Action History System for Payment Timing Prediction
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Solution Overview
Problem
Current predictive models for business actions lack context and fail to accurately forecast the timing of payments, especially for small businesses with limited sales history, relying on traditional credit scores that focus on risk of non-payment rather than late payment, and are cumbersome for lending to small and medium businesses due to incomplete data.
Innovation Solution
An action history system that generates action scores and probabilities based on payment history across multiple suppliers, sales invoices, and financial data, providing real-time insights into cash flow and risk, enabling accurate forecasting and lending decisions by analyzing live financial data and payment patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional credit scores are used for prediction, then risk of non-payment can be assessed, but predictive value for timing of actions and context are lacking
Solution Approach 1:
The patent segments traditional credit scoring into multiple specialized prediction models, each focusing on specific actions (payment initiation, payment completion, chargeback, etc.). This segmentation allows each model to capture contextual nuances for specific payment behaviors while maintaining overall predictive accuracy.
Solution Approach 2:
The patent adds temporal and contextual dimensions to traditional credit scoring by incorporating time-series transaction data, merchant categories, geographic information, and behavioral patterns. This transforms static credit scores into dynamic, multi-dimensional prediction frameworks that capture payment timing and context.
2Measurement precision
If comprehensive transaction data is collected for accurate prediction, then predictive value improves, but data complexity and processing requirements increase
Solution Approach 1:
The patent divides the complex prediction system into modular components: data collection layer, feature extraction layer, model training layer, and prediction layer. Each module handles specific tasks independently, reducing overall system complexity while maintaining high predictive accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces intermediary processing layers that transform raw transaction data into standardized features before model input. These intermediaries include normalization layers, feature selection modules, and data cleaning components that simplify complex data while preserving predictive signals.
3Measurement precision
If real-time financial data analysis is implemented, then cash flow forecasting accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing transaction data, pre-training models on historical data, and pre-calculating feature statistics before real-time prediction needs arise. This includes building lookup tables, pre-segmenting data, and preparing model weights in advance to enable rapid real-time forecasting.
Solution Approach 2:
The patent implements dynamic prediction models that adapt to changing data patterns and update predictions in real-time as new transactions arrive. The system dynamically adjusts model parameters, re-trains on incoming data streams, and modifies prediction horizons based on current cash flow conditions, enabling accurate real-time forecasting.
Data Source
AI summary
Methods, systems, and computer programs are presented for generating action scores based on the performance of entities to determine action probabilities. One method includes an operation for accessing data comprising transactions exchanged between entities. The method further includes generating performance values for each entity based on the data, and generating, for each entity, an action score that is an indication of past performance of the entity with respect to the performance values. The method further includes receiving a request, from a user, for action probabilities regarding an event associated with a first entity, each action probability being a probability that another entity responds to the event within a predetermined time frame, determining related entities associated with the event, and determining the action probability for each entity based on the action score of the related entity. The action probabilities are presented within a graphical user interface.