AI Model Selection for Real-Time Transaction Error Correction
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Solution Overview
Problem
High-throughput transaction computing systems face challenges in detecting and correcting erroneous data in real-time, leading to potential transaction failures and increased manual intervention, which can result in critical production issues and asset loss.
Innovation Solution
A self-healing, event-driven framework utilizing advanced artificial intelligence (AI) modeling algorithms to predict and correct data errors in transaction datasets, automatically inserting corrected data into the transaction pipeline for uninterrupted processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time error detection and correction is implemented in high-throughput transaction systems, then transaction reliability is improved, but system complexity and processing latency increase
Solution Approach 1:
The system pre-loads multiple AI model candidates into memory before they are needed for error correction. This preliminary preparation allows the system to quickly switch between pre-loaded models during real-time transaction processing without the overhead of model loading, thus improving reliability while minimizing the impact on processing latency and system complexity
Solution Approach 2:
The system creates and maintains multiple copies of AI models in memory, each trained to correct different types of data errors. These model copies enable parallel error detection and correction capabilities, allowing the system to handle various error types simultaneously without sequential processing overhead, thereby improving transaction reliability while maintaining efficient throughput
2Adaptability or versatility
If multiple AI models are loaded into memory for error correction, then error correction capability is improved, but memory consumption increases
Solution Approach 1:
The system loads a set of AI models that is sufficient to handle the majority of error types encountered in practice, rather than loading every possible model for every conceivable error scenario. This partial loading approach provides adequate error correction capability for high-throughput transactions while keeping memory consumption within acceptable limits
Solution Approach 2:
The system dynamically adjusts the number and types of AI models loaded into memory based on the specific transaction workload and error patterns observed. By changing the parameter of model quantity and selection according to actual needs, the system optimizes the balance between error correction capability and memory consumption for different operational scenarios
3Measurement precision
If AI models are retrained with updated data, then measurement precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system implements periodic retraining of AI models at scheduled intervals or when triggered by specific conditions (such as accumulating a certain amount of new transaction data). This periodic approach allows the system to maintain improved error detection precision through regular updates while avoiding continuous retraining that would cause excessive processing time and disrupt high-throughput transaction operations
Solution Approach 2:
The system prepares and validates new training data in advance before initiating model retraining processes. By performing preliminary data collection, cleaning, and validation operations beforehand, the system minimizes the actual retraining time and ensures that model updates can be performed efficiently with reduced impact on transaction processing continuity
Data Source
AI summary
Automated intelligent correction of erroneous data for uninterrupted transaction processing includes a server that captures real-time transaction data from a transaction message stream. The server determines errors in the real-time transaction data for the transaction. The server identifies a trained artificial intelligence (AI) model from a plurality of trained AI models based upon the determined errors and executes the identified AI model using the real-time transaction data as input to correct the errors. The server determines a workflow state of the transaction using the real-time transaction data. The server generates a message for insertion in the transaction message stream, the message comprising the corrected real-time transaction data. The server inserts the generated message in the transaction message stream.