Systems and methods for optimizing electronic refund transactions for detected fraudulent transactions
A method for optimizing refunds for suspected or detected fraudulent transactions includes receiving a chargeback analysis request for a potential chargeback transaction from a merchant or a payment processor, extracting identifying information of transactions associated with the chargeback transaction from the chargeback analysis request, searching for a chargeback analysis profile in a profile database, determining whether the chargeback analysis profile exists in the profile database, upon determining that the chargeback analysis profile does not exist in the profile database, obtaining a new fraud analysis profile, determining, based on the chargeback analysis profile, a first probability that the potential chargeback transaction will result in a chargeback, determining, based on the chargeback analysis profile, a second probability that the potential chargeback transaction will result in a chargeback after a proactive electronic refund transaction, and generating a proactive electronic refund transaction based on the first probability and the second probability.
1. A method for optimizing electronic refund transactions for suspect or detected fraudulent transactions, the method comprising:
receiving, by a computer processor, a chargeback analysis request for a potential chargeback transaction from a merchant or a payment processor;
extracting, by the computer processor, identifying information of transactions associated with the potential chargeback transaction from the chargeback analysis request;
retrieving, by the computer processor, historical transaction data associated with the identifying information of transactions from at least one of a plurality of issuer chargeback databases;
generating, by the computer processor, a chargeback analysis profile request, wherein the chargeback analysis profile request includes a unique profile identifier and aggregated historical transaction data from the plurality of issuer chargeback databases;
tokenizing, by the computer processor, the identifying information and the unique profile identifier of transactions associated with the potential chargeback transaction within the chargeback analysis profile request;
sending, by the computer processor, the chargeback analysis profile request to a cloud platform for analysis;
receiving, by the computer processor, a chargeback analysis profile from the cloud platform;
detokenizing, by the computer processor, the identifying information and the unique profile identifier of transactions associated with the potential chargeback transaction within the chargeback analysis profile;
determining, by the computer processor based on the chargeback analysis profile, a first probability that the potential chargeback transaction will result in a chargeback;
determining, by the computer processor based on the chargeback analysis profile, a second probability that the potential chargeback transaction will result in a chargeback following a refund of the potential chargeback transaction; and
generating, by the computer processor, an electronic refund transaction based on the first probability and the second probability.
2. The method of claim 1 , wherein generating the electronic refund transaction based on the first probability and the second probability comprises:
comparing, by the computer processor, the first probability with a first threshold likelihood and, based on determining that the first probability is greater than the first threshold likelihood, generating the electronic refund transaction; or
comparing, by the computer processor, the second probability with a second threshold likelihood and, based on determining that the second probability is not greater than the second threshold likelihood, generating the electronic refund transaction.
3. The method of claim 2 , wherein the first threshold likelihood and the second threshold likelihood are specified in the chargeback analysis request.
4. The method of claim 1 , further comprising:
searching, by the computer processor, for a chargeback analysis profile linked to the extracted identifying information in a profile database; and
determining, by the computer processor, whether the chargeback analysis profile linked to the extracted identifying information exists in the profile database.
5. The method of claim 1 , wherein the chargeback analysis request comprises at least one of identifying information of transactions associated with one or more chargebacks, an account identification, personally identifiable information (PII), transaction detail, and a type of the chargeback analysis request for the potential chargeback transaction, and wherein the PII comprises at least one of a name, an address, a social security number, and an email address.
6. The method of claim 1 , wherein the chargeback analysis profile includes at least one of spending irregularities and suspicious activities associated with the identifying information of the potential chargeback transaction, account identification, or personally identifiable information (PII).
7. The method of claim 6 , wherein the spending irregularities are calculated based on at least one of a related cardholder's spending patterns, a geographic region of an IP address, a billing address, and a type of payment card.
8. A system for optimizing refunds for suspected or detected fraudulent transactions, the system comprising:
a data storage device storing instructions for fraud chargeback automatic refunds in an electronic storage medium; and
a processor configured to execute the instructions to perform operations comprising:
receiving a chargeback analysis request for a potential chargeback transaction from a merchant or a payment processor;
extracting identifying information of transactions associated with the potential chargeback transaction from the chargeback analysis request;
retrieving historical transaction data associated with the identifying information of transactions from at least one of a plurality of issuer chargeback databases;
generating a chargeback analysis profile request, wherein the chargeback analysis profile request includes a unique profile identifier and aggregated historical transaction data from the plurality of issuer chargeback databases;
tokenizing the identifying information of transactions associated with the potential chargeback transaction and the unique profile identifier within the chargeback analysis profile request;
sending the chargeback analysis profile request to a cloud platform for analysis;
receiving a chargeback analysis profile from the cloud platform;
detokenizing the identifying information of transactions associated with the potential chargeback transaction and the unique profile identifier within the chargeback analysis profile;
determining, based on the chargeback analysis profile, a first probability that the potential chargeback transaction will result in a chargeback;
determining, based on the chargeback analysis profile, a second probability that the potential chargeback transaction will result in a chargeback following a refund of the potential chargeback transaction; and
generating an electronic refund transaction based on the first probability and the second probability.
9. The system of claim 8 , wherein generating the electronic refund transaction based on the first probability and the second probability comprises:
comparing the first probability with a first threshold likelihood and, based on determining that the first probability is greater than the first threshold likelihood, generating the electronic refund transaction; or
comparing the second probability with a second threshold likelihood and, based on determining that the second probability is not greater than the second threshold likelihood, generating the electronic refund transaction.
10. The system of claim 9 , wherein the first threshold likelihood and the second threshold likelihood are specified in the chargeback analysis request.
11. The system of claim 8 , wherein the operations further comprise:
searching for a chargeback analysis profile linked to the extracted identifying information in a profile database;
determining whether the chargeback analysis profile linked to the extracted identifying information exists in the profile database; and
based on determining that the chargeback analysis profile linked to the extracted identifying information does not exist in the profile database, retrieving historical transaction data associated with the extracted identifying information from a historical transaction database.
12. The system of claim 8 , wherein the chargeback analysis request comprises at least one of identifying information of transactions associated with one or more chargebacks, an account identification, personally identifiable information (PII), transaction detail, and a type of the chargeback analysis request for the potential chargeback transaction, and wherein the PII comprises at least one of a name, an address, a social security number, and an email address.
13. The system of claim 8 , wherein the chargeback analysis profile includes at least one of spending irregularities and suspicious activities associated with the identifying information of the potential chargeback transaction, account identification, or personally identifiable information (PII).
14. The system of claim 13 , wherein the spending irregularities are calculated based on at least one of a related cardholder's spending patterns, a geographic region of an IP address, a billing address, and a type of payment card.
15. A non-transitory machine-readable medium storing instructions that, when executed by a computing system, causes the computing system to perform a operations for optimizing refunds for suspected or detected fraudulent transactions, the operations including:
receiving a chargeback analysis request for a potential chargeback transaction from a merchant or a payment processor;
extracting identifying information of transactions associated with the potential chargeback transaction from the chargeback analysis request;
retrieving historical transaction data associated with the identifying information of transactions from at least one of a plurality of issuer chargeback databases;
generating a chargeback analysis profile request, wherein the chargeback analysis profile request includes a unique profile identifier and aggregated historical transaction data from the plurality of issuer chargeback databases;
tokenizing the identifying information of transactions associated with the potential chargeback transaction and the unique profile identifier within the chargeback analysis profile request;
sending the chargeback analysis profile request to a cloud platform for analysis;
receiving a chargeback analysis profile from the cloud platform;
detokenizing the identifying information of transactions associated with the potential chargeback transaction and the unique profile identifier within the chargeback analysis profile;
determining, based on the chargeback analysis profile, a first probability that the potential chargeback transaction will result in a chargeback;
determining, based on the chargeback analysis profile, a second probability that the potential chargeback transaction will result in a chargeback following a refund of the potential chargeback transaction; and
generating an electronic refund transaction based on the first probability and the second probability.
16. The non-transitory machine-readable medium of claim 15 , wherein generating the electronic refund transaction based on the first probability and the second probability comprises:
comparing the first probability with a first threshold likelihood and, based on determining that the first probability is greater than the first threshold likelihood, generating the electronic refund transaction; or
comparing the second probability with a second threshold likelihood and, based on determining that the second probability is not greater than the second threshold likelihood, generating the electronic refund transaction.
17. The non-transitory machine-readable medium of claim 16 , wherein the first threshold likelihood and the second threshold likelihood are specified in the chargeback analysis request.
18. The non-transitory machine-readable medium of claim 15 , wherein the operations further comprise:
searching for a chargeback analysis profile linked to the extracted identifying information in a profile database;
determining whether the chargeback analysis profile linked to the extracted identifying information exists in the profile database; and
based on determining that the chargeback analysis profile linked to the extracted identifying information does not exist in the profile database, retrieving historical transaction data associated with the extracted identifying information from a historical transaction database.
19. The non-transitory machine-readable medium of claim 15 , wherein the chargeback analysis request comprises at least one of identifying information of transactions associated with one or more chargebacks, an account identification, personally identifiable information (PII), transaction detail, and a type of the chargeback analysis request for the potential chargeback transaction, and wherein the PII comprises at least one of a name, an address, a social security number, and an email address.
20. The non-transitory machine-readable medium of claim 15 , wherein the chargeback analysis profile includes at least one of spending irregularities and suspicious activities associated with the identifying information of the potential chargeback transaction, account identification, or personally identifiable information (PII).