Recurring Transaction Clustering for Accurate Cash Flow Forecasting
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Solution Overview
Problem
Predicting cash flow is computationally complex, labor-intensive, and error-prone due to the wide range of dynamic factors involved, making it challenging for businesses to ensure adequate access to funds and optimal asset investment.
Innovation Solution
A computer-implemented method for determining reoccurring transactions by clustering transactions based on common attributes and interval patterns, generating models that predict future recurring transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to predict cash flow by considering all dynamic factors, then prediction accuracy is improved, but computational complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the cash flow prediction problem into multiple components: identifying recurring transactions, analyzing transaction patterns, and predicting future cash flow. This segmentation allows each component to be processed independently, reducing overall computational complexity while maintaining prediction accuracy.
Solution Approach 2:
The patent extracts recurring transaction patterns from historical data and separates them from one-time transactions. By taking out and analyzing only the recurring patterns, the system reduces the complexity of predicting future cash flow while preserving the essential predictive information.
2Measurement precision
If traditional methods are used to predict cash flow by analyzing all dynamic factors, then prediction accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The patent performs preliminary analysis by identifying and categorizing recurring transactions before the actual prediction process. This preliminary action prepares the data in advance, allowing the prediction phase to proceed more quickly without sacrificing accuracy.
Solution Approach 2:
The patent creates models that copy the identified recurring transaction patterns and uses these models for prediction. Instead of re-analyzing all raw data each time, the system uses the copied patterns to generate predictions efficiently.
3Adaptability or versatility
If manual analysis of transaction data is performed, then flexibility in handling various factors is improved, but error rate and time consumption increase
Solution Approach 1:
The patent implements automated systems that perform transaction analysis independently without manual intervention. The system automatically identifies patterns, categorizes transactions, and generates predictions, eliminating human errors while maintaining the flexibility to handle various transaction types through programmable logic.
Data Source
AI summary
A computer-implemented method for generating a model of periodic transactions based on the cluster of transactions comprises determining a dataset of transactions occurring during a first time period, and determining a subset of related transactions from the dataset of transactions. The method comprises selecting a first transaction interval pattern, selecting a first clustering criteria, the first clustering criteria comprising a threshold deviation from the first transaction interval pattern, and based on the first transaction interval pattern and the first clustering criteria, identifying a cluster of transactions from the subset of related transactions. Identifying the cluster of transactions comprises: determining an interval difference between the dates of at least one pair of transactions in the subset of related transactions; and determining the cluster of transactions as the transactions of the subset of related transactions that comply with the first transaction interval pattern and the threshold deviation from first transaction interval pattern.