Account Balance Prediction Using Recurring Transaction Analysis
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Solution Overview
Problem
Current methods for account transaction and balance prediction do not accurately account for all past and present account activities, leading to incomplete representations of future balances and transactions.
Innovation Solution
A financial institution system aggregates and analyzes internal and external account data, transaction data, seasonal, geographical, and demographic trends to predict future account balances, using predictive models and machine learning to identify recurring transactions and alert account holders of disposable income.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If account holders manually calculate future balances using known activity and estimates, then they can obtain a rough balance prediction, but the accuracy and completeness of the prediction deteriorates because not all past and present account activities are accounted for
Solution Approach 1:
The system automatically performs balance predictions by itself without requiring account holders to manually calculate. The financial institution system autonomously collects account data, identifies recurring transactions, and generates future balance predictions, allowing the system to serve itself in the prediction process.
Solution Approach 2:
The patent replaces manual mechanical calculation methods with an automated electronic system that uses data collection, processing, and predictive algorithms. The mechanical process of manual estimation is substituted with an electronic automated prediction system that analyzes account data and generates accurate forecasts.
2Measurement precision
If the system collects and analyzes comprehensive account data including internal and external accounts, transaction data, and trend data, then the prediction accuracy improves, but the data processing complexity and computational resources increase
Solution Approach 1:
The system segments data collection and processing into distinct modules: one for collecting account data, another for identifying recurring transactions, and a third for generating predictions. This segmentation allows comprehensive data analysis while organizing complexity into manageable, specialized components.
Solution Approach 2:
The system is designed to handle multiple types of data (internal account data, external account data, transaction data, trend data) and perform multiple functions (data collection, recurring transaction identification, balance prediction) through a unified multi-functional platform, reducing overall system complexity despite comprehensive processing requirements.
3Reliability
If the system identifies recurring transactions and uses predictive models, then the representation of future account balance improves, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary identification and classification of recurring transactions in advance, organizing this information before it is needed for balance predictions. This preliminary action reduces the computational burden during actual prediction operations, decreasing processing time while maintaining reliable future balance representation.
Data Source
AI summary
A system includes a financial data storage that stores internal account data, external account data, and transaction data, a communications interface configured to receive external account data from an external data source and transaction data from a merchant, and a database manager that cooperates with the communications interface to store the external account data and the transaction data. The system also includes an account balance prediction processor configured to identify recurring expense transaction(s) and recurring income transaction(s) and update them based on user input, determine predicted expense transaction(s) and a predictive income using a predictive model, and calculate a predicted account balance. The system also includes a transaction monitor configured to compare one or more actual expense transactions with one or more predicted expense transactions, and an alert transmitter configure to generate and transmit one or more alerts based on the comparison.