Dynamic Risk Analytics System Iterative Data Querying
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Solution Overview
Problem
Merchants face challenges in efficiently evaluating the risk of consumer interactions, as current methods require significant financial and network resources to aggregate data from multiple sources, and may not effectively detect fraudulent activities across multiple interactions.
Innovation Solution
A system and method that iteratively query external data sources to generate a risk assessment by analyzing internal and external data, determining sufficient data based on cut-off values, and automatically acquiring additional data to optimize resource usage and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is aggregated from multiple sources to generate a comprehensive risk assessment, then the reliability of risk evaluation is improved, but the financial and network resources required increase significantly
Solution Approach 1:
The patent segments the risk evaluation process into multiple iterative stages, where data is collected from external sources in successive rounds rather than all at once. The system evaluates risk after each data collection cycle, allowing it to stop early when sufficient information is obtained, thus reducing overall resource consumption while maintaining evaluation reliability.
Solution Approach 2:
The system applies partial action by collecting only the necessary amount of data required to reach a confident risk assessment conclusion. Rather than aggregating all available data from all sources, the iterative process stops data collection when the risk level can be determined with sufficient confidence, avoiding unnecessary resource expenditure.
2Loss of information
If all available data is collected and aggregated, then the completeness of risk assessment is improved, but the time and computational resources required increase
Solution Approach 1:
The patent implements a dynamic data collection strategy where the system adaptively adjusts the data gathering process based on evolving risk assessments. The iterative methodology allows the system to dynamically determine when sufficient data has been collected, balancing completeness against time consumption by stopping the process early when the risk level becomes sufficiently certain.
3Reliability
If a single risk assessment is generated based on total available data, then the comprehensiveness of fraud detection is improved, but the ability to detect progressive fraudulent activity across multiple interactions is reduced
Solution Approach 1:
The patent establishes continuous risk evaluation through iterative data collection and assessment cycles. Rather than performing a single comprehensive assessment, the system continuously updates risk evaluations as new data becomes available across multiple interactions, enabling it to detect progressive fraudulent activities while maintaining accurate risk determination throughout the interaction sequence.
Data Source
AI summary
Embodiments of the invention is directed to a dynamic network analytics system capable of receiving and analyzing queries sent in data messages from data requesters. The queries contain a request from the data requester as to a risk level associated with an interaction conducted by a user. The dynamic network analytics system can determine an optimized process for determining the risk level of the interaction, based on an analysis of past interactions by the user and past interactions by users similar to the user. The dynamic network analytics system can retrieve data from internal and external data sources to generate a response to the query. The dynamic network analytics system conducts the optimized process and uses the retrieved data to generate risk assessments and risk scores in response to the query from the data requester.