Private Equity Derivatives Platform With Digital Identity Pricing
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Solution Overview
Problem
Private equity investments face challenges due to complexity, lack of transparency, and computational scalability issues, limiting access to derivatives and hindering efficient risk management for diverse investors, with existing computer systems struggling to handle the computational loads and data management needs.
Innovation Solution
A cloud-based platform utilizing digital identities and machine-readable references enables efficient creation and management of private equity derivatives, allowing parallel processing and secure data segregation for diverse investor needs, using CPUs and GPUs for computational scalability and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional financial markets infrastructure is used for private equity derivatives, then market liquidity and tradability are improved, but computational scalability and data security deteriorate due to inability to handle complex pricing and diverse investor needs
Solution Approach 1:
The system segments the computational infrastructure into distributed computing nodes that can independently process different derivative pricing calculations. Each node handles specific investor requests and asset classes, enabling scalable adaptation to diverse needs without overwhelming a centralized system. The segmentation of data into isolated environments further supports this by allowing parallel processing of multiple investor portfolios.
Solution Approach 2:
The platform implements a universal derivatives archetype framework that can accommodate multiple types of private equity derivatives (options, futures, forwards) and various investor profiles through a single unified system. This multi-functional architecture eliminates the need for separate infrastructure for different derivative types while maintaining specialized processing capabilities through configurable parameters and machine-readable instructions.
2Loss of information
If comprehensive data access is provided to all investors, then transparency and investment decision quality are improved, but security and data protection deteriorate due to exposure of sensitive private equity information
Solution Approach 1:
The system applies local quality by providing different levels of data access and transparency to different investors based on their credentials, investment mandates, and risk profiles. Each investor receives customized data views that are transparent and comprehensive for their specific needs while maintaining security boundaries. The data isolation environments ensure that sensitive information is only accessible to authorized parties with appropriate clearance levels.
Solution Approach 2:
The platform acts as an intermediary between private equity assets and investors, mediating data access through a secure digital identity verification system. This intermediary layer provides the necessary transparency for investment decisions by standardizing risk-return信息披露 while protecting underlying asset data through controlled access mechanisms and encrypted data transmission channels.
3Measurement precision
If manual due diligence and verification processes are used, then accuracy of investor verification is improved, but processing time and operational efficiency deteriorate
Solution Approach 1:
The system implements self-service through automated digital identity verification where investors independently complete KYC/AML checks by uploading required documentation and credentials. The platform automatically verifies these credentials against trusted data sources and issues digital identities without manual intervention. This self-service approach maintains verification precision while dramatically reducing onboarding time and operational overhead for both investors and platform operators.
Solution Approach 2:
The platform performs preliminary verification actions by pre-validating investor credentials and establishing digital identities before derivative creation begins. This preliminary action includes pre-checking accreditation status, investment mandates, and risk profiles, so that when investors request derivatives, the verification process is already complete or near-complete, enabling immediate derivative issuance without redundant manual checks.
4Reliability
If complex portfolios are assembled to achieve desired risk profiles, then risk management capability is improved, but accessibility and ease of operation deteriorate due to complexity barriers
Solution Approach 1:
The system changes parameters by allowing investors to directly specify risk profile parameters (risk appetite, investment horizon, sector preferences, geographic exposure) rather than requiring them to construct complex portfolios. The platform's archetype framework automatically translates these parameter specifications into optimized derivative positions that achieve the desired risk profile, making sophisticated risk management accessible to investors regardless of their portfolio construction expertise.
Solution Approach 2:
The platform serves as an intermediary that bridges the gap between simple investor risk preferences and complex derivative portfolio construction. Investors express their risk management goals in simple terms through the archetype framework, and the platform's algorithms automatically assemble the appropriate complex derivative portfolios, eliminating the need for investors to directly navigate portfolio construction complexity while maintaining sophisticated risk management capabilities.
Data Source
AI summary
The platform is provided with security, verifiability, and computational speed necessary to implement transactions involving derivatives of private equity assets. All relevant data necessary for the performance of the transactions is converted into machine-readable digital identities. References to location within the storages of the digital identities are included in a derivatives archetype associated with one derivative instrument. Price of the derivative instrument can similarly be determined using a pricing archetype that includes machine-readable references to the digital identities of data items as well as the formula necessary for the determination of the price. The processors in the platform can be combined to work in parallel in a variety of infrastructures. The methodology implemented by the platform allows to identify derivatives underlyings in the private market space, make projections for their potential future payments, dissect the types of risks associated with the potential future payments and offer various derivatives archetypes.