Abstract: The methods and systems relate to generating hierarchical data structures based on crowdsourced data featuring non-homogenous metadata. For example, the methods and systems address challenges with conventional crowdsourcing platforms, which fail to provide a mechanism for identifying contributors that would be best fit for a task, particularly when there is no standardized criteria for that task. Additionally, the methods and systems negate the need for peer-vetting and manual review of contributions thus significantly limiting bottlenecks in the development process. The methods and systems provide these advantages through the use of a novel data structure generated based on machine learning.

Abstract: A system and method for measuring or quantifying the probability of default of a borrower. Credit factors from companies that banks have extended loans to are inputted and collected into a processor. The method employs a process utilizing an optimization function and a standard multivariate nonlinear regression to process client information and to provide an output value whose value is indicative of the likelihood or risk of default by a particular borrower.