Utilizing a machine learning model and blockchain technology to manage collateral

A device, comprising:

• one or more memories;
• and
• one or more processors, communicatively coupled to the one or more memories, to:
• receive a request for a collateral allocation between a collateral giver and a collateral receiver on a date;
• receive eligibility schedule data via a blockchain and based on the request,
• wherein the eligibility schedule data includes collateral inventory and eligibility criteria associated with the collateral giver and the collateral receiver on the date;
• receive transaction details data via a smart contract and based on the request,
• wherein the transaction details data includes transaction details associated with the collateral giver and the collateral receiver on the date;
• process the eligibility schedule data and the transaction details data, with a first model, to determine a first collateral allocation between the collateral giver and the collateral receiver on the date;
• process the eligibility schedule data and the transaction details data, with a second model, to determine a second collateral allocation between the collateral giver and the collateral receiver on the date,
• wherein the second model is a different type of model than the first model;
• determine whether the first collateral allocation is predicted to result in a collateral allocation failure;
• and
• perform one or more actions based on the first collateral allocation or the second collateral allocation and based on whether the first collateral allocation is predicted to result in the collateral allocation failure.

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the one or more processors to:
• receive eligibility schedule data via a blockchain,
• wherein the eligibility schedule data includes collateral inventory and eligibility criteria associated with a collateral giver and a collateral receiver on a date;
• receive transaction details data via a smart contract,
• wherein the transaction details data includes transaction details associated with the collateral giver and the collateral receiver on the date;
• process the eligibility schedule data and the transaction details data, with a trained machine learning model, to determine a first collateral allocation between the collateral giver and the collateral receiver on the date,
• wherein a machine learning model is trained with historical exposure allocation data to generate the trained machine learning model that determines a collateral allocation based on inputted eligibility schedule data and inputted transaction details data;
• process the eligibility schedule data and the transaction details data, with a linear programming model, to determine a second collateral allocation between the collateral giver and the collateral receiver on the date;
• determine whether the first collateral allocation is predicted to result in a collateral allocation failure;
• and
• selectively implement the first collateral allocation or the second collateral allocation,
• wherein the first collateral allocation is to be implemented when the first collateral allocation is predicted to not result in the collateral allocation failure, and
• wherein the second collateral allocation is to be selectively implemented when the first collateral allocation is predicted to result in the collateral allocation failure.