A data-driven framework modernizes M&A financial due diligence through automated data integration, real-time indicator monitoring, risk analysis, and information security. By connecting financial and nonfinancial data across transaction workflows, the research supports earlier risk identification, more consistent analysis, and better-informed acquisition decision-making.
— In merger and acquisition transactions, financial due diligence helps buyers understand a target company’s financial condition, operating risks, and potential obligations. In the 2025 paper “Research on Optimization of M&A Financial Due Diligence Process Based on Data Analysis,” published in the Journal of Computer, Signal, and System Research, Wei Li examines how data analysis could make financial due diligence more efficient, standardized, and responsive to risk.
The paper addresses three recurring problems in traditional due diligence work. Financial data may come from ERP systems, financial shared-service platforms, bank interfaces, tax systems, industry databases, PDFs, scanned vouchers, and other third-party sources, each with differing formats. Key financial indicators may also be monitored through periodic reports rather than real-time systems, creating delays in risk identification. At the same time, sensitive assets, profit, tax, contract, and transaction data can be exposed when transmission, storage, access permissions, and operating logs are not carefully controlled.
To respond to these issues, the research sets out a data-driven due diligence process organized around five working levels: data input, processing and calculation, risk analysis, decision support, and feedback optimization. The framework treats due diligence as a connected data flow rather than a sequence of isolated document reviews. Information is collected from multiple channels, cleaned, modeled, tested for risk, and then connected to reporting and feedback functions for later adjustment.
A central part of the framework is intelligent extraction and processing of financial data. The paper describes how systems can extract key fields from different documents and platforms, convert values into standard units, clean missing or redundant items, and standardize information from balance sheets, income statements, and cash-flow statements. It also describes outlier comparison across sources and a quality-verification function that evaluates completeness, logical consistency, and semantic accuracy before it supports later modeling.
The research connects data processing with a broader indicator system for financial due diligence. It identifies financial dimensions such as assets, liabilities, profitability, capital stock, debt-paying ability, asset-liability ratio, current ratio, gross profit margin, working-capital efficiency, EBITDA growth, and cash-conversion cycle. By adding nonfinancial information such as supply-chain stability and customer concentration, the framework is designed to support a more multidimensional view of risk than a review based only on historical statements.
For process optimization, the paper proposes three main directions. The first is a unified data interface using standardized communication protocols, field mapping, API integration, ETL scheduling, and data-quality verification to integrate internal and external sources. The second is real-time monitoring of financial indicators, with warning thresholds for changes in operating income, cash flow, accounts receivable, inventory turnover, debt, and related metrics. The third is stronger data security, including role-based access, secure transmission, data desensitization, and operation logs to protect confidential financial and tax information during a transaction.
That focus on transaction data, risk indicators, and controls connects the research to Wei Li’s M&A advisory work. Li, a CPA, is a manager in Mergers and Acquisitions Transaction Advisory Services in New York. His resume materials describe financial due diligence experience across consumer, industrials, technology, financials, and healthcare transactions, including work on quality of earnings, EBITDA, net working capital, net debt and debt-like items, valuation inputs, purchase agreement disclosures, and confidential information memoranda. He previously worked as a senior auditor and in audit-related roles, and has an academic background in economics and accounting.
By linking data integration, financial-indicator monitoring, risk scoring, and information security, Li’s research frames M&A financial due diligence as a process that can move from static document review toward more dynamic and data-supported analysis. The work points to a due diligence model in which financial professionals can use structured data systems to identify risk earlier, organize evidence more consistently, and support acquisition decisions with clearer analytical foundations.
Contact Info:
Name: Wei Li
Email: Send Email
Organization: Wei Li
Website: https://scholar.google.com/citations?hl=en&authuser=1&user=MD8svWsAAAAJ
Release ID: 89202887
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Source : Wei Li Examines Data Analysis as a Framework for M&A Financial Due Diligence
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