How to Use AI for Due Diligence: Streamlining Legal and Financial Reviews
Due diligence is a core part of mergers, acquisitions, investments, lending, and client onboarding. It involves reviewing documents and data to confirm facts, assess risk, and check compliance. Traditionally, this work has been slow, manual, and document-heavy.
AI is changing that. For lawyers, financial professionals, and business teams, understanding how to use AI for due diligence is increasingly important. The right tools can speed up review, improve consistency, and help teams focus on higher-value judgment calls.
Why AI Matters in Due Diligence
Due diligence often involves large volumes of contracts, financial statements, regulatory filings, internal records, and communications. Manual review can be time-consuming and prone to missed issues, especially when teams are working under tight deadlines.
AI can help in several practical ways:
- Faster review: AI can analyze large document sets much more quickly than manual review alone.
- More consistent analysis: It helps reduce variation caused by fatigue or subjective interpretation.
- Better risk spotting: AI can flag unusual clauses, compliance concerns, financial irregularities, and other red flags.
- Lower review costs: Automating repetitive tasks can reduce the time and effort required for the process.
- Deeper insights: AI can identify patterns and connections across documents that may be difficult to spot manually.
- Easier scaling: It can handle larger data sets without requiring a proportional increase in staff.
Used well, AI does not replace legal or financial judgment. It supports it, allowing professionals to spend more time on risk assessment, negotiation, and strategic advice.
Best AI Tools for Due Diligence
The best tool depends on the type of review you need to perform. Some platforms are built for contract analysis, while others are stronger in eDiscovery, compliance, or broader document review.
Kira Systems (now part of Litera)
Kira is a contract analysis platform that uses machine learning to extract and review key provisions from legal documents. It includes pre-built models and supports custom models for specific review needs.
Why it is useful: Kira is especially effective for M&A due diligence. It can quickly identify clauses such as change of control, assignment, termination, and indemnification across large contract sets.
Best fit: M&A, real estate transactions, and other projects requiring fast review of large numbers of contracts.
Pros:
- Strong accuracy
- Robust pre-built models
- Good for identifying specific clauses
- Strong legal document focus
Cons:
- Can be expensive
- More focused on document review than financial analysis
- May require training to build custom models
DISCO AI
DISCO AI is a cloud-native eDiscovery platform that includes AI features for document review, including due diligence. Its capabilities include Technology Assisted Review, clustering, and concept search.
Why it is useful: It helps legal teams sort through large volumes of unstructured data and surface relevant contracts, communications, and records more quickly.
Best fit: Complex investigations, litigation-related due diligence, and reviews involving large amounts of unstructured data.
Pros:
- Strong for large-scale data analysis
- Built on an established eDiscovery workflow
- Useful clustering and search features
- Integrates well with legal review processes
Cons:
- Primarily an eDiscovery platform
- Less specialized for contract analysis than some dedicated tools
Relativity
Relativity is another major eDiscovery and review platform with AI and machine learning features. It offers Technology Assisted Review, clustering, conceptual search, and Active Learning.
Why it is useful: Relativity can help prioritize documents, categorize data, and surface issues related to compliance, litigation, or financial risk.
Best fit: Large due diligence projects, regulatory reviews, and matters involving extensive documentation or litigation history.
Pros:
- Highly scalable
- Flexible and customizable
- Widely used in the legal industry
- Strong AI and review capabilities
Cons:
- Can be complex to set up
- May require specialized expertise
- Pricing can be a factor for smaller matters
Luminance
Luminance is an AI platform designed for legal document review, with a strong emphasis on due diligence. It reads and analyzes legal documents to identify key clauses, deviations from standard terms, and potential risks.
Why it is useful: Luminance is particularly strong at spotting anomalies and comparing contracts against a standard or template, making it useful for compliance checks.
Best fit: M&A, real estate, and contract portfolio review where the goal is to identify non-standard terms and contractual risk.
Pros:
- Strong contract analysis
- Good at flagging deviations
- User-friendly interface
- Supports multiple languages
Cons:
- Focused mainly on contract review
- May need integration for broader financial analysis
- Pricing can vary
Ideagen Audit+
Ideagen Audit+ is not a pure due diligence tool, but it can support compliance and audit-related review. It helps manage audit processes, track findings, and review regulatory requirements.
Why it is useful: It can be helpful for regulatory due diligence and for assessing internal controls, audit reports, and compliance documentation.
Best fit: Reviews focused on compliance, internal control environments, and operational risk.
Pros:
- Strong audit and compliance focus
- Useful for structured workflow management
- Helpful for internal risk assessment
Cons:
- Less specialized for contract analysis
- AI features are more audit-oriented than general document review
Eigen Technologies
Eigen Technologies uses natural language processing and machine learning to extract and analyze information from unstructured documents. It can work across contracts, financial statements, and other legal materials.
Why it is useful: Eigen can extract terms, dates, figures, and other key data points that support financial analysis, covenant review, and liability identification.
Best fit: M&A, lending, and regulatory compliance matters where structured data needs to be pulled from large document sets.
Pros:
- Flexible across document types
- Strong NLP capabilities
- Good for complex data extraction
- Scalable
Cons:
- Requires setup and training for specific use cases
- Can be a significant investment
- May need to be integrated with other systems
ContractPodAi
ContractPodAi is a contract lifecycle management platform with AI-powered review and analysis features. It can identify clauses, obligations, risks, and deviations in contract portfolios.
Why it is useful: It helps legal teams review agreements more quickly and spot issues that may otherwise be missed in large contract sets.
Best fit: M&A, investment diligence, and ongoing contract-based compliance review.
Pros:
- Integrated CLM platform
- Strong contract review features
- User-friendly
- Broad functionality beyond due diligence
Cons:
- Primarily contract-focused
- Not a full standalone solution for financial due diligence
How to Choose the Right AI Tool for Due Diligence
The best tool depends on your goals, document types, team structure, and budget. Before selecting a platform, consider the following:
- Primary objective: Are you reviewing contracts, analyzing unstructured data, extracting financial information, or assessing compliance?
- Document types: Make sure the tool can handle the records you actually use, such as contracts, emails, filings, or financial statements.
- Data volume: Large matters require tools that can ingest and process data at scale.
- Integration needs: Check whether the platform works with your document management, CLM, or financial systems.
- User experience: Some tools are easier to learn and deploy than others.
- Budget: Pricing can vary significantly depending on features, users, and usage.
- Customization: Some matters are best served by pre-built models, while others require configurable workflows.
A pilot using sample data is often the best way to evaluate performance before committing to a full rollout.
Pricing and Value Considerations
Pricing for AI due diligence tools varies widely. Common pricing factors include:
- Number of users
- Amount of data processed
- Features and modules included
- Project-based or enterprise licensing
- Implementation, training, and storage costs
When evaluating cost, consider the full value of the tool, not just the subscription price. Faster review, fewer missed issues, and more efficient transactions can create significant return on investment. In some cases, the cost of a tool is justified by avoiding even one material oversight.
Frequently Asked Questions About AI for Due Diligence
Can AI completely replace human reviewers in due diligence?
No. AI can automate many parts of the process, but human judgment remains essential. Lawyers and analysts still need to interpret findings, assess context, and advise clients.
What kind of data can AI analyze for due diligence?
AI can analyze contracts, financial statements, emails, corporate filings, regulatory documents, intellectual property records, and meeting or call transcripts, depending on the platform.
How accurate are AI tools for due diligence?
Accuracy varies by tool, data quality, and task complexity. Many systems perform well on repetitive review tasks, but important findings should still be validated by a human reviewer.
Is AI for due diligence cost-effective for smaller firms or deals?
It can be. While some enterprise tools are expensive, others offer tiered pricing, modular features, or cloud-based access that may fit smaller matters.
How do I protect sensitive data when using AI for due diligence?
Choose vendors with strong security controls, including encryption, access management, audit logs, and compliance with relevant data protection requirements. Vendor review is essential before sharing sensitive information.
What is the learning curve for AI due diligence tools?
It depends on the platform. Some tools are intuitive and quick to adopt, while others require training, configuration, or model customization.
Conclusion
AI is reshaping due diligence by making document review faster, more consistent, and more scalable. For legal and financial professionals, the goal is not to replace judgment but to improve the efficiency and quality of review.
If you are evaluating how to use AI for due diligence, start by identifying the type of work you need to support, then compare tools based on document coverage, workflow fit, integration needs, and cost. With the right platform, AI can help reduce manual effort, surface risks earlier, and support better decision-making across the due diligence process.