A method for facilitating micro-loan transactions, comprising: aggregating, by a micro-loan system comprising a hardware processor, data regarding a plurality of factors associated with human capital management information and a plurality of users; scrubbing, by the micro-loan system, the aggregated data to generate a refined dataset; dividing, by the micro-loan system, the refined dataset into a training dataset and a testing dataset; constructing, by the micro-loan system via execution of machine learning on the training dataset, a model configured to predict categories for the plurality of users, the categories indicating at least one of borrower or lender; determining, by the micro-loan system, a plurality of integrity scores for the plurality of users based on human capital information for the plurality of users and peer-submitted feedback about the plurality of users; receiving, by the micro-loan system, a request for a loan transaction request from a first user of the plurality of users, the first user predicted by the model to be a borrower; determining, by the micro-loan system, a risk score for the loan transaction based at least on a first integrity score of the plurality of integrity scores for the first user; identifying, by the micro-loan system, a plurality of lenders from the plurality of users based on the determined risk score and integrity scores of the plurality of integrity scores corresponding to the plurality of lenders; facilitating, by the micro-loan system, a negotiation of transaction terms between the borrower and the set of lenders to determine the transaction terms; recording, by the micro-loan system responsive to finalization of the transaction terms, the loan transaction and the transaction terms in a first smart contract in a first blockchain; synchronizing the first blockchain across a plurality of nodes; soliciting, by the micro-loan system responsive to subsequent repayment of the loan transaction according to the transaction terms, peer-submitted feedback; and updating, by the micro-loan system based on user feedback, at least one integrity score of the plurality of integrity scores and feeding said updated integrity score back to a machine learning process to modify the model. aggregating, by a micro-loan system comprising a hardware processor, data regarding a plurality of factors associated with human capital management information and a plurality of users; scrubbing, by the micro-loan system, the aggregated data to generate a refined dataset; dividing, by the micro-loan system, the refined dataset into a training dataset and a testing dataset; constructing, by the micro-loan system via execution of machine learning on the training dataset, a model configured to predict categories for the plurality of users, the categories indicating at least one of borrower or lender; determining, by the micro-loan system, a plurality of integrity scores for the plurality of users based on human capital information for the plurality of users and peer-submitted feedback about the plurality of users; receiving, by the micro-loan system, a request for a loan transaction request from a first user of the plurality of users, the first user predicted by the model to be a borrower; determining, by the micro-loan system, a risk score for the loan transaction based at least on a first integrity score of the plurality of integrity scores for the first user; identifying, by the micro-loan system, a plurality of lenders from the plurality of users based on the determined risk score and integrity scores of the plurality of integrity scores corresponding to the plurality of lenders; facilitating, by the micro-loan system, a negotiation of transaction terms between the borrower and the set of lenders to determine the transaction terms; recording, by the micro-loan system responsive to finalization of the transaction terms, the loan transaction and the transaction terms in a first smart contract in a first blockchain; synchronizing the first blockchain across a plurality of nodes; soliciting, by the micro-loan system responsive to subsequent repayment of the loan transaction according to the transaction terms, peer-submitted feedback; and updating, by the micro-loan system based on user feedback, at least one integrity score of the plurality of integrity scores and feeding said updated integrity score back to a machine learning process to modify the model.