The Legal Landscape of Artificial Intelligence & Automation for Australian Non-Bank Lenders

Introduction

The use of artificial intelligence and automation is now an operational reality for Australian non-bank lenders, moving from experimentation to execution in core financial services. This technological shift enables greater precision in automated credit decisions and risk management, but it has also attracted heightened regulator scrutiny over fairness, accountability, and the potential for consumer harm.

This article examines the evolving legal landscape for any lender implementing these technologies. It covers critical areas including AI governance, data handling and privacy compliance, the operational risks of outsourcing, and the duties of directors in overseeing AI systems. Understanding these issues is essential for compliant and effective AI adoption in Australia.

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The Shift Towards Artificial Intelligence & Automation in Australian Non-Bank Lending

Implementing Agentic AI & Automated Credit Decisions

The Australian non-bank lending sector is transitioning from experimental artificial intelligence to operational systems that form a core part of credit processes. Where previous years focused on AI for insights, the market is now adopting automated credit decision systems that use algorithms to approve loans, adjust credit limits, and monitor repayment risks. These systems enable lenders to process applications faster and incorporate more diverse datasets into their underwriting.

A significant development in this area is the move towards agentic AI. These are systems capable of acting autonomously to coordinate, investigate, and optimise workflows. In the context of financial services, agentic AI can perform several key functions:

• Identifying default risk months in advance.
• Flagging breaches of lending policy before a loan is approved.
• Managing frontline compliance activities in real time.
• Handling fraud detection and anti-money laundering investigations with human oversight reserved for complex cases.

This shift means AI is no longer just a tool for efficiency but is becoming central to managing credit and compliance risk.

Utilising Alternative Data for Risk Assessment

Alternative data refers to the use of non-traditional information to assess a borrower’s risk profile. Australian lenders are increasingly using these data sources to supplement or replace traditional credit scoring methods. This information can include:

• Transaction data from bank accounts.
• Activity on digital platforms.
• A borrower’s digital footprint.
• Real-time payment information.

The use of this data allows fintech lenders to extend credit to borrowers who might be underserved by traditional models that rely solely on credit bureau scores. For small and medium-sized enterprises, this can mean offers of working capital based on transaction history within e-commerce or payment processing platforms.

While alternative data can improve the accuracy of default prediction, its use in algorithmic credit scoring raises concerns about fairness and discrimination. There is a risk that algorithms could produce different pricing outcomes for various groups of people, even as overall prediction accuracy improves. This has attracted regulatory attention, as lenders must ensure their automated systems do not lead to discriminatory practices.

Decisioning Risk & Regulator Scrutiny for Private Lenders

Addressing Algorithmic Fairness & Explainability

As automated lending systems become more common, the ability to explain their decisions is a key legal constraint. Lenders using complex algorithms must be prepared to provide specific and accurate reasons for taking adverse actions, such as denying a credit application. This requirement ensures that decision-making processes remain transparent and accountable, even when driven by artificial intelligence.

The use of AI in credit scoring also presents challenges related to fairness and discrimination. Algorithms can produce different pricing outcomes for various groups, even if they improve overall prediction accuracy. This raises concerns about proxy discrimination, where models may use variables that indirectly correlate with protected characteristics, leading to biased lending decisions.

For Australian lenders, this means that simply adopting AI for efficiency is not enough. They must also ensure their models are:

• Fair – avoiding outcomes that disadvantage particular groups without justification; and
• Explainable – capable of providing clear reasons for individual decisions.

Managing the Dangers of Agentic AI

ASIC has expressed concerns about the risks consumers face from advanced technologies, including automated decisions and AI-driven interactions. The rapid adoption of new technology can enable conduct that exploits behavioural biases in consumers. As AI becomes more sophisticated, its capacity to independently plan and act can compound these risks.

The introduction of agentic AI, in particular, increases these dangers. Such systems introduce greater autonomy and unpredictability into financial services. This creates the potential for new types of harm that can result from autonomous decision-making. For any lender, this highlights the need for careful governance to manage the risks associated with these emerging AI capabilities, making it critical to seek legal guidance for private lenders.

Data Handling & Privacy Compliance for Finance Companies

Preparing for New Automated Decision Transparency Rules

New obligations under the Privacy and Other Legislation Amendment Act 2024 (Cth) will require entities to provide more transparency around automated decision-making. From 10 December 2026, if a computer program uses personal information to make decisions that could significantly affect an individual’s rights or interests, this must be disclosed in the entity’s privacy policy.

Under Australian Privacy Principles (‘APPs‘) Principle 1, as amended, the privacy policy will need to include specific information about these automated processes. This includes detailing:

• The kinds of personal information used by the computer programs.
• The types of decisions made solely by these programs.
• The kinds of decisions where a computer program is substantially and directly involved in the process.

Examples of decisions that could be affected include those related to granting or refusing a benefit, such as a loan, or decisions that impact an individual’s rights under a contract. Lenders using artificial intelligence for credit assessment will need to update their privacy policies to comply with these upcoming requirements.

Ensuring Lawful Collection & Data Minimisation

The APPs establish clear rules for the collection of personal information. Under APPs Principle 3, collection must be reasonably necessary for an entity’s functions and activities. This implies a principle of data minimisation, meaning lenders should only collect the minimum amount of personal information required. Over-collection increases security risks and the potential for harm if a data breach occurs.

Personal information must also be collected by lawful and fair means. The practice of data scraping, which involves automatically extracting data from websites, may be considered an unfair means of collection under APPs Principle 3.5. Whether it is unfair depends on factors such as:

• The individual’s reasonable expectations.
• The sensitivity of the information.
• The risk of harm.

Just because personal data is publicly available online does not grant an automatic right to collect and use it for training artificial intelligence models.

Outsourcing & Operational Resilience in Alternative Lending

Mitigating Third-Party Technology & Cybersecurity Threats

The increasing use of digital systems and reliance on third-party technology providers elevates cyber risk for non-bank lenders. Key sources of risk include:

• Legacy systems that can introduce vulnerabilities
• Dependence on a small number of external cloud and artificial intelligence vendors, creating operational concentration risks

A disruption at one of these major providers could affect a large part of the financial system at the same time.

ASIC has urged directors and holders of financial services licences to maintain strong risk management frameworks to counter these threats. This includes:

• Testing their operational resilience and crisis response plans
• Addressing any security weaknesses with their third-party service providers to ensure the entire technology chain is secure

Upholding Director Accountability in Corporate Governance

Company directors are expected to actively oversee the risks associated with using artificial intelligence and other third-party technologies. The law requires directors to engage with these issues with a high degree of care and curiosity, and they cannot simply be passive recipients of information provided by management or external vendors.

Directors have a duty to interrogate the information presented to them and cannot substitute reliance on management’s advice for their own examination of important matters. This means:

• Actively questioning vendor assurances
• Understanding the potential for new kinds of harm that can arise from autonomous decision-making systems

Being overwhelmed by the volume of information is not an excuse for failing to guide and monitor the company’s management of these critical operational risks.

Conclusion

The use of artificial intelligence and automation offers Australian non-bank lenders significant opportunities, but it also creates complex obligations regarding AI governance, data privacy, and operational resilience. For any lender, the successful adoption of these technologies depends on precise execution and careful management to satisfy regulatory expectations and maintain consumer trust.

To understand how these evolving requirements affect your financial services operations, contact the legal professionals at GRM Law. Our team provides specialised legal advice for non-bank lenders to help you implement AI and automation in compliance with Australia’s changing laws.

Frequently Asked Questions

Disclaimer: This is general information only and is not legal advice. For advice on your circumstances, contact GRM LAW.