Machine Learning Cash Flow Forecasting for PEO Payment Patterns
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Solution Overview
Problem
Professional employer organizations (PEOs) face unique cash flow cycles with shorter payment terms, making existing industry-standard methods for cash flow prediction inaccurate, and there is a need for a more precise forecasting system.
Innovation Solution
A machine learning-based computing method that generates clusters of entities associated with PEOs using frequency, distance, and seasonality-based features, employing models like DBSCAN and K-means clustering to predict future cash flow accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If industry-standard methods (rolling average, alternate weeks seasonality, week of year average) are used for cash flow prediction, then the prediction process is simple and easy to implement, but the prediction accuracy is insufficient for PEOs with unique short payment terms
Solution Approach 1:
The patent transforms the cash flow prediction problem by changing the parameters used in traditional methods. Instead of using fixed time-based parameters (rolling averages, weekly patterns), it introduces feature parameters derived from historical data including payment frequency, amount patterns, and entity-specific characteristics. These transformed parameters enable the machine learning model to capture the unique short payment term patterns of PEOs that traditional methods miss.
Solution Approach 2:
The patent replaces the mechanical calculation systems of traditional methods (manual rolling averages, fixed seasonal adjustments) with a machine learning-based system. This substitution allows the system to automatically learn and adapt to the complex patterns in PEO cash flow data, including the unique 2-4 day payment terms, without requiring manual configuration of time-based rules.
2Measurement precision
If traditional cash flow prediction methods are used, then the computational resources required are minimal, but the system cannot capture unique PEO payment patterns and produces inaccurate forecasts
Solution Approach 1:
The patent performs preliminary actions by pre-processing historical cash flow data to extract meaningful features before feeding them to the machine learning model. This includes calculating payment frequencies, amount distributions, and entity-specific patterns from historical data. By performing these computations in advance, the system reduces the computational burden during actual prediction while maintaining high accuracy for capturing PEO payment patterns.
Solution Approach 2:
The machine learning model is trained on historical PEO cash flow data to automatically learn and adapt to the unique payment patterns of different entities. The system serves itself by continuously improving its predictions through learning from past data, automatically adjusting to variations in payment terms, amounts, and timing without requiring manual intervention or reconfiguration for each new prediction scenario.
3Reliability
If machine learning models (DBSCAN, K-means) are employed to cluster entities and predict cash flow, then prediction accuracy improves for PEOs with short payment terms, but the system complexity and computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the population of client entities into distinct clusters based on their cash flow characteristics using DBSCAN and K-means algorithms. Entities with similar payment patterns, frequencies, and amounts are grouped together, allowing the system to create entity-specific prediction models that account for the unique 2-4 day payment terms of PEOs. This segmentation improves reliability by treating different entity types differently rather than applying a one-size-fits-all approach.
Solution Approach 2:
The system dynamically adapts to different entity characteristics by using machine learning models that can adjust their parameters based on the specific patterns observed in each cluster. The clustering algorithms and prediction models are designed to be flexible, automatically adjusting to variations in payment terms, frequencies, and amounts across different entities, thereby maintaining high reliability despite the diversity of PEO client profiles.
Data Source
AI summary
A machine learning based computing method for computing future cash flow for first users, is disclosed. The machine learning based computing method includes: receiving inputs from second users; extracting data associated with cash flow data of the first users and second information associated with third users, from databases based on the inputs; generating features associated with the third users based on the extracted data; generating clusters associated with the third users of entities associated with the first users based on the features, using a machine learning model; computing future cash flow for the clusters associated with the third users by adding the future cash flow determined for each cluster associated with the third users, for entities associated with the first customers; and providing an output of the future cash flow for the entities associated with the first users, to second users on user interface associated with electronic devices.