Machine-Learning Risk Assessment Combining Traditional and Behavioral Data
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Solution Overview
Problem
Existing risk assessment systems often rely on single sources of data, leading to inaccurate and incomplete evaluations, resulting in entities being denied access to services due to inadequate risk assessment.
Innovation Solution
Integrate traditional and nontraditional risk assessment data using machine-learning techniques to generate an integrated risk assessment value, enhancing accuracy and completeness by leveraging a broader range of data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single source of data is used for risk assessment, then the system complexity is reduced, but the measurement precision of risk assessment deteriorates
Solution Approach 1:
The patent combines multiple data sources including traditional risk data (credit scores, payment history) with nontraditional risk data (device information, location data, browsing behavior) into a unified risk assessment system. This merging of diverse data sources improves measurement precision while managing system complexity through integrated processing.
Solution Approach 2:
The risk assessment system is designed to handle multiple types of data sources and processing methods within a single platform. The system can process both traditional financial data and nontraditional behavioral data, making it multi-functional and reducing the need for separate systems for different data types.
2Measurement precision
If multiple data sources are integrated for risk assessment, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent segments the risk assessment process into distinct modules: data collection from multiple sources, data authentication, traditional risk assessment processing, nontraditional risk assessment processing, and integrated risk value determination. This segmentation manages device complexity by organizing complex functions into manageable, independent components.
Solution Approach 2:
The system employs intermediary components including data authentication services and machine learning models that mediate between raw data from multiple sources and the final risk assessment output. These intermediaries simplify the integration process and manage complexity by handling data standardization and processing transformations.
3Ease of operation
If traditional risk assessment methods are used, then the ease of operation is maintained, but the reliability of risk assessment deteriorates
Solution Approach 1:
The system automatically collects, authenticates, and processes data from multiple sources without requiring manual intervention. Machine learning models automatically integrate traditional and nontraditional data, and the system self-adjusts risk assessments based on processed information, maintaining ease of operation while improving reliability.
Solution Approach 2:
The system implements feedback loops where risk assessment results are continuously refined based on new data from multiple sources. The machine learning models learn from patterns in the data and adjust assessments automatically, improving reliability while requiring minimal operational input from users.
Data Source
AI summary
Systems and methods for using machine-learning techniques to provide risk assessment based on multiple sources of data are described herein. Data about an entity can be received, and the data can be authenticated. Integrated risk data about the entity can be received. The integrated risk data can include traditional risk assessment data and nontraditional risk assessment data. An integrated risk assessment value can be determined based on the integrated risk data by aligning a first output from a first risk assessment model and a second output by a second risk assessment model. A responsive message including at least the integrated risk assessment value and associated information for the entity can be transmitted to a remote computing device for use in controlling access of the entity to one or more interactive computing environments.