Centralized Due Diligence Platform Using Machine Learning and Distributed Ledger
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Solution Overview
Problem
Current due diligence processes lack centralization, accuracy, and security, requiring significant time and manual resources, and fail to provide a mechanism for facilitating risk reduction at both individual and transactional levels, while also inadequately protecting sensitive data during assessments.
Innovation Solution
A system utilizing a centralized platform with machine learning techniques to assess risk, integrate data from multiple sources, and secure sensitive information using a distributed ledger, enabling secure data sharing and risk analysis between buyers and sellers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a centralized platform with machine learning is implemented, then measurement precision and reliability of risk assessment are improved, but device complexity increases
Solution Approach 1:
The patent introduces a centralized due diligence platform as an intermediary system that mediates between multiple data sources (interviews, documents, cyber security assessments) and the risk assessment process. This platform uses machine learning models to process and analyze data, providing objective risk calculations that resolve the contradiction by centralizing complexity in a dedicated system rather than分散 across multiple manual processes
Solution Approach 2:
The patent replaces manual, subjective due diligence processes with automated machine learning-based risk assessment systems. The machine learning models objectively quantify risk factors by analyzing data from multiple sources, substituting human judgment with algorithmic processing to improve measurement precision while managing system complexity through automation
2Adaptability or versatility
If manual data collection and interviews are conducted, then adaptability to specific buyer-seller contexts is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent implements preliminary automated data collection and analysis capabilities that prepare risk assessments before formal due diligence begins. The system pre-processes available information, identifies key risk factors, and prepares initial assessments, allowing the process to start from an advanced state rather than beginning manual collection from scratch
Solution Approach 2:
The patent enables continuous risk assessment processing through the centralized platform that operates throughout the due diligence process. Rather than discrete manual interviews, the system continuously analyzes data from multiple sources including cyber security assessments, financial documents, and operational data, maintaining continuous useful action to improve productivity while adapting to transaction specifics
3Ease of operation
If virtual data rooms are used for document storage, then ease of operation for document sharing is improved, but security and protection of sensitive information worsen
Solution Approach 1:
The patent implements differentiated access controls and security measures tailored to specific documents, data types, and user roles within the virtual data room. Different security protocols and access restrictions are applied locally to different sections of the data room based on sensitivity requirements, allowing easy operation for authorized users while maintaining strong security for sensitive information through localized quality controls
4Reliability
If multiple subject matter experts and cybersecurity personnel are involved, then reliability and measurement precision of assessment are improved, but device complexity and loss of time increase
Solution Approach 1:
The patent merges multiple assessment functions and expert contributions into a single centralized due diligence platform. The system integrates cyber security assessments, financial analysis, operational due diligence, and other specialized evaluations into one unified system that coordinates all experts and data sources, improving reliability through comprehensive assessment while reducing coordination complexity through centralization
Data Source
AI summary
Systems and method for optimizing due diligence are disclosed. The system includes a server designed to generate a centralized platform and configured to receive an initial health assessment landscape pertaining to a subscriber on a centralized platform operated by the server. The plurality of health assessment data is extracted from the initial health assessment landscape. The system generates a risk assessment score associated with the subscriber. The system further includes a communication module configured to host communicative sessions over the centralized platform in a manner that securitizes confidential or sensitive data subject to transactions occurring over the centralized platform.