In the context of the insolvency industry, Artificial Intelligence (AI) refers to the use of machine learning algorithms (MLAs) and other forms of AI to analyse data, identify patterns, and make predictions. MLAs use historical data to identify factors likely to influence insolvency risk, such as financial performance, market trends, and economic conditions, which can help insolvency practitioners make more informed decisions about how to manage cases and develop recovery strategies. AI can also be used to automate routine tasks and improve efficiency, freeing up time for insolvency professionals to focus on more complex and strategic work.
Some examples of how AI can be applied to different areas of the insolvency industry include:
- Document review: Lawyers can use AI-powered contract review software to analyse large volumes of documents such as contracts quickly and accurately which helps identify potential risks and inconsistencies.
- Legal research: AI can help lawyers more quickly find cases, statutes, and regulations through MLA identifying key concepts and extract relevant information from legal documents, improving the accuracy of their research.
- E-discovery: AI can analyse electronic documents and communications during e-discovery through MLA identifying relevant information and flagging potential areas of concern.
- Due diligence: AI can automate due diligence processes, by reviewing financial statements and conducting background checks. This reduces the time and cost associated with due diligence, while improving the accuracy of the analysis.
- Predictive analytics: AI can predict outcomes of legal cases, based on historical data and other factors. Lawyers can use this information to develop effective legal strategies and advise their clients accordingly.
- Asset valuation: AI can accurately and efficiently value assets through MLA analysing market trends, historical sales data, and other factors.
- Creditors’ claims: AI can automate the process of verifying creditors’ claims by utilising MLA to review claims and identify potential errors or inconsistencies which reduces the risk of errors and saves time.
- Fraud detection: AI can detect potential fraud in a liquidation through MLA identifying suspicious patterns and behaviours, such as unusual transactions or changes in ownership, and flag them for investigation.
- Asset recovery: AI can identify and recover assets that have been hidden or transferred to avoid recovery in a liquidation by MLA analysing financial data and identifying patterns of suspicious activity, allowing liquidators to recover more assets.
- Payment distribution: AI can automate the process of distributing payments to creditors. MLA analysing creditor claims and determining the appropriate distribution of funds, reducing the risk of errors and improving efficiency.
- Credit scoring: AI can assess creditworthiness and determine credit scores quickly through MLA analysing various factors, such as payment history and outstanding debts.
- Portfolio optimisation: AI can optimise investment portfolios through MLA analysing market trends, historical data, and other factors to determine the best investment strategies.
- Trading algorithms: AI can develop trading algorithms that analyses market trends and executes trades automatically which improves accuracy and efficiency in trading, whilst reducing the risk of human error.
- Risk management: AI can assess and manage risks associated with financial transactions through MLA analysing factors, such as market trends and economic indicators, to identify potential risks and develop strategies to mitigate them.
- Quality control: AI can ensure the quality of valuations by analysing data, identifying potential errors or inconsistencies and flagging them for review.
An illustration of how AI is already impacting our industry is that this article was, up until this sentence, written by ChatGPT. We simply asked it to explain how AI will impact the insolvency industry, to provide some examples that specifically apply to lawyers, liquidators, financiers, and valuers, and then amalgamate it into one report. (We then did a little bit of editing, but not much). In total, it took a fraction of the time it would have taken to write a comparable article from the beginning.
Many professionals, including lawyers and liquidators, are already using ChatGPT for report writing and other tasks (ChatGPT was only released to the public in November 2022). It is presently free to subscribe (although a paid subscription version is available). It has considerable limitations – it doesn’t like giving legal advice or drafting legal documents, and it has limited access to data since 2021, although a ‘live’ version is expected to be released eventually.
ChatGPT is only one adaptation of AI. Some examples listed above have been around for many years. eDiscovery platforms, for example, are not new and common law courts have recognised ‘Technology Assisted Revue’ (TAR) as not only an allowable but the preferred method of giving discovery (see, for example, Da Silva Moore v Publicus Groupe 287 FRD 182 (SDNY 2012) and McConnell Dowell Constructors (Aust) Pty Ltd v Santam Limited (No 1) (2016) 51 VR 421).
eDiscovery platforms have been expensive and only suitable to cases with large volumes of information, but the cost has steadily reduced. Most law firms will soon have a ‘desktop’ eDiscovery platform at their fingertips and liquidators will also use similar programmes for collating and reviewing voluminous books and records.
Technological advances (such as increased automation) have historically tended to impact blue collar jobs, but white-collar professionals in the insolvency and legal profession are not immune to the consistent technical advancements, especially from AI. The perception of many is to view AI as a catalyst to jobs being replaced by “robots”, ethical decisions being made by algorithms and a direct causal change in the way humans communicate.
Instead, we should be viewing AI, not as something that will replace us, but as something we can utilise to improve efficiency, generate more opportunities, and advance our own capabilities as professionals. The possibilities AI creates should be looked at with excitement and inspiration, not with fear and defeat.
AI will not replace professionals, but professionals who embrace AI may well replace those who do not.
If nothing else, when AI advances the way time recording is done by professionals, then these authors will be grateful to our new Robot overlords.
If you have any questions about this article, please get in touch with an author or any member of our Restructuring, Turnaround & Insolvency team.
This information and the contents of this publication, current as at the date of publication, is general in nature to offer assistance to Cornwalls’ clients, prospective clients and stakeholders, and is for reference purposes only. It does not constitute legal or financial advice. If you are concerned about any topic covered, we recommend that you seek your own specific legal and financial advice before taking any action.