Future Account Status Prediction for Transaction Reliability

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Solution Overview

Problem

Transactions that take time to process are susceptible to risk due to potential changes in account status, such as insufficient funds or fraudulent activities, which can lead to transaction failures or resource wastage.

Innovation Solution

A machine learning algorithm is used to analyze account data and predict future account conditions, generating a risk score to assess the likelihood of completing transactions successfully at settlement time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If transactions are processed quickly based on current account status, then transaction speed is improved, but transaction reliability deteriorates due to account status changes during processing

Engineering Contradiction:

Improvetransaction processing speedVSAvoidtransaction completion reliability

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The system performs preliminary actions by predicting future account status before the transaction is fully processed. The machine learning model forecasts account parameters (balance, fraud risk, terms of service compliance) at the expected settlement time, allowing the system to proactively identify and flag potential transaction failures before they occur, thus improving reliability without sacrificing processing speed

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies beforehand cushioning by generating risk scores and confidence indicators that cushion against potential account status changes. These predictions act as a protective layer, allowing the transaction system to prepare contingency measures or reject risky transactions in advance, thereby maintaining high processing speed while ensuring reliable transaction completion

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

2Reliability

If account status is verified continuously during transaction processing, then transaction reliability is improved, but processing time increases

Engineering Contradiction:

Improvetransaction completion reliabilityVSAvoidtransaction processing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system creates a copy or projection of the future account status using machine learning models trained on historical data. Instead of continuously verifying the actual account status during processing, the system uses this predictive copy to assess likely outcomes, maintaining reliability while avoiding the time cost of continuous real-time verification

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system changes the parameter verification approach from checking current actual values to evaluating predicted future values. The machine learning model transforms historical account data into predicted future parameters (balance, fraud indicators, compliance status), allowing the system to assess transaction reliability based on these transformed parameters rather than continuous real-time monitoring

Inventive Principle:

Principle #35Parameter changes

3Reliability

If machine learning models predict future account status, then transaction risk assessment is improved, but system complexity increases

Engineering Contradiction:

Improvetransaction risk assessment accuracyVSAvoidsystem architecture complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The machine learning model serves multiple functions: it predicts account balance, assesses fraud risk, evaluates terms of service compliance, and generates confidence scores all in a single unified system. This multi-functionality improves comprehensive risk assessment while avoiding the complexity of multiple separate prediction systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The machine learning model acts as an intermediary layer between the transaction processing system and the account data. It translates complex historical account data into simplified predictive outputs (risk scores, confidence indicators) that the transaction system can easily interpret and act upon, reducing the complexity burden on the overall system architecture

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12450608B2Transaction evaluation based on a machine learning projection of future account status

Publication Date: 2025.10.21 SARDINEAI CORP

AI summary

The present technology pertains to an evaluation service for enhancing the accuracy of transaction-dependent parameter assessments. The evaluation service processes requests from web services concerning a subject entity's ongoing transaction, which relies on specific user account parameters meeting predetermined criteria. The evaluation service retrieves historical account data associated with the subject entity and utilizes a trained machine-learning model to predict a projected parameter at a future time. This model, optimized for analyzing historical data, forecasts the parameter with precision. The evaluation service generates a probability score indicating the likelihood of the projected parameter's accuracy at the specified future time.