AI Fund Valuation Platform Using Comparable Entity Detection
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Solution Overview
Problem
Conventional portfolio valuation methods are subjective, inaccurate, and time-consuming, leading to inconsistent and unreliable results due to their reliance on human judgment and inefficient processing power.
Innovation Solution
An AI-supported method utilizing a set of artificial intelligence models, including a learned distance k-nearest algorithm, linear regression algorithm, and boosting tree regression algorithm, to identify comparable public entities for private entities within a fund, thereby determining their value and displaying it in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional portfolio valuation methods are used, then human judgment and subjective understanding are applied, but the valuation results are inconsistent and highly unreliable
Solution Approach 1:
The patent replaces the mechanical system of human judgment and subjective analysis with an artificial intelligence-based automated valuation system. The AI model processes financial data, identifies comparable public entities, and generates valuation results without human intervention, thereby eliminating subjectivity and improving reliability and consistency of valuations.
2Productivity
If conventional portfolio valuation methods are used, then manual analysis is performed, but the process is very inefficient and time-consuming
Solution Approach 1:
The AI-based valuation system performs self-service by automatically collecting financial data, identifying comparable entities, and generating valuation results without requiring manual analysis. The system processes valuations continuously and autonomously, dramatically improving productivity and eliminating time delays associated with manual methods.
3Measurement precision
If existing AI modeling methods are used to identify similar companies, then clustering algorithms are applied, but the valuation accuracy is not high
Solution Approach 1:
The patent changes the parameters used for identifying comparable entities from simple clustering algorithms to a multi-factor AI model that considers financial metrics, business characteristics, and market conditions. This parameter transformation enables more accurate identification of truly comparable public entities, thereby improving valuation accuracy despite increased modeling complexity.
4Productivity
If existing AI modeling techniques are used for portfolio valuation, then large processing power is required, but the system becomes costly and inefficient
Solution Approach 1:
The patent segments the valuation process into distinct modular components: data collection, comparable entity identification, valuation calculation, and result generation. Each module processes specific aspects of the valuation independently, reducing overall computational complexity and processing power requirements while maintaining accuracy and improving efficiency.
Data Source
AI summary
Disclosed are method and systems to program a server to identify the value of a fund comprising shares of multiple private entities. The server receives transaction data associated with a fund where the transaction data identifies a proportion of shares within the fund associated with each private entity, price per share of each private entity, and other relevant data. The server then executes multiple artificial intelligence models to identify comparable public entities to each private entity. The server then retrieves stock price data for each public entity and calculates a value for each private entity in real time. The server also displays a value of the fund in real time where identification of each private entity is anonymized.