Dynamic Spending Limit Override System for Secondary Users
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Solution Overview
Problem
Current credit transaction card networks lack the ability to dynamically adjust spending limits for secondary users and fail to automatically identify and fund recurring purchases, leading to manual intervention and potential transaction denials.
Innovation Solution
A system utilizing predictive models based on user account data to dynamically adjust spending limits and automatically authorize overrides for secondary users, while also detecting and funding recurring transactions by generating graphical user interfaces and processing transactions in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predetermined spending limit is set for a secondary user, then the primary user can control the secondary user's purchasing power, but the secondary user may be denied purchases even when the excess amount is nominal (e.g., a $151.00 transaction denied when the limit is $150.00)
Solution Approach 1:
The patent applies dynamics by transitioning from static predetermined spending limits to dynamic spending limits that are adjusted in real-time based on contextual factors. The system evaluates transaction context (merchant type, time of day, user location, historical patterns) and dynamically modifies the spending limit threshold, allowing nominal excesses to be approved while maintaining control over significant deviations. This resolves the contradiction by making the limit adaptive rather than rigid.
Solution Approach 2:
The system changes the parameter of spending limit from a fixed value to a variable that adjusts based on multiple factors including transaction amount, merchant category, time of day, and user behavior patterns. By modifying this key parameter dynamically, the system maintains reliable spending control while accommodating reasonable variations, thus resolving the contradiction between control reliability and transaction approval ease.
2Reliability
If manual intervention is required for spending limit overrides, then the primary user maintains full control over secondary user transactions, but the process requires manual intervention and time
Solution Approach 1:
The system performs preliminary action by pre-establishing spending limit parameters and contextual evaluation criteria before transactions occur. It proactively monitors incoming transactions and automatically evaluates them against predefined and dynamic criteria, making decisions before the primary user would need to intervene. This maintains control reliability through pre-configured parameters while eliminating manual intervention time for routine transactions.
Solution Approach 2:
The system enables self-service by allowing the secondary user to initiate transactions that are then automatically evaluated and approved or denied based on dynamic spending limits and contextual factors, without requiring primary user intervention. The primary user retains control through initial parameter setting and can review exceptions, but routine transactions are handled autonomously, reducing manual intervention time while maintaining control reliability.
3Device complexity
If recurring transactions are not automatically identified and funded, then the system maintains simplicity, but users must manually fund each recurring purchase
Solution Approach 1:
The system uses copying by creating a template or model of recurring transaction patterns based on historical data. Once a recurring transaction is identified (e.g., monthly subscription), the system copies the funding mechanism and automatically replicates the payment process for subsequent occurrences. This adds minimal complexity by using pattern recognition but dramatically improves ease of operation for recurring transactions.
Solution Approach 2:
The system performs preliminary action by proactively identifying recurring transaction patterns and pre-configuring automatic funding mechanisms before the next recurrence date. It monitors transaction history, detects patterns, and sets up automated funding in advance, eliminating the need for manual intervention while maintaining system simplicity through automated pattern recognition and execution.
4Extent of automation
If dynamic spending limits with predictive models are implemented, then the system can automatically authorize overrides for nominal excesses, but the system complexity increases
Solution Approach 1:
The system manages complexity by changing parameters in a structured way - using predefined contextual factors (merchant category, time of day, location, transaction amount thresholds) rather than complex black-box predictive models. The dynamic spending limit adjustment is based on rule-based parameter changes that are interpretable and manageable, achieving automatic override authorization for nominal excesses while controlling system complexity through transparent, rule-based logic.
Data Source
AI summary
A system including: one or more processors; a memory storing instructions that, when executed by the one or more processors are configured to cause the system to receive primary and secondary user account data. The system generates one or more predictive model systems based on the primary and secondary user account data. The system receives a first input from the primary user corresponding to a first spending limitation for the secondary user. The system identifies a first transaction of the secondary user exceeding the spending limitation and determines using the one or more predictive model systems whether to authorize a spending limitation override. The system automatically authorizes the spending limitation override when the first transaction exceeds the spending limitation by less than a predetermined threshold. The system can also identify and automatically fund recurring transactions with an associated funding account using the one or more predictive model systems.