Systems and methods for using a predictive engine to predict failures in machine-learning trained systems for display via graphical user interface

A computer-implemented method for using a machine-learning predictive engine to predict failures in transaction occurrences, comprising:

• receiving electronic prior transaction data corresponding to terms from a plurality of prior transactions that did occur and terms from a plurality of prior transactions that did not occur;
• training a machine learning predictive engine based on the electronic prior transaction data corresponding to the terms from the plurality of prior transactions that did occur and the terms from the plurality of prior transactions that did not occur;
• receiving electronic transaction data comprising a user, an item, and candidate transaction terms, the electronic transaction data being associated with a candidate transaction involving a plurality of parties, the candidate transaction terms being received from the user via a graphical user interface;
• determining, by the trained machine learning predictive engine, a first likelihood that all of the parties in the plurality of parties will agree to the candidate transaction terms based on the electronic transaction data by comparing the candidate transaction terms with a trained model of historical transactions;
• determining whether the first likelihood is below a predetermined threshold;
• in response to determining that the first likelihood is below the predetermined threshold, determining one or more alternate terms of the candidate transaction that increase the first likelihood to a second likelihood that all of the parties will agree to the candidate transaction terms that is above the predetermined threshold;
• and
• causing the graphical interface to display the one or more alternate terms of the candidate transaction.

A computer system for using a machine-learning predictive engine to predict failures in transaction occurrences, the computer system comprising:

• a memory storing instructions;
• and
• one or more processors configured to execute the instructions to perform operations including:
• receiving, from a user using a graphical user interface, electronic transaction data comprising an item and candidate transaction terms, the candidate transaction terms comprising a first transaction term, the electronic transaction data being associated with a candidate transaction involving a plurality of parties;
• determining a first likelihood that all of the parties in the plurality of parties will agree to the candidate transaction terms based on the electronic transaction data by comparing the candidate transaction terms with the machine learning predictive engine, wherein the machine learning predictive engine is configured to:
• represent electronic prior transaction data of each of a plurality of prior transactions that occurred and each of a plurality of prior candidate transactions that did not occur as a respective vector in an n-dimensional space;
• represent the electronic transaction data received from the user as a further feature vector;
• performing a clustering operation to determine distances between the further feature vector and each of a first cluster corresponding to the plurality of prior transactions that occurred and a second cluster corresponding to the plurality of prior transaction that did not occur;
• and
• determine the first likelihood based on the determined distances;
• determining, by the machine learning predictive engine, based on the first transaction term, a second transaction term that, together with the first transaction term, increases the first likelihood that all of the parties will agree to the candidate transaction terms to a second likelihood that is above a predetermined threshold;
• and
• displaying an indication of the second transaction term and an indication of the second likelihood that all of the parties will agree to the candidate transaction terms on the graphical user interface.