Gaming Behavior Analysis for Financial Risk Assessment
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Solution Overview
Problem
Current methods for evaluating player traits in computer gaming lack comprehensive analysis of gaming behavior data, which limits their effectiveness in providing insights for financial services decisions.
Innovation Solution
A gaming behavior analysis system that utilizes a behavior analytics engine to collect and analyze session-based, game-selection, and ancillary gaming behavior data to determine player traits, which are then used to influence financial services offerings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive gaming behavior data collection is implemented, then player trait assessment accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments gaming behavior data into three distinct categories: session-based behavior data (capturing in-game decisions and actions), game-selection behavior data (tracking game choice patterns), and ancillary gaming behavior data (recording external gaming activities). This segmentation allows the system to comprehensively assess player traits while managing complexity through organized data classification and targeted analysis for each data type.
2Loss of information
If multiple types of gaming behavior data are analyzed, then insights for financial services decisions are improved, but data processing requirements increase
Solution Approach 1:
The patent extracts specific behavioral indicators from each data category that are most relevant to financial services assessment. From session-based data, it extracts decision-making patterns and risk tolerance indicators.从 game-selection data, it extracts preference stability and lifestyle indicators.从 ancillary data, it extracts consistency and reliability indicators. This extraction focuses processing power on the most informative elements while maintaining comprehensive insight coverage.
3Adaptability or versatility
If gaming behavior analysis is used for financial services, then service personalization is improved, but privacy concerns increase
Solution Approach 1:
The patent introduces a behavioral analytics engine as an intermediary that processes gaming behavior data to generate aggregated trait profiles without exposing raw personal data. The engine translates detailed behavioral observations into generalized psychological and behavioral traits (e.g., risk tolerance, decision-making style, consistency) that can be used for financial service personalization while maintaining player privacy through data abstraction and anonymization.
Data Source
AI summary
A system includes at least one hardware processor in communication with a user computing device associated with a user and a memory storing instructions. When executed by the at least one hardware processor, the instructions cause the at least one hardware processor to perform operations including receiving gaming behavior data associated with the user playing a computer game on the user computing device, the gaming behavior data includes one or more of session-based behavior data, game-selection behavior data, and ancillary gaming behavior data, determining a first trait of the user based on the gaming behavior data, and automatically computing a financial risk factor associated with the user based on the determined first trait.