Recurring Transaction AI Modeling for Pre-Authorization Timing
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Solution Overview
Problem
Conventional subscription systems face challenges with failed transactions due to insufficient funds, leading to computational overburden and resource wastage, as they lack effective methods to predict transaction success and mitigate failures.
Innovation Solution
Employing machine-learning models to analyze transaction data and determine scores for success likelihood, with preemptive actions like pre-authorization to improve success chances, and adapt to user behavior through recalibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional subscription systems attempt to charge accounts with insufficient funds, then transaction processing continues, but transaction failure rate increases and computational resources are wasted
Solution Approach 1:
The system performs pre-authorization checks before the actual transaction to predict potential failures in advance. By evaluating account balance, credit limit, and transaction amount beforehand, the system identifies at-risk transactions and takes preventive actions such as notifying users or adjusting transaction timing, thereby improving both success rate and resource efficiency
Solution Approach 2:
The system implements a feedback mechanism where pre-authorization results and actual transaction outcomes are continuously monitored. This feedback loop enables the system to learn from past transactions, refine its prediction models, and adjust its behavior to prevent future failures, thereby improving reliability without increasing resource consumption
2Reliability
If mitigating actions such as notifications and holds are implemented, then transaction success may improve, but system complexity increases
Solution Approach 1:
The system implements pre-authorization as a preliminary action that automatically assesses transaction risk before execution. This single preliminary step consolidates multiple potential mitigating actions into one efficient process, improving reliability without proportionally increasing system complexity
Solution Approach 2:
The system uses automated pre-authorization assessments and machine learning models to evaluate transaction risk without requiring manual intervention. This self-service approach handles complex evaluation logic internally, maintaining high reliability while keeping the user-facing system simple
3Reliability
If pre-authorization is conducted for future recurring transactions, then transaction success likelihood increases, but computational resources are consumed earlier
Solution Approach 1:
The system performs pre-authorization selectively rather than for all transactions. By using machine learning models to identify only high-risk transactions that benefit from pre-authorization, the system avoids unnecessary computational overhead on low-risk transactions while still improving overall success rates
Solution Approach 2:
The system dynamically adjusts the pre-authorization threshold and timing based on transaction characteristics, user history, and account status. This parameter optimization ensures pre-authorization is performed at the right time for the right transactions, maximizing reliability improvement while minimizing computational resource consumption
Data Source
AI summary
Disclosed herein are methods and systems for using machine learning to improve the likelihood of success of recurring transactions. In one example, a suite of different machine learning models can be used together, such that a first machine learning model predicts a likelihood of success for a recurring transaction associated with a user account and the second machine learning model predicts whether a pre-authorization would help with the predicted likelihood of success. As a result, a server may pre-authorize the recurring transactions at a time earlier than the scheduled transaction time and place a hold on the user account using an amount predicted by the second machine learning model where the hold amount can be adjusted in accordance with the user account's activities. Data associated with the recurring transaction itself can be ingested by the second machine learning model for re-calibration purposes.