Abstract: A computer-implemented method includes retrieving data associated with procurement of products; generating a first model training dataset for a machine-learning market module; training the market module to classify a suitability of each product for supply chain insurance; applying the market module to classify a first product of the products as suitable for supply chain insurance; generating a second model training dataset for a machine-learning contract term module; training the contract term module to determine terms of a supply chain insurance contract for the first product; applying the contract term module to determine terms of the supply chain insurance contract; presenting, on a user platform generated on a user interface, the terms of the supply chain insurance contract for the first product; receiving a user input that indicates whether the terms are agreeable; and updating the first model training dataset or the second model training dataset based on the user input.

Abstract: A computer-implemented method includes retrieving data associated with procurement of products; generating a first model training dataset for a machine-learning market module; training the market module to classify a suitability of each product for supply chain insurance; applying the market module to classify a first product of the products as suitable for supply chain insurance; generating a second model training dataset for a machine-learning contract term module; training the contract term module to determine terms of a supply chain insurance contract for the first product; applying the contract term module to determine terms of the supply chain insurance contract; presenting, on a user platform generated on a user interface, the terms of the supply chain insurance contract for the first product; receiving a user input that indicates whether the terms are agreeable; and updating the first model training dataset or the second model training dataset based on the user input.