Abstract: Certain aspects of the disclosure pertain to inferring a candidate entity associated with a transaction with a machine learning model. An organization identifier and description associated with a transaction can be received as input. In response, an entity embedding, comprising a vector for each entity of an organization based on the organization identifier, can be retrieved from storage. A machine learning model can be invoked with the entity embedding and description. The machine learning model can be trained to infer a transaction embedding from the description and compute a similarity score between the transaction embedding and each vector of the entity embedding. A candidate entity with a similarity score satisfying a threshold can be identified and returned. The candidate entity with the highest similarity score can be identified in certain aspects.

Abstract: Aspects of the present disclosure provide techniques for confidence score calibration for automatic transaction categorization. Embodiments include providing one or more first inputs to a prediction model based on a transaction of a user. Embodiments include receiving a prediction of an account with a confidence score from the prediction model based on the one or more first inputs. Embodiments include providing one or more second inputs to a calibration model based on the confidence score, a detail type associated with the account, and a number of accounts of the user. Embodiments include receiving a calibrated confidence score from the calibration model based on the one or more second inputs. Embodiments include determining whether to automatically categorize the transaction into the account based on the calibrated confidence score.