Dynamic Security Deposit Disbursement via Machine Learning

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Solution Overview

Problem

Existing systems lack an efficient and automated method for disbursing security deposits associated with secured payment instruments when these instruments are graduated to unsecured status or canceled, often requiring manual intervention and not providing dynamic disbursement options based on account-specific factors.

Innovation Solution

A computer-implemented method that uses machine learning to identify and provide dynamic disbursement options for security deposits, allowing automatic disbursement to various endpoints upon graduation or cancellation of secured payment instruments, including options like bank deposits, charitable donations, and balance settlement, while updating the algorithm based on user selections and historical data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual intervention is used for disbursement of security deposits, then accuracy and control are improved, but efficiency and automation level deteriorate

Engineering Contradiction:

Improvedisbursement accuracyVSAvoiddisbursement efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system automatically determines disbursement options and executes deposits without requiring manual intervention. The machine learning algorithm autonomously analyzes account data, historical information, and current conditions to select appropriate disbursement destinations, enabling the system to serve itself rather than requiring human operators for each disbursement transaction.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical disbursement processes with an automated computer-based system using machine learning algorithms. The system substitutes human decision-making and manual transfer operations with automated computational processes that analyze data, determine optimal disbursement options, and execute transfers electronically.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If static disbursement options are provided, then system simplicity is maintained, but adaptability to account-specific factors deteriorates

Engineering Contradiction:

Improvedisbursement system simplicityVSAvoidaccount-specific customization

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The disbursement system dynamically adapts to each account's unique characteristics by analyzing real-time account data, transaction history, and customer behavior patterns. The machine learning algorithm continuously learns from new information and adjusts disbursement recommendations accordingly, transforming the system from static to dynamic while maintaining manageable complexity through automated adaptation.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes disbursement parameters such as destination, amount, and timing based on varying account conditions, customer profiles, and historical patterns. The machine learning model adjusts these parameters dynamically for each account based on its specific characteristics, enabling customized disbursement strategies without requiring complex manual configuration.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If automated disbursement is implemented without machine learning, then implementation speed is improved, but disbursement optimization and accuracy deteriorate

Engineering Contradiction:

Improvedisbursement automation speedVSAvoiddisbursement optimization accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The machine learning algorithm continuously receives feedback from disbursement outcomes, customer responses, and account performance data. This feedback loop enables the system to learn from past disbursements, identify patterns, and continuously improve its accuracy in selecting optimal disbursement options, thereby enhancing reliability while maintaining automation speed.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary analysis of account data, historical information, and potential disbursement scenarios before executing any disbursement transaction. The machine learning algorithm pre-determines optimal disbursement options by analyzing multiple factors in advance, ensuring that when disbursement occurs, the decision is already optimized based on comprehensive data review.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20230368287A1Systems and methods for resource disbursement associated with dual-feature payment instruments

Publication Date: 2023.11.16 SYNCHRONY BANK

AI summary

Systems and methods for resource disbursements associated with secured and unsecured payment instruments. In response to either a request to graduate or cancel a secured payment instrument, a deposit disbursement system can automatically disburse an amount corresponding to a deposit provided for issuance of the secured payment instrument. The disbursement of this amount can be performed automatically according to selection of one or more disbursement options from a set of disbursement options that are automatically, and dynamically, provided using a machine learning algorithm according to various factors corresponding to an account associated with the secured payment instrument.