Puneit Dua

20220366433, Nov 17, 2022

May 27, 2022

17/827015

- Bloomington IL, US
Elizabeth A. Flowers - Bloomington IL, US
Reena Batra - Alpharetta GA, US
Miriam Valero - Bloomington IL, US
Puneit Dua - Bloomington IL, US
Shanna L. Phillips - Bloomington IL, US
Russell Ruestman - Minonk IL, US
Bradley A. Craig - Normal IL, US

G06Q 30/00
G06N 20/00
G06Q 20/32
G06Q 20/40
G06Q 20/24
G06Q 20/20
G06N 5/04
G06Q 20/10
G06Q 20/34
G06Q 30/02
G06V 30/40
G06V 30/194

A method of using browsing activity to identify fraudulent online or virtual applications includes receiving a virtual application over one or more radio frequency links, determining an applicant name on the virtual application, determining an IP address of a source computer from which the virtual application originated, determining an online browsing or search history associated with the IP address, determining whether the online browsing or search history indicates recent Internet searches for the applicant name, and, in response to determining that the online browsing or search history does indicate recent Internet searches for the applicant name, flagging the virtual application as fraudulent and generating an electronic alert indicating that the virtual application is fraudulent to facilitate identifying fraudulent virtual applications for goods or services.

20220351216, Nov 3, 2022

May 16, 2022

17/745541

- Bloomington IL, US
Elizabeth A. Flowers - Bloomington IL, US
Reena Batra - Alpharetta GA, US
Miriam Valero - Bloomington IL, US
Puneit Dua - Bloomington IL, US
Shanna L. Phillips - Bloomington IL, US
Russell Ruestman - Minonk IL, US
Bradley A. Craig - Normal IL, US

G06Q 30/00
G06N 20/00
G06Q 20/32
G06Q 20/40
G06Q 20/24
G06Q 20/20
G06N 5/04
G06Q 20/10
G06Q 20/34
G06Q 30/02
G06V 30/40
G06V 30/194

In a computer-implemented method of using customer data to determine that geolocation-based fraud alerts are false positives, it may be determined that an electronic fraud alert is a geolocation-based alert generated based upon an unexpected or abnormal transaction location. In response, customer data may be obtained from two or more sources via radio frequency links. It may then be determined that the customer data from the sources indicates that a customer is traveling. In response, it may be determined that a customer location indicated by the customer data corresponds to the transaction location. In response to determining that the customer location corresponds to the transaction location, the electronic fraud alert may be marked as a false positive, and the electronic fraud alert may be prevented from being transmitted to a mobile device of the customer, in order to reduce an amount of false positives that are transmitted to customers.

20230099100, Mar 30, 2023

Nov 29, 2022

18/071380

- Bloomington IL, US
Puneit Dua - Bloomington IL, US
Eric Balota - Bloomington IL, US
Shanna L. Phillips - Bloomington IL, US

G06Q 20/40
G06Q 20/24

A heuristic engine includes capabilities to collect an unstructured data set and detect instances of transaction fraud in a financial account. By providing a heuristic algorithm with unstructured transaction sets and indications of particular instances of transactions that correlate with past fraudulent activity allows prevention of future occurrences of fraud. Such heuristic algorithms may learn from past indications of fraudulent activity and improve accuracy of detection of future fraud detections.

20230088436, Mar 23, 2023

Nov 23, 2022

17/993758

- Bloomington IL, US
Elizabeth A. Flowers - Bloomington IL, US
Reena Batra - Alpharetta GA, US
Miriam Valero - Bloomington IL, US
Puneit Dua - Bloomington IL, US
Shanna L. Phillips - Bloomington IL, US
Russell Ruestman - Minonk IL, US
Bradley A. Craig - Normal IL, US

G06Q 20/40
G06N 20/00
G06Q 20/24
G06Q 20/32

A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.

20230072086, Mar 9, 2023

Nov 9, 2022

17/984095

- Bloomington IL, US
Puneit Dua - Bloomington IL, US
Eric Balota - Bloomington IL, US
Shanna L. Phillips - Bloomington IL, US

G06Q 40/02
G06N 5/00

A heuristic engine includes capabilities to collect an unstructured data set and a current business context to calculate a credit worthiness score. Providing a heuristic algorithm, executing within the engine, with the data set and the context may allow determination of predicted future contexts and recommend subsequent actions, such as assessing a credit risk of a customer transaction and reducing the risk of customer transactions by processing the available data. Such heuristic algorithms may learn from past data transactions and appropriate correlations with events and available data.

20210374753, Dec 2, 2021

Mar 22, 2017

15/465856

- Bloomington IL, US
Elizabeth Flowers - Bloomington IL, US
Reena Batra - Alpharetta GA, US
Miriam Valero - Bloomington IL, US
Puneit Dua - Bloomington IL, US
Shanna L. Phillips - Bloomington IL, US
Russell Ruestman - Minonk IL, US
Bradley A. Craig - Normal IL, US

G06Q 20/40
G06N 99/00
G06N 5/04

A method of identifying a potential chargeback scenario includes generating or updating chargeback candidate detection rules, at least by training a machine learning program. The machine learning program may be trained using transaction data associated with financial transactions, and using chargeback determinations, for the financial transactions, that were made in accordance with chargeback rules associated with a card network entity. The method also includes receiving an indication that fraud has been confirmed for a financial transaction associated with a merchant and a financial account, and retrieving transaction data associated with the financial transaction. The method may further include determining, by applying the chargeback candidate detection rules, that a chargeback may be warranted for the transaction, and causing an indication of such to be displayed to one or more people via one or more computing device user interfaces.

20210374764, Dec 2, 2021

Mar 22, 2017

15/465868

- Bloomington IL, US
Elizabeth Flowers - Bloomington IL, US
Reena Batra - Alpharetta GA, US
Miriam Valero - Bloomington IL, US
Puneit Dua - Bloomington IL, US
Shanna L. Phillips - Bloomington IL, US
Russell Ruestman - Minonk IL, US
Bradley A. Craig - Normal IL, US

G06Q 30/00
G06N 99/00

In a computer-implemented method of facilitating a fraud dispute resolution process, types of information historically indicative of fraud (or its absence) may be identified by training a machine learning program using transaction data associated with financial transactions and fraud determinations for those transactions. An indication that fraud is suspected for a first transaction may be received, and transaction data may be retrieved. Based upon at least one of the identified types of information and the transaction data, a first set of one or more queries that are designed to ascertain whether the first transaction was fraudulent may be generated. The first set of queries may be transmitted to a remote computing device for display to the customer, and a first set of one or more customer responses may be received. Based upon the first set of customer responses, it may be determined whether the first transaction was fraudulent.

20210357771, Nov 18, 2021

Apr 24, 2017

15/495594

- Bloomington IL, US
Puneit Dua - Bloomington IL, US
Eric Balota - Bloomington IL, US
Shanna L. Phillips - Bloomington IL, US

G06N 5/04
G06F 17/27
G06N 3/00
G06Q 30/00

A heuristic engine includes capabilities to collect an unstructured data set, for example including question and answer sets from prior customer interactions, and predict future questions based on a current customer context. Such heuristic algorithms may learn from past data transactions and appropriate correlations with events and available data. By improving the heuristic algorithm with growing sets of question and answer interactions, the accuracy of question and answer set predictions may improve over time, allowing improved customer service and understanding of customer interaction outcomes.