Heuristic account fraud detection engine
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.
1. A computer-implemented method, executed by one or more processors, for training a heuristic algorithm to generate an indication of fraudulent transaction activity within a financial account, comprising:
retrieving, by the one or more processors and from a first memory, an unstructured transaction set comprising aggregated transaction data from plurality of users and at least one indication of fraudulent transaction activity;
receiving, by the one or more processors, an indication of a pending financial transaction from a network interface device;
receiving, by the one or more processors and from a user of the plurality of users via a human machine interface, natural language context data for the pending financial transaction and past transactions;
accessing, by the one or more processors, the heuristic algorithm stored in a second memory;
executing, by the one or more processors, the heuristic algorithm, to generate a predicted probability of fraudulent account activity using the unstructured transaction set, the natural language context data, and the indication of the pending financial transaction;
prompting, by the one or more processors and based at least in part on the predicted probability of fraudulent activity, the user to provide additional context data for the pending financial transaction;
receiving, by the one or more processors, the additional context data from the user; and
training, by the one or more processors, the heuristic algorithm using the predicted probability of fraudulent account activity, the natural language context data, and the additional context data.
2. The computer-implemented method of claim 1 , wherein the at least one indication of fraudulent transaction activity comprises an unauthorized withdrawal.
3. The computer-implemented method of claim 1 , wherein the predicted probability of fraudulent account activity corresponds to an unauthorized credit transaction.
4. The computer-implemented method of claim 1 , wherein the unstructured transaction set further comprises one or more past transactions related to at least one account.
5. The computer-implemented method of claim 1 , wherein the first memory comprises an external transaction server.
6. The computer-implemented method of claim 1 , wherein the second memory comprises an external heuristic server.
7. The computer-implemented method of claim 1 , further comprising updating, by the one or more processors, the heuristic algorithm stored in the second memory using the predicted probability of fraudulent account activity.
8. A computer system configured to train a heuristic algorithm to generate an indication of fraudulent transaction activity within a financial account, the computer system comprising at least one of one or more processors or one or more transceivers configured to:
retrieve, from a first memory, an unstructured transaction set comprising aggregated transaction data for a plurality of users, including at least one indication of fraudulent transaction activity associated with one or more users of the plurality of users;
receive an indication of a pending financial transaction from a network interface device;
receive, from a user of the plurality of users, natural language context data for the pending financial transaction and past transactions;
access the heuristic algorithm stored in a second memory;
execute the heuristic algorithm to generate a predicted probability of fraudulent account activity using the unstructured transaction set, the natural language context data, and the indication of the pending financial transaction;
prompt, based at least in part on the predicted probability of fraudulent account activity, the user to provide additional context data for the pending financial transaction;
receive the additional context data from the user; and
train the heuristic algorithm using the predicted probability of fraudulent account activity, the natural language context data, and the additional context data.
9. The computer system of claim 8 , wherein the at least one indication of fraudulent transaction activity comprises an unauthorized withdrawal.
10. The computer system of claim 8 , wherein the at least one indication of fraudulent transaction activity comprises an unauthorized credit transaction.
11. The computer system of claim 8 , wherein the unstructured transaction set further comprises one or more past transactions related to at least one account.
12. The computer system of claim 8 , wherein the first memory comprises an external transaction server.
13. The computer system of claim 8 , wherein the second memory comprises an external heuristic server.
14. A non-transitory computer readable medium, comprising computer readable instructions that, when executed, cause one or more processors to:
retrieve, from a first memory, an unstructured transaction set comprising aggregated transaction data for a plurality of users and at least one indication of fraudulent transaction activity;
receive an indication of a pending financial transaction from a network interface device;
receive, from a user of the plurality of users via a human machine interface, natural language context data for the pending financial transaction and past transactions;
access a heuristic algorithm stored in a second memory;
execute the heuristic algorithm to generate a predicted probability of fraudulent account activity using the unstructured transaction set, the natural language context data, and the indication of the pending financial transaction;
prompt, based at least in part on the predicted probability of fraudulent account activity, the user to provide additional context data;
receive the additional context data from the user; and
train the heuristic algorithm using the predicted probability of fraudulent account activity, the natural language context data, and the additional context data.
15. The non-transitory computer readable medium of claim 14 , wherein the at least one indication of fraudulent transaction activity comprises an unauthorized withdrawal.
16. The non-transitory computer readable medium of claim 14 , wherein the at least one indication of fraudulent transaction activity comprises an unauthorized credit transaction.
17. The non-transitory computer readable medium of claim 14 , wherein the unstructured transaction set further comprises one or more past transactions related to at least one account.
18. The non-transitory computer readable medium of claim 14 , wherein the first memory comprises an external transaction server.
19. The non-transitory computer readable medium of claim 14 , wherein the second memory comprises an external heuristic server.
20. The non-transitory computer readable medium of claim 14 , wherein the computer readable instructions, when executed, further cause the one or more processors to:
update the heuristic algorithm stored in the second memory using the predicted probability of fraudulent account activity.