Transaction Optimization Model for Authorization Conversion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional electronic transaction processing methods fail to optimize transaction conversion rates due to factors such as insufficient account information, card expiration, and other factors not considered in authorization requests, leading to declined transactions and undesirable outcomes for merchants and account holders.
Innovation Solution
A computer-implemented method and system that retrieve payment transaction parameters and authorization results from a database, generate a transaction success model using data science methods for statistical inference, modify transaction parameters based on this model, and submit the modified transactions for processing to improve authorization conversion rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods submit transactions according to factors specific to the acquirer processor or merchant, then the submission process is simple and straightforward, but the transaction conversion rate is low due to not considering factors that affect authorization success
Solution Approach 1:
The system performs preliminary actions by retrieving historical transaction data and generating a transaction success model before actual transaction submission. The model identifies authorization success factors and optimal parameter combinations in advance, allowing the system to proactively optimize transactions rather than reactively handling declines.
Solution Approach 2:
The system implements feedback by retrieving authorization results from historical transactions and using them to generate and refine the transaction success model. The model continuously learns from past authorization outcomes, incorporating feedback loops that improve transaction optimization over time based on measured performance data.
2Reliability
If the system retrieves and analyzes historical transaction data to generate optimization models, then transaction conversion rates improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs data retrieval and model generation as preliminary actions before the actual transaction authorization process. By pre-processing historical data and establishing the success model in advance, the system minimizes the computational burden during live transaction processing, reducing time loss while maintaining high authorization success rates.
3Reliability
If the system modifies transaction parameters based on the success model, then the number of declined transactions decreases, but the complexity of transaction processing increases
Solution Approach 1:
The transaction success model acts as an intermediary between historical data and transaction optimization. The model translates complex historical patterns into actionable optimization recommendations, simplifying the processing by providing clear guidance on which parameters to modify rather than requiring direct complex analysis during transaction processing.
Data Source
AI summary
A method for optimizing transaction authorization conversion rates using measured feedback includes retrieving payment transaction parameters and authorization results for a plurality of past payment transactions from a database, generating a transaction success model comprising authorization success factors for each of a plurality of payment transaction parameters using data science methods for statistical inference based on the retrieved payment transaction parameters and authorization results, receiving, at an acquirer processor, a payment transaction from a merchant, modifying one or more parameters of the payment transaction according to the generated transaction success model, and submitting the modified payment transaction to a financial institution for processing.