| CPC G06Q 30/0631 (2013.01) [G06F 40/30 (2020.01); G06N 3/006 (2013.01); G06N 5/01 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/06315 (2013.01); G06Q 10/06316 (2013.01); G06Q 20/042 (2013.01); G06Q 20/24 (2013.01); G06Q 20/4016 (2013.01); G06Q 30/01 (2013.01); G06Q 30/018 (2013.01); G06Q 40/02 (2013.01); G06Q 40/03 (2023.01); G06Q 40/12 (2013.12); H04M 3/2218 (2013.01); H04M 3/5175 (2013.01); H04M 3/5191 (2013.01); H04W 12/08 (2013.01); G06Q 30/016 (2013.01); H04M 2203/403 (2013.01)] | 20 Claims |

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1. A computer-implemented method implemented by a processor of a heuristic server for training a heuristic algorithm to generate an account indication of fraudulent activity, comprising:
retrieving, by the processor and from a first memory, a transaction set comprising transaction data associated with a plurality of users, and at least one indication of fraudulent activity;
receiving, by the processor and from a user device, context data associated with a transaction by a user of the plurality of users;
generating, by the processor, the account indication based on executing the heuristic algorithm stored in a second memory of the heuristic server and using the transaction set, the at least one indication of fraudulent activity, and the context data as input to the heuristic algorithm;
providing, by the processor and based at least in part on the account indication, a request for additional context data associated with the transaction;
receiving, by the processor and based on the request, the additional context data from the user device; and
updating, by the processor and using the account indication and the additional context data, the heuristic algorithm stored in the second memory of the heuristic server to determine characteristics or patterns and increase accuracy of subsequently generated account indications.
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