Data-Driven Insolvency Prediction and the Director Duty to Prevent Insolvent Trading under s588G of the Corporations Act 2001 (Cth)

Overview

How could data-driven insolvency prediction tools inform liability arising from 'reasonable grounds for suspecting' insolvency under s588G of the Corporations Act 2001 (Cth)?

This research project examines how emerging data-driven insolvency prediction tools could inform, and/or influence the assessment of, directorial liability under s588G of the Corporations Act 2001 (Cth).

Section 588G provides a duty upon Australian directors to prevent insolvent trading by the company. It applies if a director fails to prevent a company incurring a debt in circumstances where:
-  the company is insolvent at the time the debt is incurred, or becomes insolvent by incurring debts including the debt;
- there are ‘reasonable grounds for suspecting’ the company is insolvent or would become insolvent; and
- the director is aware at the time that there were such grounds for suspecting, or a reasonable person in ‘a like position in a company in the company’s circumstances’ would be aware.

The research project focuses on how prediction tools may affect or inform consideration as to what directors knew, or ought reasonably to have known, or suspected about insolvency, particularly in light of the objective reasonable director standard.

While Australian law traditionally relies upon statutory presumptions of insolvency and traditional indicators of insolvency derived from financial statements, advances in data analysis and artificial intelligence now arguably enable more sophisticated detection of insolvency risk. The project will explore whether such tools could reshape the resources available to directors, the expectations of directors, and influence how courts assess liability to prevent insolvent trading.

An optional comparative element for the project, or for subsequent research, is the similar doctrine of wrongful trading (under s 214 of the Insolvency Act 1986 (UK)) to provide additional insight into director liability standards more broadly. There are further international comparisons which could extend the research in any Master or PhD undertaking.

Research engagement

The research will be primarily doctrinal legal research focusing on s588G of the Corporations Act 2001 (Cth), relevant case law and regulator guidance. It will involve consideration as to how courts interpret 'reasonable grounds for suspecting' insolvency and the purpose of s588G. The project will be contemporary and interdisciplinary in nature, drawing upon literature across corporate insolvency law, and emerging data-driven insolvency prediction tools and artificial intelligence.

Research activities

Research skills

The student will develop skills in doctrinal legal research, including analysing legislation and case law. They will strengthen their critical analysis skills and engage with interdisciplinary scholarship in law and technology. The research project will facilitate and enhance skills in legal writing, critical thinking, and independent research. It is on a highly contemporary topic, providing exposure to legal issues arising from developments in artificial intelligence.

Outcomes

The aim of this project is to examine how insolvency prediction tools may inform the interpretation and application of ‘reasonable grounds for suspecting’ insolvency under s 588G of the Corporations Act 2001 (Cth). The project seeks to critically determine whether developments in data analsis and artificial intelligence may influence the resources available to directors, the expectations placed upon directors, and the legal consequences for directors with respect to their duty to prevent insolvent trading. The primary outcome of the project will be a critical research paper that synthesises relevant case law, statute and literature to advance contemporary academic consideration of the intersection of corporate insolvency law and emerging technologies.

Skills and experience

The student should have a basic understanding of corporate law and doctrinal analysis (the study of statute and case law).

Start date

2 November, 2026

End date

19 February, 2027

Location

Queensland University of Technology, Gardens Point

Additional information

The student may discuss the project timeframe (within the mandatory period from 02 November 2026 to 19 February 2027) to facilitate a mutually convenient project schedule.

Keywords

• corporate
• insolvency law
• data
• artificial intelligence
• insolvency prediction

Contact

Dr Elizabeth Streten

31384031

elizabeth.streten@qut.edu.au