format; and

generating, by a neural network implemented at the server, an output indicating fraud information in response to input transaction data.

2. The method of claim 1 , wherein the data integration package file includes a plurality of JAR developer files and a configuration file configured with a software development kit (SDK) for a user to build the data connector using the SDK.

3. The method of claim 1 , wherein generating the data integration package file comprises:

embedding the building feature based on a type of the data provider and/or a type of an entity associated with the client device.

4. The method of claim 1 , wherein generating the data integration package file comprises:

embedding the building feature into the data integration package file in response to a particular type of operation system and/or application running on the client device that sends the data integration request.

5. The method of claim 1 , further comprising:

upon creating the data integration package file, storing the data integration package file at a designated location at the server.

6. The method of claim 1 , further comprising:

determining whether the data provider has an existing data connector with the server;

in response to determining that the data provider has no existing data connector with the server, determining a number of occurrences or a frequency when the client device requests data integration from data providers having no existing data connector with the server; and

in response to determining that the number of occurrences or the frequency when the client device requests data integration from data providers having no existing data connector with the server exceeds a threshold, generating the data integration package file for sending to the client device.

7. The method of claim 1 , further comprising:

training the neural network implemented at the server using the converted data having the compliant data format for fraud detection.

8. The method of claim 1 , further comprising:

receiving, at the server and from the client device, a fraud detection request relating to a transaction; and

using the data having the compliant data format as an input to a fraud detection system at the server without directly intaking the data from the data provider.

9. The method of claim 1 , wherein the data connector is built at the client device by copying the data integration package file to a virtual machine where an exchange web service of the server is deployed.

10. A non-transitory processor-readable storage medium storing processor-executable instructions at a server, wherein the processor-executable instructions comprising:

a first processor-executable instruction embedded with a building feature for building a data connector,

wherein the data connector is configured to intake data from a data provider into a compliant data format for fraud detection;

a second processor-executable instruction that is executable by a processor of the server to deliver the first processor-executable instruction in a form of a data integration package file to a client device from the server,

wherein the first processor-executable instruction is executable by a processor of the client device to perform operations comprising:

generating the data connector at the client device based on the data integration package file;

in response to a data integration request for intaking data from a data provider, invoking the data connector to intake the data from the data provider;

rendering, by the data connector, the data received from the data provider to a compliant format for fraud detection at the server; and

sending the rendered data having the compliant format to the server.

11. The non-transitory processor-readable storage medium of claim 10 , wherein the processor-executable instructions comprise a third processor-executable instruction that is executable by the processor of the server to generate the data integration package file, including:

embedding the building feature based on a type of the data provider and/or a type of an entity associated with the client device.

12. The non-transitory processor-readable storage medium of claim 11 , wherein the

building feature is embedded into the data integration package file further based on a particular type of operation system and/or application running on the client device that sends the data integration request.

13. The non-transitory processor-readable storage medium of claim 10 , wherein the operations further comprise:

building the data connector at the client device by copying the data integration package file to a virtual machine where an exchange web service of the server is deployed.

14. The non-transitory processor-readable storage medium of claim 10 , wherein the data integration package file includes a plurality of JAR developer files and a configuration file configured with a software development kit (SDK) for a user to build the data connector using the SDK.

15. A system comprising:

a memory configured to store a data integration package file at a designated location;

a processor configured to:

generate the data integration package file that is embedded with a building feature for building a data connector at a client device, wherein the data connector is configured to intake data from a data provider into a compliant data format for fraud detection;

send, from a server to the client device, the data integration package file to be instantiated at the client device,

wherein the integration package file causes the data connector to be built and to convert data from the data provider into the compliant data format at the client device;

obtain, from the client device, the converted data having the compliant data format; and

generate, by a neural network implemented at one or more hardware processors, an output detecting possible fraud in response to input transaction data.

16. The system of claim 15 , wherein the data integration package file includes a plurality of JAR developer files and a configuration file configured with a software development kit (SDK) for a user to build the data connector using the SDK.

17. The system of claim 15 , wherein the processor is further to generate the data integration package file, comprising:

embedding the building feature based on a type of the data provider and/or a type of an entity associated with the client device.

18. The system of claim 15 , wherein the processor is further to generate data integration package file, comprising:

embedding the building feature into the data integration package file in response to a particular type of operation system and/or application running on the client device that sends the data integration request.

19. The system of claim 15 , wherein the processor is further to:

determine whether the data provider has an existing data connector with the server;

in response to determining that the data provider has no existing data connector with the server, determine a number of occurrences or a frequency when the client device requests data integration from data providers having no existing data connector with the server; and

in response to determining that the number of occurrences or the frequency when the client device requests data integration from data providers having no existing data connector with the server exceeds a threshold, generate the data integration package file for sending to the client device.

20. The system of claim 15 , wherein the processor is further to:

receive, from the client device, a fraud detection request relating to a transaction; and

use the data having the compliant data format as an input to a fraud detection system at the server without directly intaking the data from the data provider.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 8, 2024
From: DWIVEDI, VINAY; SINGH, MAGANDEEP
To: PAYPAL, INC.
Reel/Frame 066699/0729 →
Priority Claims (1)
IN 202041027744 · Jun 30, 2020 · national
Continuity (2)
Continuation 17345746 · Jun 11, 2021
Related Publication 20240320679A1 · Sep 26, 2024
References Cited (4)
US 11392847B1 · Abdollahian · 2022 [cited by examiner]
US 20140317600A1 · Klunder · 2014 [cited by examiner]
US 20180189871A1 · Lennert · 2018 [cited by examiner]
US 20210374127A1 · Mavrommatis · 2021 [cited by examiner]