Intelligent loan qualification based on future servicing capability

摘要

A weight is generated for one or more loan tier metrics for each of a plurality of loan tiers, each loan tier associated with a respective loan tier qualifying score. A plurality of transaction records in the depository account of the user are received, the received transaction records including a chronological listing of debits and credits. A plurality of loan tier metrics are determined based on the chronological listing of debits and credits. A respective metric value is generated for one or more of the loan tier metrics, each respective metric value based on the transaction records in the depository account of the user. A machine learning model determines that based on prior activity of the user recorded in the transaction records, a first loan tier metric of a first loan tier has greater importance in determining a loan servicing capability of the user than a second loan tier metric of the first loan tier, and adjusts a selected weight chosen from a first weight of the first loan tier metric and a second weight of the second loan tier metric, so that the first weight is greater than the second weight. A user score is computer based on applying the adjusted weight to the respective metric value associated with the transaction records that corresponds to the first loan tier metric of the first loan tier, wherein the user score represents a prediction of the user's future loan servicing capability. A determination is made that the user score meets or exceeds a loan qualifying score for the first loan tier.