Due diligence is a critical process in major business deals and transactions. But conducting comprehensive due diligence manually is rife with challenges. In this blog post, we‘ll explore how automating due diligence can transform the process and deliver significant benefits in 2024 and beyond.
What is Due Diligence and Why Does it Matter?
Due diligence refers to the investigative activities undertaken by a company to assess and validate the details of a business transaction or deal prior to completion. This includes thoroughly reviewing financial records, operations, legal obligations, and more of the other party.
According to McKinsey, inadequate due diligence is a major reason 40% of mergers and acquisitions (M&A) fail. Without proper due diligence, companies take on unacceptable levels of risk, overpay, or make deals that fail to create value.
For private equity firms, VCs, and other investors, due diligence is absolutely essential to avoid costly mistakes and validate investment assumptions. The risks of cutting corners are enormous. A 2020 survey of institutional investors by PitchBook found that 95% see due diligence as highly important when evaluating potential investments.
The Challenges of Manual Due Diligence
Traditionally, due diligence has been an entirely manual process. Teams of analysts and experts dive into mountains of documents, financial records, market data, and more to assess a potential deal.
This manual approach has several drawbacks:
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It‘s time consuming. Comprehensive due diligence for an M&A deal can take 30-60 days for analysts to complete. According to a survey of private equity investors, over 50% said due diligence takes at least 1-2 months per deal.
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It‘s inefficient. Human analysts get fatigued reviewing hundreds of pages of contracts and financials. Important details can be missed. Per an EY report, analysts spend 80% of their time simply gathering and organizing data from various sources.
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It‘s prone to error. Without automation, human oversight and misjudgments can allow risks and red flags to slip through. Studies suggest human error in due diligence contributes to up to 30% of M&A failures.
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It‘s difficult to coordinate. With various teams involved like legal, compliance, and finance, keeping everyone on the same page is challenging. This lack of transparency between silos can hamper due diligence.
According to a survey from Datasite, 56% of dealmakers say lack of standardization across workflows makes collaboration difficult.
Cost of Inadequate Due Diligence
When due diligence falls short, deals can turn into expensive disasters. Here are some examples:
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HP‘s $11B Autonomy blunder – In 2011, HP bought enterprise software firm Autonomy for $11 billion. Just one year later, HP took an $8.8 billion write-down after discovering Autonomy misrepresented financials and growth prior to acquisition.
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ABS-CBN/Boto Mo Investment – Philippine company ABS-CBN made a $10 million investment into Boto Mo, a US-based startup. But a lack of due diligence failed to uncover Boto Mo was actually a scam artist using fake executives and shell companies.
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Sears/Kmart merger – In 2004, Sears acquired Kmart for $11 billion in hopes of revival. But issues like brand identity and culture mismatches, overlooked during due diligence, led to Sears ultimately filing for bankruptcy in 2018.
These examples showcase how critical comprehensive due diligence is for minimizing risks and avoiding massive loss in value.
What is Due Diligence Automation?
Due diligence automation uses software tools and technologies like artificial intelligence to digitize, streamline, and enhance the due diligence process. The goal is to extract key insights from documents faster, reduce human error, and create transparency across stakeholders.
According to a survey from PitchBook, 61% of institutional investors already use some form of automation for due diligence activities, while 86% expect to increase automation use over the next 2 years.
Some of the key technologies enabling due diligence automation include:
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OCR (optical character recognition) – Scans documents and extracts text, numbers, tables, and other important data.
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NLP (natural language processing) – "Reads" unstructured text documents and extracts context and insights.
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RPA (robotic process automation) – Automates repetitive manual tasks involved in due diligence.
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Web scraping – Harvesting publicly available web data on companies, key personnel, etc.
These technologies power end-to-end due diligence automation solutions that can tackle the entire due diligence workflow – from data collection to analysis and risk assessments. Top solutions include:
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Ankura – Offers AI-enhanced due diligence leveraging NLP and intelligent analytics.
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Kira Systems – Analyzes contracts and documents using advanced machine learning.
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eBrevia – Due diligence platform powered by NLP for faster contract review.
9 Benefits of Due Diligence Automation
Now let‘s explore some of the major ways due diligence automation transforms the process for the better.
1. Identify Risks More Accurately
The scale of documents involved in due diligence for major transactions can be vast. Financial records alone can range from 120-180 pages on average for publicly traded companies.
Automated tools have the capacity and endurance to analyze far more documents with greater accuracy. Key risk indicators and red flags are less likely to be missed.
According to McKinsey, AI-driven due diligence can improve risk identification by 15-25%. That could make a major difference in deal outcomes.
Kira Systems analysis of commercial contracts found AI identifies 56% more risks and red flags than human reviewers. Automation delivers a huge boost to accuracy.
2. Ensure Compliance
Automated due diligence solutions can be programmed to check for regulatory requirements. They ensure investments comply with all applicable rules and standards – for example, checking fund mandates for asset managers.
This helps minimize compliance failures that could put companies in legal jeopardy. Research from Deloitte suggests process automation can reduce instances of non-compliance by up to 90%.
3. Save Valuable Time
Automating tedious manual document review tasks can drastically reduce the time required for due diligence.
Whereas manual processes take 30-60 days typically, automated due diligence may cut this timeframe down to just 1-2 weeks – up to a 75% reduction. Those time savings allow deals to progress faster.
Powered by AI tools, Ankura‘s automated due diligence offering achieved 60-70% faster turnaround times compared to traditional methods.
4. Improve Team Communication
Collaborating across siloed teams like legal, compliance, and finance is a constant pain point in manual due diligence.
Automation platforms provide transparency into due diligence progress and findings. All parties can get on the same page faster, streamlining workflows.
According to a survey from PitchBook, 87% of investors feel increased automation will significantly improve cross-team collaboration in due diligence.
5. Reduce Post-Acquisition Surprises
Thorough due diligence minimizes the risk of expensive post-acquisition surprises. But human oversights still happen.
For example, HP‘s disastrous $11 billion purchase of Autonomy in 2011. HP later had to write off $8.8 billion in value due to accounting irregularities it failed to catch.
Automated tools help spot and flag such issues early, before commitments are made. An IBM study found AI due diligence can reduce post-merger restatements by up to 80%.
6. Increase Information Reach
Automation amplifies the power of data collection in due diligence. Solutions can integrate and cross-reference both internal and external data sources to build a complete picture.
Web scraping tools source data from public websites on companies, executives, assets, etc. Natural language processing analyzes and extracts insights from vast document sets.
By expanding information reach, hidden risks are uncovered. One academic study of 1,700 private equity transactions over a decade found use of Automated Web Scraping increased adverse finding rates by 42% compared to other methods.
7. Help Determine Fair Valuations
Flawed valuation is a huge source of M&A failure. The right data science and modeling techniques applied during due diligence can significantly improve valuation accuracy.
According to a KPMG survey, 61% of CEOs feel M&A deals happen at too high a price. Quality data and analytics prevent overpayment.
Research suggests AI due diligence reduces valuation adjustments post-merger by 19-31%, indicating more accurate initial valuations.
8. Remove Human Biases
Experts have biases. But software does not. Automating analysis of due diligence materials with AI can deliver impartiality.
For example, natural language processing tools can scan contracts without preconceived notions. This results in valuations and risk profiles free of human bias.
According to an EY survey, 73% of institutional investors believe AI improves objectivity during due diligence by removing emotional biases.
9. Leave No Stone Unturned
Technology allows due diligence to be more exhaustive than humanly possible. Automated tools run through checklists and data sources 24/7 without tiring.
Rather than just sampling data, solutions can perform deep analysis on 100% of available information. Critical oversights become far less likely.
Studies show AI is between 7-15x faster at reviewing business contracts than lawyers or paralegals, with equal or greater accuracy – allowing full document sets to be analyzed versus just samples.
Overcoming Due Diligence Automation Challenges
Implementing due diligence automation comes with common growing pains. But these can be overcome with the right strategy:
Integrating Systems – Combining tools like OCR, NLP, and RPA with in-house data sources may require custom integration work. Allocating resources here pays off long-term.
Change Management – Transitioning staff to incorporate new technologies requires training and internal advocacy. Clear communication of benefits is key.
Building Trust – Some may distrust insights from "black box" algorithms. Being transparent on AI limitations builds confidence.
Legacy Processes – Don‘t just digitize current workflows. Reimagine processes to fully benefit from automation capabilities.
With the right expertise and partners, these hurdles can be cleared to make due diligence automation a smooth reality.
Making Due Diligence Automation a Reality
Hopefully this gives a sense of the tremendous potential for transforming due diligence using the latest AI capabilities. But how can companies get started putting this into practice? Here are a few key recommendations:
Evaluate Opportunities – Determine high-value starting points for automation based on current pain points. Common options include contractual review, financial analysis, background checks, and regulatory compliance checks.
Start Small, Then Scale – Pilot automation on a small subset of due diligence before expanding. Measure results, iterate, and gather feedback from staff.
Review Internal Processes – Assess current workflows to determine where automation can boost efficiency the most. Don‘t just digitize – reinvent processes.
Combine The Right Tools – Blend individual point solutions like OCR and NLP with end-to-end platforms to maximize impact. Integrations may take work.
Train and Support Staff – Implement change management strategies as technologies are rolled out. Upskill employees to augment skills rather than replace them.
With the right strategy and technology partners, implementing due diligence automation is within reach for companies of all sizes seeking an edge. Reap the rewards in 2024.
More Process Automation Use Cases to Explore
To learn more about additional applications of intelligent process automation, see: