User Account Risk Evaluation Through Virtualized API Instances
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Solution Overview
Problem
Existing systems face inefficiencies in obtaining and evaluating user account data, particularly in securely accessing and normalizing data from multiple external systems, and in providing comprehensive risk assessments for user accounts.
Innovation Solution
The system employs virtualized instances of software applications to interface with external systems via non-public APIs, normalizes user account data, and generates evaluation models using machine learning to provide efficient and objective risk assessments via a standardized API, allowing for customizable data access and interactive user interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtualized instances are used to access external systems via non-public APIs, then data access efficiency and security are improved, but system complexity increases
Solution Approach 1:
The patent introduces virtualized instances as intermediary components that mediate between the evaluation system and external financial systems. These virtualized instances simulate mobile application behavior to access non-public APIs, enabling efficient data retrieval while maintaining a standardized internal interface that abstracts the complexity of multiple external system protocols.
Solution Approach 2:
The patent creates virtualized copies of mobile application instances that can interact with external systems. These virtual instances replicate the authentication and data access patterns of legitimate mobile applications, allowing the system to access external data efficiently without requiring actual mobile devices or direct API integrations with each external system.
2Measurement precision
If comprehensive user account data is collected and evaluated, then risk assessment accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent pre-generates evaluation models using historical user account data from the population database before actual risk assessments are needed. These pre-computed models capture patterns and relationships in the data, allowing the system to quickly evaluate new user accounts by applying existing models rather than performing comprehensive analysis from scratch each time.
Solution Approach 2:
The patent evaluates a subset of user account parameters against pre-generated models rather than processing all possible data points. By focusing on the most relevant parameters identified through preliminary model generation, the system achieves accurate risk assessment while reducing processing time and computational resource requirements.
3Quantity of substance
If multiple external systems are integrated to obtain user account data, then data comprehensiveness is improved, but integration complexity and security risks increase
Solution Approach 1:
The patent creates a universal virtualized instance framework that can interact with multiple different external financial systems through a standardized interface. Rather than implementing separate integration logic for each external system, the virtualized instances provide multi-functional capability to access various non-public APIs using common authentication and data retrieval patterns.
Solution Approach 2:
The virtualized instances serve as intermediary components that abstract the complexity of integrating with multiple external systems. They handle authentication, data retrieval, and protocol translation internally, presenting a simplified standardized interface to the evaluation system while comprehensively accessing data from multiple external sources.
Data Source
AI summary
A user account evaluation system is disclosed for evaluating risk associated with a user account. The system may obtain user account data associated with many user accounts, select a statistically significant subset of the user accounts, and then process (e.g., to determine types of the user accounts, etc.) and analyze the subset of user accounts to generate a plurality of evaluation models. When a new user account is accessed by the system, user account data may be obtained for the new user account, and the new user account may be evaluated based on the plurality of evaluation models. Accordingly, a plurality of evaluation parameter scores may be generated for the new user account, each of which may indicate an amount of risk associated with the user account. Some embodiments of the present disclosure may include machine learning and/or artificial intelligence methods to improve evaluation of the user accounts.