Preference-Aware Financial Planning With Flexible Goal Optimization
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Solution Overview
Problem
Conventional financial planning systems lack individualization and flexibility, failing to account for user preferences and resulting in suboptimal achievement of financial goals.
Innovation Solution
A method utilizing an optimization algorithm to analyze user financial goals, preferences, and contributions, generating tailored recommendations to increase the achievable percentage of those goals by adjusting time frames and monetary contributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional textbook-style financial planning advice is provided, then broadly applicable guidance is given to large classes of customers, but individualization and tailoring to user preferences is lost
Solution Approach 1:
The system changes parameters by incorporating user preferences as quantitative inputs (e.g., risk tolerance levels, time horizon preferences, contribution capacity) into the financial planning algorithm. This transforms generic financial advice into personalized recommendations by adjusting planning parameters based on individual user characteristics while maintaining system efficiency through structured parameter management.
2Reliability
If users modify their financial goals to increase funding percentage, then goal achievement likelihood improves, but user preference satisfaction may decrease
Solution Approach 1:
The system implements feedback by continuously evaluating user preferences against goal achievement metrics and adjusting recommendations accordingly. It provides iterative feedback to users about how different goal modifications affect both achievement likelihood and preference alignment, enabling users to make informed decisions that balance reliability with personal satisfaction.
Solution Approach 2:
The financial planning system is made dynamic by allowing flexible adjustment of goal parameters (time horizon, contribution amounts, target savings) based on user preferences and changing circumstances. The system dynamically recalculates optimal paths to goal achievement while adapting to user feedback and preference changes throughout the planning process.
3Measurement precision
If historical data is used for fact-based analysis, then analysis accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant historical data and features needed for financial planning analysis, rather than processing entire datasets. It identifies and extracts key patterns from historical financial data that are specifically applicable to user scenarios, reducing computational burden while maintaining analysis accuracy through selective data extraction.
Data Source
AI summary
A method for providing a tool for individualized financial planning and navigation that is tailored to user preferences and offers flexibility with respect to modifications in financial goals is provided. The method includes: receiving information that includes a financial goal of a user; applying an optimization algorithm that is designed to analyze the information with respect to the financial goal; and using a result thereof to determine an achievable percentage of the financial goal of the user and recommendation(s) for modifying the information in order to increase the achievable percentage of the financial goal. The information further includes a number of time steps for achieving the goal, a first amount of money that the user contributes per time step towards the goal, and a second amount of money that the user expects to save by the end of the time steps.