PPA Benchmarking Analytics with Automated Filing Data Validation
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Solution Overview
Problem
Current systems for purchase price allocation (PPA) in business acquisitions lack standardization, leading to manual, error-prone, and inefficient data retrieval and analysis, with inconsistent data interpretation across projects and teams, and a lack of comprehensive analytics.
Innovation Solution
A computer-implemented system with a data collection interface, centralized database, and processor for collecting, normalizing, and validating transaction data from disparate sources, using mapping and matching algorithms to generate interactive dashboards for enhanced analytics, incorporating Generative Artificial Intelligence (Gen AI) for data retrieval and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data retrieval and analysis is used, then flexibility in data selection is maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The system enables self-service through automated data collection where the platform automatically retrieves, normalizes, and validates PPA data from multiple sources without requiring manual intervention. The automated workflow includes automatic data extraction from SEC filings, XBRL data, and financial research platforms, eliminating manual retrieval operations while maintaining data accuracy through built-in validation algorithms.
Solution Approach 2:
The patent replaces manual mechanical data collection processes with automated computer-based systems. The mechanical action of manually searching, copying, and pasting data from various sources is substituted by automated software that programmatically extracts data from SEC filings, XBRL databases, and financial research platforms, significantly reducing time consumption and human error.
2Measurement precision
If standardized data collection is implemented, then data consistency and accuracy improve, but system complexity increases
Solution Approach 1:
The system introduces a centralized database as an intermediary layer between multiple disparate data sources and the user interface. This intermediary normalizes and standardizes data from various formats (SEC filings, XBRL, financial research platforms) into a unified structure, enabling consistent data collection and validation while abstracting the complexity of multiple source formats from the user.
Solution Approach 2:
The platform achieves universality by consolidating multiple data collection functions into a single integrated system. The same infrastructure handles data retrieval from SEC filings, XBRL data, and financial research platforms, along with normalization, validation, and visualization functions, reducing overall system complexity compared to maintaining separate specialized systems.
3Productivity
If automated data collection is implemented, then time efficiency improves, but data validation and quality control become more challenging
Solution Approach 1:
The system implements feedback mechanisms through automated validation algorithms that continuously monitor data quality during collection. The validation process provides immediate feedback on data accuracy, completeness, and consistency, allowing the system to correct errors and maintain high data quality standards while operating at automated speed. Users receive feedback on data validation status and can review or correct automated entries.
Data Source
AI summary
The invention relates to computer-implemented systems and methods for implementing an Intangibles Analytics Tool that may be used to benchmark purchase price allocation (PPA) data from public filings. The Intangibles Analytics Tool may include the ability to: (i) automatically retrieve acquisition disclosure information and provide initial PPA mapping, replacing the need to perform these steps manually and (ii) serve as a database of verified public PPA data that may be leveraged across offices, applications, industries, etc. With the Intangibles Analytics Tool, users may perform transaction searches and automatically retrieve filings associated with the buyers of those transactions.