RPA Due Diligence: Top 8 Use Cases in 2024

Mergers and acquisitions (M&A) activity has accelerated globally, with over 64,000 deals worth more than $5 trillion inked in 2021 alone, per Refinitiv data.

However, up to 90% of these deals fail to generate returns for the acquirer.

A key reason is inadequate due diligence. Due diligence refers to the detailed investigation and audit of a target company prior to an acquisition, to uncover any hidden risks or deal breakers.

With rising M&A activity, the importance of rigorous due diligence has increased exponentially. 97% of executives surveyed by Deloitte said comprehensive due diligence could have prevented issues in previous deals.

However, traditional manual due diligence methods struggle to keep pace, given the sheer volume of data and documents involved in today‘s deals. This leads to missed red flags and failed outcomes.

This is where robotic process automation (RPA) comes in. By deploying software bots to automate repetitive manual tasks, RPA can significantly enhance due diligence.

Let‘s explore the top 8 use cases of RPA in M&A due diligence and how they deliver game-changing efficiency and insights.

1. Data Extraction and Analysis

One of the first steps in due diligence involves gathering and analyzing data from the target company to gain a comprehensive view of their finances, operations, compliance, risks, and more.

This data extraction and analysis process is often manual, fragmented, and slow – with data locked away across public records, filings, contracts, ERP systems, and various business units.

RPA helps tackle this in a few key ways:

• Accelerated data collection – RPA bots can be easily trained to log into databases and scrape data from public/private sources up to 10x faster than humans, as per McKinsey. This provides a far more extensive data set for review.
• Pattern recognition – Bots can be programmed with rules to flag inconsistencies, discrepancies, or anomalies in the data that may point to underlying issues. This helps prioritize the most critical areas for further investigation.
• Faster analysis – By structuring extracted data and inputting it into models, RPA streamlines quantitative analysis like financial ratios, growth projections, sensitivity modeling etc.

According to a KPMG study, auto-analysis of financial data can reduce due diligence timelines by 80%.

This allows deal teams to be more thorough in their review and detect risks that could have gone unnoticed previously.

2. Document Review and Verification

Due diligence involves reviewing hundreds of contracts, financial statements, emails, and other documents for accuracy, risk factors, and completeness.

Given the volumes involved, manual document review is hugely time-consuming, expensive, and prone to overlooking key information.

RPA helps by:

• Accelerated document processing – Bots can rapidly read and extract relevant data points and clauses from contracts and filings in any format. This is over 70% faster than humans.
• 100% verification – Bots can cross-verify figures, dates, names across documents to consistently detect discrepancies that humans may miss.
• Reduced costs – Automation cuts the need for large due diligence teams to manually review documents, saving [$X million] in advisor fees, as per research.

According to research from the Association for Intelligent Information Management, RPA can accelerate contract review in due diligence by over 70% compared to manual methods. This allows deal teams to thoroughly assess a far broader set of documents prior to closing a transaction.

3. Compliance Checks

Regulatory compliance is an important area of focus in due diligence, especially for deals in highly regulated sectors like financial services, healthcare, and energy.

RPA improves compliance analysis in a few key ways:

• Automated screening of the target company‘s documents, contracts, and data against constantly updated databases of global and local regulations. This quickly flags potential violations for further investigation.
• Accelerated historical analysis of past fines, breaches, and litigation to uncover systemic issues. Analysis that would take compliance teams weeks can be done in hours with RPA.
• Ongoing monitoring of new regulations and enforcement actions to proactively assess potential liabilities.
• Faster certification of the combined entity post-merger by auto-updating control documentation. This can reduce compliance timelines by over 75%.

By automating tedious compliance legwork, RPA enables deal teams to have deeper, risk-focused compliance discussions with the target company and develop robust integration plans.

4. Financial Modeling

Evaluating the target company‘s historical financial performance and projecting future combined results is crucial in due diligence.

RPA enhances financial modeling in deals by:

• Automated data consolidation – Bots can rapidly pull financial figures from multiple ERPs, plans, and systems into modeling templates. This provides a unified view.
• Accelerated analysis – Bots can automate complex valuation calculations, sensitivity analysis, scenario modeling etc. based on programmable logic.
• Automated reporting – RPA allows automatic creation of visualizations, charts, and reports for sharing insights across the deal team.

According to KPMG, RPA financial modeling reduced due diligence timelines for a private equity firm‘s acquisition by over 50%.

The improved speed and analysis helped them avoid overpaying for the asset.

5. Market Research

Due diligence should look beyond the target company‘s internal data to gather external market intelligence – on customers, competitors, industry trends, macroeconomic forces etc.

RPA enables expansive market research by:

• Automating collection of alternative data from public sources – social media, reviews, job postings etc.
• Web scraping to monitor competitors‘ product launches, pricing changes, hirings etc.
• Sentiment analysis of customer feedback on social media and forums to detect early warning signs of reputation risk.

These insights help create a 360-degree view of the target company‘s competitive positioning and projected growth within its market context – uncovering risks that warrant further investigation.

6. HR Analytics

Assessing the target company‘s workforce and culture is imperative during due diligence to evaluate integration readiness, retention risks, liabilities etc.

RPA enables holistic HR analysis by:

• Extracting employee data from HR systems – turnover, performance, compensation, demographics etc.
• Auto-analyzing trends – common reasons for attrition, diversity metrics, pay equity issues etc.
• Scanning glassdoor and social media to uncover red flags around work culture, grievances, or discrimination.

These talent insights help acquirers design effective workforce integration strategies, minimize turnover, and avoid risks associated with human capital issues.

7. Vendor Analysis

Third-party vendors and partners are an important part of most companies‘ business ecosystem.

Rapid analysis of the target company‘s vendors and contracts helps identify:

• Over-dependence risks if a few major vendors account for a large % of spend.
• Unfavorable contract terms and short notice periods.
• Compliance risks associated with key vendors.
• New partner opportunities post-acquisition.

RPA allows comprehensive analysis of thousands of vendor-related documents – accelerating risk detection and integration planning.

8. IP Analysis

Analyzing the target company‘s intellectual property (IP) and rights is vital to assess value, ensure compliance, and reduce infringement risks.

Key RPA applications include:

• Automated extraction of patent data – ownership history, expiration dates, jurisdictions covered etc.
• Rapidly comparing trademarks and patents to public databases to identify conflicts.
• Ongoing IP competitive monitoring – for R&D overlaps, new patent applications etc.

By handling the volume-intensive aspects of IP analysis, RPA helps deal teams focus on high-level IP strategy and risk mitigation.

While RPA offers immense efficiency through automation, integrating it with AI-based technologies can unlock further advanced capabilities:

• Natural language processing (NLP) can extract insights from textual documents that are difficult to analyze manually.
• Machine learning algorithms can be trained to spot non-obvious patterns and anomalies indicating areas of risk.
• Computer vision can automate analysis of images, video, and other unstructured data sources.
• OCR and ICR can digitize scanned documents and handwritten notes for easier processing by bots.

By combining RPA with AI, due diligence functions can evolve from tactical process improvement to predictive risk identification. Leading acquirers are already pursuing RPA+AI integration.

However, there are some risks acquirers should be aware of when implementing RPA for due diligence:

Bot errors incorrectly processing data can provide misleading insights unless sufficient testing and logic checks are implemented.

IT security risks of bots accessing sensitive data should be evaluated and controlled.

Over-reliance on technology can lead to overlooking soft risks not captured in data. Combining RPA with human oversight is important.

Proactive risk management is key to maximize the benefits of RPA while minimizing downsides.

As M&A activity scales new highs globally, acquirers need to digitally transform due diligence to support deal success.

RPA is a powerful starting point, providing real-time access to data, faster document review, 100% verifiability, and data-driven insights.

To learn more about implementing RPA in your organization, please reach out to our experts. We can guide you through building a pilot automation program, selecting the right tools, training staff, and scaling RPA across critical processes like due diligence.

The future of deals is automated. Let‘s discuss how to start your RPA journey today!