Dynamic Risk Score Calculation for Online Transactions
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Solution Overview
Problem
Current human-defined policies and rules for online transaction security become inadequate and obsolete quickly due to the rapid advancement of hacking technologies, necessitating a more dynamic and real-time risk assessment method.
Innovation Solution
A computer-implemented method using machine intelligence to calculate a risk score for individuals by processing and normalizing internal and external data, employing algorithms like Bayesian networks and neural networks to detect patterns and anomalies, and providing real-time risk assessment for online transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If human-defined policies and rules are used for risk assessment, then the system is easy to understand and implement, but the security reliability becomes inadequate and obsolete quickly due to rapid advancement of hacking technologies
Solution Approach 1:
The patent transitions from static human-defined policies to dynamic machine learning models that automatically adapt to new threats. The system continuously learns from emerging hacking patterns and updates risk assessment criteria in real-time, making the security system dynamic rather than static.
Solution Approach 2:
The machine learning system performs self-training and self-updating without requiring manual policy revisions. The automated risk assessment engine continuously improves its capabilities by learning from new data, eliminating the need for human experts to constantly update security rules.
2Device complexity
If traditional risk assessment methods are used, then the system complexity remains low, but the measurement precision of risk evaluation becomes inadequate
Solution Approach 1:
The patent replaces manual rule-based assessment mechanisms with automated machine learning systems. This substitution enables sophisticated pattern recognition and probabilistic risk calculation, dramatically improving measurement precision while managing complexity through automation.
Solution Approach 2:
The system evaluates multiple dynamic parameters simultaneously (user behavior patterns, transaction characteristics, device information, location data) rather than relying on fixed criteria. This multi-parameter approach with continuous adjustment significantly enhances risk assessment precision.
3Productivity
If real-time risk assessment is implemented, then the productivity and response speed improve, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary risk assessment during user registration and initial interactions, building baseline profiles in advance. This preliminary action enables faster real-time decision-making during actual transactions, as the foundation work is already completed.
Solution Approach 2:
The patent introduces an intermediary layer of machine learning models that sit between raw data and risk decisions. This intermediary automatically processes and synthesizes multiple data sources, managing computational complexity while enabling real-time responses.
Data Source
AI summary
A method, computer system, and computer program product for on-demand risk assessment in on-line transactions comprises: computing, by a machine intelligence application, a risk score for the individual; providing the risk score to a cache; and responsive to receiving new data regarding the individual, calculating a new risk score for the individual and replacing the risk score in the cache with the new score.