Payment Behavior Prediction Model Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Businesses face challenges in predicting payment behavior of entities based on financial records, as different entities exhibit distinct and dynamic payment patterns, making it difficult to accurately forecast when invoices will be paid, which is crucial for cash flow management and financial planning.
Innovation Solution
The method involves configuring and selecting suitable prediction models, such as univariate and multivariate models, to predict payment dates and probabilities of non-payment using historical financial data, allowing for the identification of the most accurate model based on error metrics, and deploying it for predicting payment behavior of entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple different prediction models are used to predict payment dates, then prediction accuracy is improved, but model selection complexity increases
Solution Approach 1:
The patent changes the parameter of model configuration by testing multiple different prediction models (univariate, multivariate, machine learning models) with varying complexity levels. Each model uses different parameters and assumptions about payment behavior, allowing the system to select the optimal model based on performance metrics rather than manually configuring complexity.
Solution Approach 2:
The system performs self-service by automatically evaluating multiple prediction models against historical data and selecting the best-performing model without requiring manual intervention. The automated model selection process compares prediction accuracy across different models anddeploystheoptimalone, reducing the complexity burden on users.
2Reliability
If payment prediction models are continuously updated to adapt to changing payment patterns, then prediction reliability is improved, but computational resources increase
Solution Approach 1:
The patent implements dynamic model adaptation by continuously or periodically retraining prediction models with new payment data. The system adjusts to changing payment patterns by updating model parameters and reselecting optimal models based on current performance, making the prediction system adaptive rather than static.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing historical payment data in ready-to-use formats before model training is needed. This includes cleaning, normalizing, and organizing historical financial record data, which reduces computational resources required during actual model updates and deployment.
3Measurement precision
If historical financial record data is comprehensively analyzed to derive actionable insights, then cash flow forecasting accuracy is improved, but data processing time increases
Solution Approach 1:
The patent extracts only the most relevant features and patterns from comprehensive historical financial record data for model training. Instead of processing all raw data, the system identifies and extracts key payment behavior indicators, invoice characteristics, and entity-specific patterns, reducing processing time while maintaining forecasting accuracy.
Solution Approach 2:
The system performs preliminary data processing by pre-cleaning, normalizing, and organizing historical financial records before they are needed for prediction. This includes handling missing data, standardizing formats, and creating derived features in advance, which significantly reduces processing time during actual cash flow forecasting operations.
Data Source
AI summary
Described is determining a dataset of historical financial record data related to an entity set comprising one or more entities having a common attribute, the historical financial record data comprising an actual payment date or an indication of voiding for each of a plurality of invoices associated with one or more entities of the entity set. Also described is determining a first model of payment behavior of the entity set configured to predict a date of payment of an invoice by an entity.