AI Financial Planning System Using Simulated Parameter Variation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing financial planning software tools lack the ability to generate real-time projections or summaries for user-selected objectives, requiring significant time and effort to produce new scenarios, and often rely on static entry fields and industry standard recommendations that may not consider individual client circumstances.
Innovation Solution
A computer-based financial planning system that uses artificial intelligence to analyze user inputs, determine client lifestyle parameters, and generate modified financial plans by applying various financial strategies, scoring and ranking these plans based on their effectiveness and cost, and presenting the highest ranked plan to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If financial planners use existing software tools to develop complex financial plans with multiple scenario adjustments, then the comprehensiveness of the financial plan improves, but the time and effort required to produce new projections increases significantly
Solution Approach 1:
The system pre-calculates and stores projection data for multiple parameter combinations in advance. When a user requests a financial plan, the system retrieves pre-computed projections from storage rather than calculating them in real-time, significantly reducing the time required to generate comprehensive financial plans with multiple scenario adjustments.
Solution Approach 2:
The system uses simulated parameter variation to efficiently generate multiple financial plan scenarios by systematically varying key parameters (such as retirement age, savings rate, investment returns) and storing the results. This allows rapid generation of comprehensive projections for different what-if scenarios without requiring full recalculation each time.
2Ease of operation
If financial planning tools include static entry fields for desired income, projected retirement age, and current savings, then the simplicity of the tool improves, but the ability to facilitate changes to other major factors that affect retiree income is limited
Solution Approach 1:
The system segments the financial planning process into distinct modules: a simple data entry interface for basic parameters, and a separate simulation engine that handles complex parameter variations. This allows the tool to maintain ease of operation for basic inputs while providing extensive adaptability through the simulation capability that can adjust multiple factors independently.
Solution Approach 2:
The system introduces an intermediary simulation layer between the simple static entry fields and the comprehensive financial analysis. This intermediary automatically varies parameters such as retirement age, savings rate, and investment returns based on user selections, translating simple inputs into comprehensive multi-scenario projections without requiring the user to manually adjust each parameter.
3Reliability
If existing software tools generate multiple sets of projections for each scenario adjustment, then the accuracy of financial planning improves, but the productivity of the financial planner decreases
Solution Approach 1:
The system uses simulated parameter variation to efficiently generate multiple financial plan scenarios by systematically varying key parameters (such as retirement age, savings rate, investment returns) and storing the results. This allows rapid generation of comprehensive projections for different what-if scenarios without requiring full recalculation each time.
Solution Approach 2:
The system pre-calculates and stores projection data for multiple parameter combinations in advance. When a user requests a financial plan, the system retrieves pre-computed projections from storage rather than calculating them in real-time, significantly reducing the time required to generate comprehensive financial plans with multiple scenario adjustments.
Data Source
AI summary
A prediction system includes memory hardware configured to store instructions and processor hardware configured to execute the instructions stored by the memory hardware. The instructions include receiving a first set of inputs that indicate objectives, receiving a second set of inputs corresponding to a set of quantitative information, receiving a set of responses, generating a first prediction, and generating a set of actions. The instructions include, for each action, determining a respective parameter. Determining the respective parameter includes performing simulations to generate a set of outcomes and generating a comparison of the set of outcomes to the set of objectives. The instructions include, for each action, generating a respective score. Generating the respective score based is on a first score segment and a second score segment. The instructions include, generating a second prediction based on the set of objectives, the set of quantitative information, and a selected action.


