ML Entity Behavior Modeling with Periodic Data Updates
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Solution Overview
Problem
Conventional approaches to modeling entity behavior struggle to balance fixed and periodically updated information, face challenges in large systems due to data volume, and require sophisticated end-user intervention for evaluation and correction.
Innovation Solution
A machine learning approach that utilizes both fixed and periodically updated information to predict entity behavior, incorporating data dependencies to maintain up-to-date predictions and account for ongoing obligations, with a system that includes a machine learning model trained on labeled data to generate behavior scores indicating transactional risk.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches are used to model entity behavior, then the model can be built, but it struggles to balance fixed and periodically updated information and requires sophisticated end-user intervention
Solution Approach 1:
The system automatically performs data normalization, model training, and behavior prediction without requiring end-users to have sophisticated technical knowledge. The machine learning model self-adjusts to periodic data updates and automatically rebalances fixed and updated information, eliminating the need for manual model maintenance by non-expert users.
2Quantity of substance
If conventional approaches are used with large systems, then all data can be processed, but the sheer volume of data makes it difficult to draw correlations and normalize data
Solution Approach 1:
The patent replaces manual data normalization and correlation-drawing processes with automated machine learning algorithms. The system automatically normalizes diverse data sources, draws correlations between data types, and processes large volumes of periodic updates without requiring complex manual intervention or sophisticated data processing infrastructure.
3Measurement precision
If conventional approaches are used, then the model can evaluate data, but end users struggle to interpret the results and make corrections
Solution Approach 1:
The system introduces an automated machine learning model as an intermediary between raw data and user decision-making. The model translates complex behavioral data into interpretable predictions and automatically adjusts to user feedback through periodic retraining, bridging the gap between sophisticated analysis and user-friendly interpretation without requiring users to understand the underlying complexity.
4Reliability
If the system incorporates periodically updated information, then up-to-date predictions can be maintained, but computational complexity increases
Solution Approach 1:
The system implements periodic model retraining and data updates at scheduled intervals rather than continuously. This allows the model to maintain up-to-date predictions by incorporating periodic data updates while optimizing computational resource usage by processing changes only when necessary, rather than performing continuous heavy computations.
Data Source
AI summary
Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized 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. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities 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.