Abstract: In an embodiment of the disclosed principles, a system and method are provided to output one search result for a user based a search query, thus allowing quick and automated decision making.

Abstract: Systems, methods, and apparatuses for automatically prioritizing unperfected liens are described. A machine learning model may be trained to detect a risk of dealer default. Lien information corresponding to liens for vehicles associated with dealers may be received. Prioritized lien information may be generated based on selection of the liens that satisfy criteria. Financing information for vehicles may be received and correlated with the prioritized lien information. Furthermore, the machine learning model may be used to generate risk scores associated with the liens. The liens may be prioritized based on the aggregate risk scores.

Abstract: Systems, methods, and apparatuses for automatically detecting the risk of dealer default are described. A machine learning model may be trained using a gradient boosting technique on a dataset that includes historical lien information, historical dealer information, and historical vehicle default information. Lien information and financing information for vehicles may be received and correlated. Furthermore, a machine learning model may generate aggregate risk scores for dealers. Furthermore, determination of an aggregate risk score that exceeds a threshold risk score may cause the generation of a notification.