Automatically predicting transaction likelihood information and transaction-related temporal information using machine learning techniques

A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

• to obtain historical data pertaining to one or more completed transactions within at least one enterprise system;
• to determine, for at least one of the one or more completed transactions, a set of multiple transaction-related features by processing at least a portion of the obtained historical data;
• to train at least one machine learning model using at least a portion of the set of multiple determined transaction-related features;
• to predict transaction likelihood information and transaction-related temporal information associated with input data attributed to at least one pending transaction within the at least one enterprise system by processing at least a portion of the input data using the at least one machine learning model, wherein predicting the transaction likelihood information and the transaction-related temporal information comprises predicting confidence values attributed to the at least one pending transaction based at least in part on respective status indicators representative of respective stages in at least one transaction-related process;
• and
• to perform one or more automated actions based at least in part on one or more of the predicted transaction likelihood information and the predicted transaction-related temporal information.

An apparatus comprising:

• at least one processing device comprising a processor coupled to a memory;
• the at least one processing device being configured:
• to obtain historical data pertaining to one or more completed transactions within at least one enterprise system;
• to determine, for at least one of the one or more completed transactions, a set of multiple transaction-related features by processing at least a portion of the obtained historical data;
• to train at least one machine learning model using at least a portion of the set of multiple determined transaction-related features;
• to predict transaction likelihood information and transaction-related temporal information associated with input data attributed to at least one pending transaction within the at least one enterprise system by processing at least a portion of the input data using the at least one machine learning model, wherein predicting the transaction likelihood information and the transaction-related temporal information comprises predicting confidence values attributed to the at least one pending transaction based at least in part on respective status indicators representative of respective stages in at least one transaction-related process;
• and
• to perform one or more automated actions based at least in part on one or more of the predicted transaction likelihood information and the predicted transaction-related temporal information.