AI Transaction Threat Detection for Geopolitical Event Response
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Solution Overview
Problem
Geopolitical events pose significant security threats to transactions, necessitating enhanced real-time detection and remedial actions to mitigate risks associated with impacted transactions.
Innovation Solution
Implementing artificial intelligence (AI) systems to analyze third-party data for geopolitical events, determine threat severity, and initiate remedial actions such as denying, delaying, or requiring user verification of transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional transaction processing is used without geopolitical event detection, then processing speed is maintained, but transaction security deteriorates due to undetected geopolitical threats
Solution Approach 1:
An AI-based geopolitical event detection system is introduced as an intermediary component between transaction request reception and processing. This mediator analyzes third-party data sources for geopolitical events and determines whether they impact the transaction, thereby enhancing security without requiring fundamental changes to the core transaction processing system.
Solution Approach 2:
The transaction processing system is segmented into distinct functional modules: transaction request reception, geopolitical event detection (using AI model), threat determination, and processing execution. This segmentation allows the geopolitical detection capability to be added independently while maintaining the原有的 transaction processing flow.
2Reliability
If AI-based geopolitical event detection is implemented, then transaction security is improved, but processing time increases due to additional analysis steps
Solution Approach 1:
The system performs preliminary geopolitical event detection and threat assessment before the transaction is fully processed. By determining potential geopolitical impacts in advance, the system can quickly make go/no-go decisions or apply pre-determined remedial actions, reducing the time penalty of the additional security check.
Solution Approach 2:
The AI model dynamically adjusts its analysis parameters and data source selection based on the specific transaction context and current geopolitical landscape. This adaptive parameter adjustment allows the system to focus computational resources only on relevant factors, reducing overall processing time while maintaining detection accuracy.
3Measurement precision
If comprehensive third-party data analysis is performed, then detection accuracy is improved, but data processing complexity increases
Solution Approach 1:
The AI model employs partial action by selectively analyzing only the most relevant third-party data sources and geopolitical indicators for each specific transaction. Rather than comprehensively analyzing all available data, the system focuses on high-impact factors, achieving sufficient detection accuracy with reduced processing complexity.
Solution Approach 2:
The AI-based detection system autonomously manages the complexity of analyzing multiple third-party data sources, correlating geopolitical events with transaction impacts, and determining threat levels. This self-service capability eliminates the need for manual analysis of complex data relationships, allowing comprehensive data analysis without proportionally increasing operational complexity.
4Reliability
If dynamic remedial actions are implemented based on threat severity, then transaction security is enhanced, but operational complexity increases
Solution Approach 1:
The system implements dynamic remedial actions that automatically adjust based on the determined threat severity level. Rather than applying a static approval or rejection decision, the system adapts its response (e.g., approve, deny, request additional verification, delay) according to the specific geopolitical risk assessment, enhancing security while maintaining operational simplicity through automation.
Data Source
AI summary
Systems and methods are described herein for detecting and responding to transaction threats caused by geopolitical events. Such systems and methods may use a provider computing system to receive a transaction request and to receive third-party data from one or more third-party data sources. The provider computing system may identify, using a trained artificial intelligence (AI) model configured to ingest the third-party data, one or more geopolitical events based on the third-party data. The provider computing system may determine, based on the one or more identified geopolitical events, a threat associated with the transaction request and a severity of the threat. In response to the transaction request and based on the severity of the threat, the provider computing system may initiate a remedial action. The remedial action may include denying the transaction request, delaying the transaction request, or requiring a user-verification of the transaction request.