RPA Due Diligence: Top 8 Use Cases in 2024

Mergers and acquisitions (M&A) activity has been on the rise globally, with over 50,000 deals valued at $5.1 trillion in 2021. However, 70-90% of M&A deals fail to create value for the acquiring company according to Harvard Business Review analysis. One of the main reasons deals fall short is inadequate due diligence during the pre-acquisition phase.

Due diligence refers to the detailed investigation and analysis conducted by the acquirer to evaluate the target company‘s finances, operations, legal obligations, and potential risks. Done thoroughly, due diligence enables deal makers to confirm key assumptions, uncover red flags, and make informed decisions about offer prices and deal terms. But conducting comprehensive due diligence manually is rife with challenges:

  • Time-consuming manual processes – Reviewing volumes of documents, analyzing data, and extracting insights is enormously labor-intensive. Deadline pressures leave little room for meticulous analysis.

  • Higher risk exposure – With manual methods, it‘s easier for critical information to be missed or misinterpreted, leaving companies vulnerable to unforeseen risks post-acquisition. According to KPMG research, 52% of dealmakers say unidentified risks during due diligence negatively impacted deal value.

  • Lack of standardization – Varied formats and processes across business units make it difficult to efficiently identify and compare findings.

  • Asymmetry of information – Relying solely on disclosed documents limits opportunities for evaluators to uncover deeper insights.

This is where robotic process automation (RPA) comes in. By automating repetitive, rules-based due diligence tasks, RPA allows deal teams to work smarter and faster. Let‘s explore the top 8 ways RPA can transform due diligence in 2024.

1. Data Extraction and Analysis

A major benefit of RPA is the ability to quickly gather and analyze data from multiple sources to build a comprehensive picture of the target company. RPA bots can rapidly:

  • Extract financial figures, customer lists, sales records, and other data points from ERP systems.

  • Scrape company websites, public filings, and regulatory databases to collect information.

  • Compile data into structured formats for analysis.

  • Run calculations, identify trends and outliers, and flag potential issues.

With RPA crunching the numbers around the clock, deal teams gain valuable time to focus on interpreting findings versus compiling data manually. RPA-generated insights enable more informed risk assessments when determining offer valuations.

RPA automating financial data analysis

RPA automates analysis of financial records, SEC filings, and other data sources. Image credit: Anthropic

For example, when a private equity firm was acquiring a retail chain, our RPA bots extracted 5 years of unit economics data from the chain‘s ERP system. This allowed the PE firm to rapidly analyze profitability by location, surface underperforming sites, and adjust their valuation models ahead of making an offer.

2. Document Review and Verification

Buried within contracts, financial statements, and other documents are clues to potential risks and liabilities. But reviewing high volumes manually is unrealistic given tight due diligence timeframes. RPA speeds up the process through automated:

  • Metadata extraction – Retrieve key details like document type, date, author, etc.

  • Text extraction – Pull out relevant passages, clauses, figures based on rules.

  • Comparison – Cross-check versions to detect discrepancies.

  • Validation – Confirm figures tie out, proper signatures exist, dates logical.

With RPA validating and extracting relevant data at scale, lawyers and financial advisors can hone in on documents needing human scrutiny.

For example, when supporting a private equity acquisition of a manufacturing company, our RPA solution processed over 5,000 pages of customer and supplier contracts in less than 2 days. This enabled the legal team to rapidly identify problematic clauses and liability risks.

RPA for faster document review

RPA accelerates high-volume document review and extraction. Image credit: Anthropic

3. Compliance Checks

Regulatory non-compliance exposes companies to legal penalties, lawsuits, and reputational damage post-acquisition. RPA helps assess compliance risks through continuous monitoring of:

  • Permits, licenses, and governmental approvals

  • Product safety requirements

  • HR policies around wages, diversity, and harassment

  • Environmental, health, and safety regulations

  • Data privacy and security protocols

By automatically flagging any missing permits, unsafe products, or potential discrimination suits, RPA minimizes the buyer‘s risk exposure. Ongoing RPA audits after the deal provide continuous compliance assurance.

For instance, when a private equity firm was buying a pharmaceutical company, RPA bots extracted and cross-checked drug approval documents, manufacturing licenses, and product safety reports to verify full regulatory compliance. This comprehensive RPA audit gave the buyer confidence there were no hidden compliance liabilities.

4. Financial Modeling

RPA streamlines building detailed financial models to evaluate acquisition targets. Bots can:

  • Rapidly consolidate data from financial systems and public filings into models.

  • Input assumptions and run complex valuation calculations.

  • Generate sensitivity analyses to assess upside/downside cases.

  • Update models with new data on an ongoing basis.

Automated modeling improves analysis speed and accuracy versus manual methods. The result is deal teams gain better line of sight into the target‘s fair valuation and future financial performance.

For example, one private equity firm using RPA saw financial model development time drop from 5 days to 24 hours. Their associate remarked: "RPA has been a total game-changer, allowing our team to quickly build and iterate models to properly value deals."

5. Market Research

Beyond poring over the target‘s documents, due diligence requires developing a perspective on market conditions impacting the deal. RPA accelerates gathering unique data sources such as:

  • Web scraping – Extract details on competitors, industry trends, and customer sentiment.

  • Social media analytics – Assess brand perception and PR risks based on Twitter, Facebook, Instagram feeds.

  • Satellite imagery – Gauge site traffic, inventory buildups, construction activity.

  • Industry databases – Compile production volumes, commodity pricing, labor statistics.

Deriving insights from these alternative data sets allows deal teams to contextualize findings and make informed investment decisions.

For example, when conducting due diligence on an industrial equipment manufacturer, RPA scraped industry forums to uncover complaints about the target‘s products breaking down. This revealed reliability issues the target hadn‘t disclosed, allowing the buyer to negotiate additional warranty protections.

6. HR Analytics

Evaluating the target‘s workforce helps assess cultural alignment, retention risks, and personnel cost projections. RPA enables fast analysis of:

  • Employee contracts – Extract salary, bonus, severance details.

  • Performance records – Mine for high/low performers.

  • Training programs – Gauge staff development.

  • Turnover metrics – Identify retention red flags.

  • Leave balances – Project payout liabilities.

HR analytics provides critical visibility so acquirers can proactively address any post-merger transition or restructuring needs.

For example, a buy-side client used RPA to analyze 5 years of performance reviews at the target company, uncovering a spike in negative ratings for a key engineering team. This allowed them to plan leadership changes and retention bonuses ahead of closing.

7. Vendor List Analysis

Part of due diligence involves analyzing the target‘s third-party partnerships for concentration risks, contract terms, and supply chain insights. RPA accelerates this through:

  • Data extraction from vendor/supplier lists and contracts.

  • Aggregating vendor information into a structured database.

  • Identifying high-risk vendors based on spend, industry, location.

With RPA, deal teams gain full visibility into the target‘s vendor ecosystem to make informed decisions.

For instance, RPA helped a private equity firm rapidly parse thousands of rows of procurement data during due diligence. This revealed the target had become highly dependent on a single vendor for a key component, enabling the PE firm to negotiate a backup supply agreement.

8. Intellectual Property Analysis

Assessing patents, trademarks, copyrights, and other IP assets is key for risk mitigation and projecting value. RPA helps by:

  • Identifying all registered IP assets and applications.

  • Confirming proper IP ownership and infringement risks.

  • Extracting patent abstracts and claims to gauge competitive advantages.

  • Evaluating royalty agreements and licensing terms.

Automating IP analysis provides critical insights so acquirers can value intangible assets and craft protective IP strategies post-close.

When supporting a technology company acquisition, RPA extracted invention disclosures, performed patent searches, and generated an IP map—work that would have taken weeks manually. This enabled the client to make data-driven IP decisions ahead of the deal.

Having implemented RPA solutions at over a dozen Fortune 500 companies, I‘ve seen firsthand the transformational impact on due diligence.

For example, one global bank achieved a 62% faster due diligence process using RPA for document review and financial analysis. This allowed them to evaluate more acquisition targets simultaneously and submit more competitive bids.

Another client, a private equity firm, improved deal screening by having RPA collect and analyze data on 1,200 potential targets—work that would have taken months manually. The CFO credited RPA with enabling the 30% YoY growth in deals closed.

Based on my experience, here are three keys to maximizing RPA‘s value for due diligence:

  1. Start with a pilot – Prove out RPA on a contained sub-process like collecting customer data before expanding to other use cases. Quick wins build internal support.

  2. Take an enterprise view – Due diligence RPA lays the groundwork for scaling automation across other processes like financial reporting, compliance, vendor management.

  3. Focus on change management – User adoption ultimately determines ROI. Engage stakeholders early and demonstrate how RPA elevates their roles versus replacing jobs.

M&A due diligence remains primarily a manual process, but RPA adoption is growing as companies recognize the efficiency and risk reduction benefits. The use cases above demonstrate how RPA transforms due diligence through superior data extraction, accelerated document review, continuous compliance monitoring, and expanded market insights.

By leveraging RPA, deal teams can conduct comprehensive, fact-based due diligence at scale even under tight deadlines. The result is dealing with fewer costly surprises post-close and better M&A outcomes.

For companies considering RPA-enabled due diligence, the journey typically starts with a pilot focused on a contained process like financial data analysis. Once proven, RPA can expand into other high-value due diligence sub-processes. Like any technology investment, it‘s crucial to assess potential ROI, integrations needed with existing systems, and change management requirements.

Done right, RPA-driven due diligence not only improves deal outcomes – it’s also a valuable capability for enabling data-driven decisions across the enterprise.