TECHNICAL FIELD

This disclosure relates to approaches for modeling entity behavior.

BACKGROUND

Conventional approaches to modeling entity behavior suffer from a variety of drawbacks. For example, existing approaches may struggle to balance fixed and periodically updated information, and/or account for ongoing obligations. In large systems, the sheer volume of data may make it difficult to draw correlations between types of data in order to normalize the data for modeling. Even if a model is able to effectively evaluate the data, end users may struggle to interpret the results. Evaluating and making corrections to the model may also be problematic as end users often do not have the sophistication required to update or modify the model should one or more issues be identified. These and other drawbacks exist with conventional approaches.

SUMMARY

A claimed solution rooted in computer technology overcomes problems specifically arising in the realm of computer technology. Various embodiments of the present disclosure may include systems, methods, and non-transitory computer readable media configured to implement a machine learning approach to modeling entity behavior. The system may utilize fixed information and periodically updated information to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. Additionally, the entity's behavior may be predicted efficiently (e.g., with fewer computations) and/or at short intervals (e.g., day-to-day) based on data dependencies (e.g., as defined by a dependency graph). For example, the data dependencies may be used to identify data that needs to be updated (e.g., for ongoing obligations of the entity) when new data is received (e.g., data for a current time period).

The machine learning model utilized to predict or assess entity behavior may be trained using labeled training data. The training data may comprise data labeled based on identified classifications related to the set of entities to which the data relates. Based on these classifications, different numerical labels may be assigned to data used to train the machine learning model. These numerical labels may be modified based on how they are to be perceived relative to one another. Additional labels (or numerical values) may be applied and the labels assigned to various classifications may be modified by a user to best suit the data based on results obtained from application of the model.

When the machine learning model is applied to data related to a set of entities, behavior scores for the set of entities may be determined. Prior to applying the machine learning model, the data associated with the set of entities may be prepared (e.g., cleaned, normalized, and/or otherwise processed) to facilitate one or more operations based on the data. In an exemplary use case, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated by the model in the foregoing exemplary use case, a user may be able to assess the credit riskiness of individual entities (or spokes as otherwise referred to herein) and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.

These and other features of the systems, methods, and non-transitory computer readable media are disclosed herein, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for purposes of illustration and description only and are not intended as a definition of the limits of the invention(s).

