Real-Time Transaction Data Processing with Dynamic Rule Configuration
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Solution Overview
Problem
Existing financial risk prediction systems, such as U.S. Pat. No. 6,658,393, are limited in their ability to provide comprehensive and versatile real-time analysis of transaction data, failing to effectively handle high volumes of complex transactions and dynamic rule configurations.
Innovation Solution
A transaction data processing system comprising an analysis engine with a rule engine and context generation engine that operates in real-time, parsing transaction data, determining context, and dynamically selecting and executing rules to identify patterns and events, capable of handling high transaction volumes and complex processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predictive model is generated based on historical data to deny transaction authorization requests, then risk prediction capability is improved, but the system cannot provide comprehensive and versatile real-time analysis of transaction data
Solution Approach 1:
The system dynamically configures rule sets based on transaction context and risk parameters. The rule engine loads and executes different rule configurations depending on the transaction type, amount, and detected patterns, allowing the system to adapt its analysis depth and methodology in real-time rather than relying on a static predictive model
Solution Approach 2:
The analysis engine is segmented into multiple independent rule sets that can be selectively executed. Each rule set targets specific fraud patterns or risk scenarios, allowing the system to comprehensive analysis by dividing the complex prediction problem into manageable, specialized rule modules that operate in parallel
2Productivity
If the system analyzes high volumes of complex transactions in real-time, then transaction processing capability is improved, but system complexity increases
Solution Approach 1:
The rule engine is divided into multiple independent rule sets, each handling specific transaction analysis tasks. This segmentation allows parallel processing of different transaction streams simultaneously, increasing throughput while keeping each individual rule set manageable in complexity
Solution Approach 2:
The rule engine serves multiple functions: it executes predefined rules, dynamically loads new rule sets, configures parameters based on transaction context, and adapts to different transaction types. This multi-functionality consolidates what would otherwise require separate systems into a single versatile engine
3Adaptability or versatility
If the system uses dynamic rule configuration for different transaction types, then adaptability is improved, but processing speed decreases
Solution Approach 1:
Rule sets are pre-configured and validated before execution. The system prepares multiple rule configurations in advance, storing them in accessible memory structures, so that during real-time transaction processing, the engine can quickly load and execute the appropriate pre-prepared rule set without performing complex configuration tasks on-the-fly
Data Source
AI summary
A system (1) has an analysis engine (2) for analyzing transaction data (20) received in real time at an interface (3). A context generation engine—(25) directly receives transaction data (20) from the interface (3) and also macro data (26). Queue listeners (52) pull data from a shared memory queue (50(a), 50(b)) and feed it to transaction processors (53), which call a dynamic procedure (43) of the context generation engine (25) in real time. The transaction processors (53) therefore feeds transaction data and context parameters to a rule processor (61), effectively customizing the rule processor in real time. Each rule processor (61) executes a decision tree to determine the rules to execute in real time.