Artificial intelligence is becoming part of everyday work in restructuring and bankruptcy, in areas such as organizing messy data, reviewing large volumes of information, and trying to make sense of it all under pressure.

This integration is already affecting how financial advisors and legal teams approach cases and raises some practical questions. How much can teams confidently rely on these tools? Which tasks and activities should continue to be performed manually? What guardrails need to be in place?

These questions apply across the restructuring and bankruptcy landscape, where professionals routinely manage large volumes of financial, legal, and operational information under tight deadlines. While the use cases are broad, they become especially relevant in matters involving potential fraud, where the stakes are often higher and the facts more complex.

Using bankruptcy data compiled from Chapter 7 and Chapter 11 filings, known fraud-related cases remain a relatively small share of the overall filing landscape: 59 out of 991 cases reviewed, or about 6%, across 2024 through 2026 year-to-date for matters with more than $10 million in assets or liabilities at filing. These matters often involve extensive records, complex fact patterns, and heightened scrutiny, making them a useful example of where AI tools may help professionals work through large volumes of information more efficiently.

Whether dealing with bankruptcy or fraud cases, they are often exactly the kinds of matters where AI can make a difference: they are document-heavy, fact-intensive, and require teams to sort through large volumes of financial and operational data quickly.

As a result, this provides a useful lens for examining both the opportunities and limitations of AI in restructuring and bankruptcy work more broadly.

Getting ahead of the filing: The value of earlier insight

One of the more interesting uses of AI is its potential to help identify companies that may need to file for bankruptcy. Instead of waiting for a company to enter Chapter 11, teams can use AI to monitor markets and industries for early warning signs, allowing them to recognize potential distress sooner.

As a result, in venues where complex cases tend to cluster, faster organization of claims, transactions, and communications can help advisors and legal teams accelerate the early stages of a matter. The benefit is not simply faster task completion; it is the ability to understand the facts sooner, identify issues earlier, and respond more effectively.

Looking ahead, firms will likely look beyond efficiency gains alone. The larger question is whether AI-assisted workflows will lead to better outcomes. Will they contribute to improved recoveries, stronger investigations, or more informed decision-making? That answer is still developing, but it is increasingly becoming a central focus as organizations evaluate the long-term impact of AI in bankruptcy and restructuring matters.

Making data usable

If you spend any time on a bankruptcy matter, you know how much of the work starts with data cleanup. Financials, ledgers, claims, bank statements, and other documents often arrive in formats that are anything but user-friendly.

AI is increasingly being used to accelerate one of the most time-consuming phases of a bankruptcy engagement: transforming large volumes of financial information into a format that can actually be analyzed. AI can:

• • Extract data from PDFs, bank statements, and other source documents
  • Convert unstructured information into searchable, structured datasets
  • Run searches across large volumes of financial data
  • Reconcile information across multiple data sources
  • Identify missing, duplicate, or inconsistent records
  • Surface anomalies, patterns, trends, and unusual transactions for further review
  • Summarize key findings from financial reports, court filings, and supporting documents

These capabilities do not replace professional judgement. Instead, they allow teams to spend less time on manual data preparation and more time on the analysis and investigation, which drives value for clients and reduces costs.

AI as a forensic tool: Transforming bankruptcy investigations

Financial investigations often require professionals to connect information scattered across bank records, general ledgers, claims data, and other financial documents. In bankruptcy matters, that process can involve reviewing thousands of transactions and reconciling information from multiple parties. AI is already making a clear difference by helping practitioners perform these tasks more efficiently as it assists in organizing, analyzing, and identifying relevant information within large, fragmented datasets.

According to data from Bankruptcy Data, fraud-related bankruptcies are most concentrated in the $10 million to $100 million and $100 million to $500 million asset bands. Because cases within these asset bands often involve larger estates and significant volumes of financial data, tools that can quickly organize and analyze information can be particularly valuable in matters involving allegations of fraud or financial misconduct, where investigators are often asked to identify unusual transactions, trace the movement of funds, and compare records across multiple sources.

Fraud-related bankruptcies also occur across a broad range of industries, with healthcare and medical, retail, and banking and finance among the largest contributors, followed closely by technology, media, and telecom. This suggests that fraud-related bankruptcy risk is not isolated to a single sector and reinforces the value of tools that help teams clean data, review records, and focus efforts on transactions that warrant further scrutiny.

AI can assist with many of the preliminary tasks that precede substantive analysis. It can organize and reconcile data, match invoices to claims, compare datasets, and search large document and email collections using targeted criteria. AI cannot replace professional judgment, but it can narrow the field of review so professionals can focus on the issues that matter most.

Accuracy still matters

While AI can help organize and analyze large volumes of information, the risks of unverified AI use remain very real. These tools can produce results that appear credible but are incomplete, inaccurate, or unsupported. As recently as July 2026, the U.S. Court of Appeals for the Eleventh Circuit rebuked an attorney for filing briefs containing what the court described as “fake and hallucinated” case citations generating by AI. That’s not a small issue, especially in a legal setting.

However, whether a legal, financial, or forensic setting, that type of error can have significant consequences. So, the question shifts from “What can AI do?” to “How should its output be reviewed and validated?”

The effective use of AI generally requires:

• • Review by professionals who understand the underlying facts and conclusions
  • Clear policies and processes governing how AI tools are used
  • An understanding of the limitations and risks associated with AI-generated output

As firms continue to integrate AI into their workflows and develop their own AI policies, clients should not hesitate to ask how those tools are governed, what safeguards are in place, and whether human review is required before work product is delivered.

Building repeatable approaches to AI

Leading firms are moving beyond individual experimentation with AI and focusing on how to incorporate it into their organizations in a repeatable and scalable way. The goal is not simply to automate individual tasks, but to incorporate AI into workflows where it can create measurable value. This often includes:

• • Identifying recurring inefficiencies and process bottlenecks
  • Prioritizing use cases that can improve quality, efficiency, or consistency
  • Developing tools and workflows that can be reused across engagements and teams
  • Creating repeatable workflows for investigations, document review, and transaction analysis
  • Building reusable AI-assisted tools that can be deployed across multiple bankruptcy and restructuring matters

Contract analysis is a practical example that expands across multiple industries and professions, not just restructuring and bankruptcy. Rather than reviewing each agreement manually, firms can develop AI-assisted tools that quickly surface critical provisions, such as scope of services, pricing terms, and revenue recognition considerations. While professionals still need to take time to evaluate the results, AI can reduce the time spent locating and organizing information, allowing teams to focus more quickly on analysis and decision-making.

Coordination and transparency are key

As AI becomes more common in bankruptcy matters, coordination among advisors, law firms, and clients becomes increasingly important.

At the start of a case, it helps to align on:

• • Which tools are being used
  • How those tools will be applied
  • How information and data will be managed

At the same time, some clients are cautious about AI and may have concerns about confidentiality, data handling, or the reliability of AI-generated output. Transparency is therefore essential. Firms should be prepared to explain their approach in clear terms and adapt when clients prefer a different level of AI involvement.

Experience and expertise remain essential

This point is worth emphasizing again: the value of AI lies not in replacing professionals, but in enhancing the efficiency and effectiveness of informed professional judgment. Regardless of how sophisticated AI tools become, experience and expertise remain indispensable.

Professionals still need to evaluate evidence, question results, spot inconsistencies, assess context, and make judgment calls. Those responsibilities remain central to bankruptcy, restructuring, and forensic engagements.

Firms need to consider that if professionals become overly dependent on AI-generated outputs, they risk bypassing the experiences that build sound judgment. This is especially vital for newer professionals, and firms need to find ways to balance the benefits of AI with the importance of developing critical thinking, investigative skills, and professional skepticism. Technology may accelerate parts of the process, but experience remains a key component of effective decision-making. The challenge for firms will be using AI to enhance professional development without inadvertently replacing it.

Looking ahead

AI has become an important part of the toolkit for bankruptcy professionals, whether firms actively prioritize it or not. Some firms will use it primarily to improve efficiency, while others will build comprehensive processes around it, including clear governance, strong quality control, and defined use cases.

This distinction will matter. The firms that derive the greatest value from AI are unlikely to be those that rely on it most heavily. Instead, they will be the ones that combine technology with sound processes, professional judgment, and a clear understanding of where AI adds value and where human expertise remains indispensable.