Predictive Event Budgeting System Using Consumer Data Aggregation
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Solution Overview
Problem
Consumers face difficulties in accurately determining the total costs and financial impacts of life and financial events, often leading to unexpected expenses due to overlooked hidden costs, which can cause significant stress and disrupt financial planning.
Innovation Solution
A system and method for predictive event budgeting that collects and analyzes financial data from contributing consumers, identifies changes associated with specific events, and provides users with aggregated and categorized data to anticipate and prepare for upcoming costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumers rely on personal knowledge and static budgeting, then budget simplicity is maintained, but accuracy of cost prediction deteriorates due to hidden costs being overlooked
Solution Approach 1:
The patent introduces an intermediary system (the budgeting platform) that mediates between consumers and cost prediction. This platform aggregates data from multiple sources, processes it through algorithms, and delivers predictions to consumers, thereby improving accuracy without requiring consumers to directly manage the complex data collection and analysis processes themselves
Solution Approach 2:
The system creates copies of financial data from multiple consumers and scenarios, then analyzes these copies to generate predictions. By working with replicated data sets rather than requiring consumers to manually collect and analyze raw data, the system improves prediction accuracy while keeping the consumer interface simple
2Measurement precision
If consumers collect comprehensive financial data from multiple sources, then accuracy of cost prediction improves, but ease of operation deteriorates due to difficulty in data collection and analysis
Solution Approach 1:
The system enables self-service by automatically collecting financial data from multiple sources (banks, credit card companies, employers) without requiring manual consumer intervention. The platform autonomously aggregates, cleans, and analyzes data, then presents predictions to consumers in an easily digestible format, maintaining both accuracy and ease of operation
Solution Approach 2:
The budgeting platform performs multiple functions universally: data collection from various sources, data cleaning, event detection, cost prediction, and result delivery. By consolidating these diverse functions into a single universal system, consumers gain access to comprehensive predictions without having to manually perform each individual task
3Reliability
If consumers prepare for financial events in advance, then financial stability improves, but loss of time occurs in identifying all likely bills and anticipating fluctuations
Solution Approach 1:
The system performs preliminary actions by proactively identifying upcoming financial events (such as seasonal utility increases, insurance renewals, or tax payments) and predicting associated costs before they occur. This allows consumers to prepare financially in advance without spending time manually researching and identifying all potential bills and fluctuations
Solution Approach 2:
The system uses feedback loops to continuously monitor financial data, learn from past consumer behavior patterns, and refine predictions over time. By analyzing historical data and providing feedback on actual versus predicted costs, the system improves its accuracy and helps consumers maintain financial stability more efficiently over the long term
Data Source
AI summary
Financial data associated with one or more “contributing consumers” is obtained. The financial data is then aggregated, analyzed, and/or categorized, according to one or more events and one or more criteria/parameters associated with the financial transaction data and/or the contributing consumer. The aggregated and/or categorized data is then stored. A “user consumer” then initiates a request for predictive event cost data associated with one or more specified events and the aggregated and/or categorized data representing the event related changes in the financial data is searched based. Results data representing the changes in the financial data for one or more similarly situated contributing consumers associated the specified event is then obtained and presented to the user consumer.