Machine learning model ensemble for computing likelihood of an entity failing to meet a target parameter

US11176495B1 · kind B1 · utility

Assignee

• Liquidity Capital M. C. Ltd. · IL

Inventors

• Daniel Ron · Somerville, US
• Meir Moti · Modiin-Maccabim-Reut, IL
• Maymon Oron · Nes Ziona, IL

Key dates

• Filing date: Jun 21, 2020
• Grant date: Nov 16, 2021
• Expiry date: Jun 21, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G06F3/0484
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

There is provided a method of generating a machine learning (ML) model ensemble for computing likelihood of an entity failing to meet a target parameter, comprising: training ML-sub-models that each output sub-values for an input of raw data elements, training a principal ML model that outputs a value of an entity parameter corresponding to the target parameter for an input of the sub-values, using a training dataset including for sample entities, the ML-sub-values and corresponding entity parameters, inputting raw data elements associated with the entity into the ML-sub-models to obtain respective sub-values, in iterations: computing simulated adjustments to the sub-values to generate adjusted sub-values that are inputted into the principal ML model to obtain simulated values for the entity parameter, and generating a risk classifier that generates a likelihood of the entity failing to meet the target parameter according to an analysis of the simulated values for the entity parameter.