Dynamic Secured Payment Instrument Graduation via Machine Learning

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Solution Overview

Problem

Existing systems for managing security deposits and credit limits associated with secured payment instruments lack dynamic and real-time adjustments, leading to inefficiencies and suboptimal user benefits as they do not account for changing transaction patterns and credit performance.

Innovation Solution

A computer-implemented method that uses machine learning algorithms to monitor transactions and credit evaluations, dynamically adjusting security deposits and credit limits in real-time, allowing for automatic graduation to unsecured payment instruments based on user metrics and performance data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If security deposits and credit limits are manually managed with static thresholds, then system complexity is reduced, but adaptability to changing transaction patterns and credit performance deteriorates

Engineering Contradiction:

Improveadaptability to transaction patterns and credit performanceVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic adjustment of security deposits and credit limits by replacing static threshold-based systems with machine learning models that continuously learn from transaction patterns and credit performance data. The system adapts to changing conditions by updating models with new data, allowing parameters to fluctuate based on real-time user behavior rather than fixed predetermined values.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback loops where transaction data and credit performance metrics are continuously collected, processed by machine learning models, and used to adjust security deposits and credit limits. This closed-loop feedback mechanism enables the system to respond to changing patterns and improve its predictions over time, directly addressing the adaptability requirement.

Inventive Principle:

Principle #23Feedback

2Productivity

If real-time monitoring and dynamic adjustment of security deposits are implemented, then user benefits and credit optimization are improved, but processing time and computational resources increase

Engineering Contradiction:

Improvecredit limit optimization speedVSAvoidprocessing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and storing transaction data as it occurs, maintaining ready-to-analyze datasets. Machine learning models are trained in advance on historical data and can be quickly retrained or updated with new data without requiring complete reprocessing, thus reducing real-time processing delays while maintaining optimization speed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical rule-based adjustment systems with machine learning algorithms that can process and analyze complex patterns more efficiently. The ML models substitute for manual or systematic rule evaluation, enabling faster decision-making by identifying relevant patterns directly from data without requiring explicit programming of all adjustment scenarios.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If machine learning algorithms are trained continuously with new data, then measurement precision of credit evaluation is improved, but computational energy consumption increases

Engineering Contradiction:

Improvecredit evaluation accuracyVSAvoidcomputational energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system implements periodic action by training machine learning models at scheduled intervals or triggered by significant data milestones rather than continuously. This approach maintains measurement precision by regularly updating models with new data while conserving computational energy by avoiding constant retraining. The system balances accuracy improvements with energy consumption through controlled periodic updates.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS20250005583A1Systems and methods for automatic graduation of secured instruments

Publication Date: 2025.01.02 SYNCHRONY BANK

AI summary

Systems and methods are provided for automatically and dynamically adjusting security deposits and credit limits associated with security payment instruments as these secured payment instruments are used for different transactions. Transactions and credit performance data associated with a secured payment instrument are monitored to determine whether an adjustment to a security deposit and a credit limit associated with the secured payment instrument can be performed. If an adjustment is performed, an account associated with the secured payment instrument is updated according to the adjustment. As new transactions and credit evaluations associated with the secured payment instrument are processed in real-time, new adjustments can be made to the security deposit and credit limit.