Abstract: Methods, non-transitory computer readable media, and entity resolution management computing devices that receive a request to identify potentially matching records that include an entity across data sources. Entity resolution is initiated to identify the potentially matching records in response to the received request. The initiated entity resolution comprises: executing a first blocking step to identify a first subset of any potentially matching records that include the entity across the data sources; executing a second blocking step to identify a second subset of any potentially matching records that include the entity across the data sources, where the first blocking step is different from the second blocking step; and generating a master set of potentially matching records from the first subset and the second subset of any potentially matching records. The master set of potentially matching records is provided in response to the received request.

Abstract: Methods, non-transitory computer readable media, and entity resolution management computing devices that receive a request to identify potentially matching records that include an entity across data sources. Entity resolution is initiated to identify the potentially matching records in response to the received request. The initiated entity resolution comprises: executing a first blocking step to identify a first subset of any potentially matching records that include the entity across the data sources; executing a second blocking step to identify a second subset of any potentially matching records that include the entity across the data sources, where the first blocking step is different from the second blocking step; and generating a master set of potentially matching records from the first subset and the second subset of any potentially matching records. The master set of potentially matching records is provided in response to the received request.

Abstract: A method, system, and non-transitory computer readable medium includes retrieving, from a database, custom text data related to a client at a client device. The custom text data can include memo data, transaction data, loan data, or asset data related to the client. Then, the method can include generating instructions to prompt a large language model to generate text data. The instructions can be configured to prompt the large language model to extract data from the custom text data for the generation of the text data. Then, the method can include generating, using the large language model, the text data. In some examples, the generated text data is generated based on the custom text data and the instructions.