AFSL Requirements for Robo-Advice & Digital Advice Platforms

Introduction

Digital advice, also known as robo-advice, involves providing automated financial product advice through algorithms and technology, often without the direct involvement of a human adviser. Under Australia’s regulatory framework, this form of financial advice is governed by the Corporations Act 2001 (Cth) in the same way as traditional advice, meaning platforms that recommend financial products generally require an Australian Financial Services Licence (AFSL).

For fintech founders, obtaining an AFSL is more than a procedural step; it serves as the blueprint for the product’s architecture, where the algorithm itself must discharge legal duties. This guide details the specific AFSL requirements for digital advice models, focusing on Australian Securities and Investments Commission (ASIC) Regulatory Guide 255 (RG 255) and the AFSL compliance & regulation frameworks necessary to meet these obligations, for which the Australian financial services licensee remains entirely responsible.

General & Personal Advice Models for Fintechs

Understanding General Advice in Robo-Advice 

General advice models provide recommendations or opinions about financial products without considering a client’s individual objectives, financial situation, or needs. Under section 766B(4) of the Corporations Act 2001 (Cth), any financial product advice that is not personal advice is classified as general advice.

This model is characterised by a lower regulatory burden because it does not tailor recommendations to specific user circumstances. Platforms offering general advice typically do not collect personal financial data, or if they do, the algorithm ignores it when making a recommendation.

Consequently, these robo-advice models must issue a clear General Advice Warning, stating that the advice does not take the user’s personal situation into account. Common applications of this model include:

• Presenting thematic investment lists or market updates.
• Offering generic “model portfolios” without a specific recommendation for the user to buy.
• Providing qualitative commentary on stocks, including performance and dividend yields.

A compliance risk arises if the platform’s user experience infers a client’s circumstances or “nudges” them toward a particular product. In such cases, the ASIC may classify the service as providing personal advice, which triggers more stringent regulatory obligations.

Defining Personal Advice in Digital Advice

Personal advice is defined under section 766B(3) of the Corporations Act 2001 (Cth) as financial product advice where the provider has considered one or more of the client’s objectives, financial situation, and needs. This also applies where a reasonable person might expect the provider to have considered these factors.

Digital advice platforms fall into this category when they use algorithms to generate tailored recommendations based on user inputs. This model carries a high regulatory burden, as it requires the Australian financial services licensee to comply with significant legal duties.

For instance, when a user inputs personal data such as their age, income, risk tolerance, and financial goals, and the algorithm suggests a specific portfolio, the platform is legally providing personal financial product advice. This triggers key obligations, including:

• The duty to act in the client’s best interests under section 961B of the Corporations Act 2001 (Cth).
• The requirement to provide the client with a Statement of Advice (SoA).

A critical point for fintechs is that personal advice can be provided by conduct. If a user reasonably expects that their personal circumstances have been considered by the platform, the service may be deemed to be providing personal advice, even if that was not the provider’s intention.

The Algorithm as Adviser under RG 255

Licensee Responsibility for Algorithm Output

Under RG 255, the law is technology-neutral, meaning the same obligations that apply to a human adviser also apply to an algorithm. The Australian Financial Services (AFS) licensee is legally treated as the provider of the digital advice and is strictly liable for all financial product advice generated by its robo-advice platform. This responsibility cannot be shifted to a software vendor or blamed on a coding error.

The AFS licensee’s accountability extends to all aspects of the algorithm’s output, including:

• Logic errors and bugs
• Incorrect risk-profiling
• Data misinterpretation
• Code changes pushed by developers

Section 961(6) of the Corporations Act 2001 (Cth) clarifies that when personal advice is provided through a computer program, the person offering the service is considered the provider. Consequently, if a client suffers a loss due to defective advice from the algorithm, the AFS licensee is the entity that bears the legal responsibility.

Organisational Competence & Responsible Managers

To meet the organisational competence obligation, a digital advice licensee must demonstrate an understanding of the technology it employs. ASIC requires that a digital advice licensee has at least one responsible manager who meets the minimum training and competence standards that apply to human financial advisers. This ensures a qualified individual oversees the automated advice process.

While this responsible manager does not need to understand the specific computer coding, they must have a general understanding of how the algorithm functions. ASIC expects people within the business to comprehend the following:

• The rationale behind the algorithm’s design
• The risks associated with the automated advice model
• The rules and decision-making logic that underpin the digital advice

Failing to have personnel with the necessary skills to understand the technology increases the risk of clients receiving poor-quality advice. The licensee cannot treat the algorithm as a “black box” and must be able to explain its decision logic to ASIC if required.

Technical Compliance &  Algorithm Audit

Implementing a ‘Compliance by Design’ Framework

Robo-advice platforms require a “Compliance by Design” framework, which involves embedding legal and regulatory obligations directly into the system’s technical architecture. This approach ensures that the algorithm is structured to act in the client’s best interests from the ground up. ASIC expects the system to have explainable logic rather than being an opaque “black box.”

Key elements of an effective Compliance by Design framework include:

Element	Description
Requirements Mapping	Each legal obligation, such as the best interests duty under section 961B of the Corporations Act 2001 (Cth), is mapped to a specific rule or block of code within the algorithm.
Transparent Decision Trees	The platform must be able to demonstrate precisely how the algorithm arrives at each recommendation, creating a clear and reproducible audit trail for every piece of financial product advice.
Hard-Coded Limitations	The algorithm is programmed to prevent it from providing advice on financial products that are outside the scope of the AFS licence authorisation.
Automated Disclosures	The system automatically generates necessary warnings and disclosures, such as an SoA, and issues real-time alerts if a client’s data is incomplete or a recommendation conflicts with their stated risk profile.

Documentation & Version Control for Algorithms

To demonstrate compliance to ASIC, a digital advice licensee must be prepared for an “Algorithm Audit.” This requires meticulous documentation and strict version control to show how the automated advice platform produces compliant financial advice.

A “Functional Specification” document is essential, as it translates legal rules into pseudo-code and serves as a key piece of evidence if your platform’s logic is ever challenged.

Effective change management is another critical component. Every time the algorithm’s code is updated, the AFS licensee must follow a clear process:

Process Step	Requirement
Log Changes	Log every change made to the algorithm, including the date and reason for the update.
Conduct Regression Testing	Run the new version against “Gold Standard” client profiles to ensure it performs as expected and does not introduce errors.
Maintain Records	Maintain a comprehensive record of which version of the algorithm was used to provide advice to each client at any given time.
Retain Records	Keep records of any changes for a minimum of seven years, in line with ASIC’s record-keeping obligations for personal advice.

Programming the Best Interests Duty & Scaled Advice

Coding the Safe Harbour Provisions 

For a personal advice model to be compliant, its algorithm must be programmed to satisfy the best interests duty under section 961B of the Corporations Act 2001 (Cth). A common way to demonstrate compliance is by digitally embedding the “safe harbour” provisions from section 961B(2) directly into the platform’s user flow and decision-making logic. This involves mapping each statutory step to a specific function within the automated advice process.

An algorithm can be coded to satisfy these provisions through several key implementations:

Implementation	Description
Identify Client Objectives & Needs	The user interface must have a mandatory fact-finding process for clients to input financial goals, investment horizon, and risk tolerance, preventing users from skipping critical data fields.
Define the Subject Matter	The algorithm must explicitly define the scope of the financial product advice it can provide (e.g., only advises on ETFs and not other assets like property).
Handle Incomplete Information	The code should trigger “hard stops” or “hard outs” if a user provides inconsistent data, forcing them to re-confirm inputs or exit the advice journey.
Prioritise Client’s Interests	The algorithm’s logic must avoid conflicts of interest, such as not preferentially recommending in-house products. If the best interests duty cannot be met, the system must automatically refuse to provide advice.

Digitally Limiting the Scope of Advice

Most robo-advice platforms provide “scaled advice,” which is personal advice that is limited in scope, often focusing on a single topic like investment selection rather than comprehensive financial planning. To provide scaled advice legally, ASIC requires the algorithm and user interface to ensure the client fully understands the limitations of the financial advice.

Digital platforms can achieve this through carefully designed mechanisms that manage the scope of the automated advice, such as:

Mechanism	Implementation
Clear Communication of Scope	The platform must clearly explain to the client from the outset what the digital advice will and will not cover, including limitations, consequences, risks, and benefits.
Active Acknowledgement	The user experience should incorporate “friction” points, such as mandatory checkboxes or pop-ups, where the client must actively confirm their understanding of the advice limitations.
Gap Analysis	The algorithm should be programmed to detect if the agreed-upon scope is too narrow to be in the client’s best interests and, if so, refuse to provide advice.

Testing, Monitoring & the Triage Process

The ‘Human in the Loop’ Requirement

Under RG 255, a digital advice platform cannot be a “set and forget” system. The AFS licensee must have the human capacity to monitor and test the quality of the algorithmic financial advice on an ongoing basis. This “Human in the Loop” model is a mandatory compliance requirement to ensure the automated advice remains appropriate and legally compliant.

Implementing this human oversight involves several key processes:

Oversight Process	Key Actions & Requirements
Regular Monitoring & Testing	Maintain a documented test strategy with clear plans and cases, and ensure the resolution of any defects is tracked.
Random Sampling	A qualified human adviser must regularly audit a random sample of digital advice to ensure the output is legally compliant, suitable for the client, and free of systematic errors.
System Controls	Implement technical controls like “stop switches” to immediately suspend the service if a bug is detected, and have mechanisms to prevent widespread defective advice.
Change Management	Every change to the algorithm’s code or logic must be logged, tested, and signed off by compliance personnel before deployment to create a clear audit trail.

Implementing a Triage Process to Filter Clients

A critical component of a compliant robo-advice framework is the triage process, a requirement detailed in RG 255.66. This process is designed to automatically filter out and reject clients for whom the automated advice model is inappropriate. In fact, a compliant digital advice platform is often defined by the clients it chooses not to serve.

The algorithm’s decision tree must be programmed with “off-ramps” or “hard outs” to identify and decline users with circumstances that are too complex for the automated model. The triage system should flag and reject users in various situations, including when they have:

Scenario for Rejection	Description
Complex Financial Situations	The user has complicated financial structures, such as complex income or self-managed superannuation funds.
Needs Outside of Scope	The user’s stated needs or goals fall outside the clearly defined scope of the digital advice service (e.g., needing mortgage advice from an ETF-only platform).
Inconsistent Information	The user provides conflicting inputs (e.g., a high-risk tolerance combined with a short-term need for funds), triggering the algorithm to halt the process.

When a client is filtered out, the platform should explain why the automated advice cannot be provided and, where appropriate, direct them to a human adviser for more specialised assistance.

Liability & Insurance for Defective Fintech Advice

Liability for Algorithmic Errors & Bugs

The AFS licensee is strictly liable for all financial product advice generated by its robo-advice platform. If an algorithm contains a bug or logic error that systematically provides defective advice, the legal responsibility falls entirely on the licensee, not the software developer or a third-party vendor.

This liability is significant because a single coding flaw can be replicated across thousands of users, creating massive and aggregated financial exposure.

When defective digital advice leads to client losses, the AFS licensee is exposed to significant legal risks, including:

Legal Risk	Description
Class Actions	A systemic error affecting many clients can lead to a single, large-scale class action lawsuit.
Statutory Liability	Under the Corporations Act 2001 (Cth), clients who suffer a loss due to a breach of adviser obligations have statutory rights to seek damages or compensation.
Remediation Obligations	ASIC expects licensees to identify and remediate all affected clients, which includes rectifying the problem, suspending the algorithm, and contacting those who suffered a loss.

PI Insurance for Robo-Advice

To manage the financial risks of defective advice, digital advice platforms must secure adequate Professional Indemnity (PI) insurance. Standard PI policies are often insufficient, as they may contain exclusions for software errors or algorithmic trading losses. Consequently, fintechs require specialised insurance products tailored to the unique risks of automated financial services.

A suitable policy for a robo-advice provider should specifically cover “Civil Liability arising from the operation of an automated advice algorithm.” When assessing the adequacy of their cover under Australian Securities and Investments Commission Regulatory Guide 126, licensees should consider:

Consideration	Description
Widespread Loss Potential	The policy must account for the risk of a single flawed algorithm causing losses for many clients simultaneously.
Aggregation Clauses	Licensees should understand how their policy treats multiple claims arising from a single algorithmic error, as they may be aggregated into a single claim with one limit.
Business Growth	The insurance cover must be regularly reviewed to ensure it remains adequate as the number of clients and the scale of the business grow.

Conclusion

Operating a digital advice platform in Australia requires an AFSL, with the licensee bearing full legal responsibility for all financial product advice generated by its algorithm under RG 255. A compliant framework demands robust technical design, including algorithm audits and triage processes, alongside continuous human oversight and specialised PI insurance to manage liability for defective advice.

Navigating this complex regulatory landscape requires specialised expertise, so contact our AFSL application lawyers at AFSL House for tailored compliance frameworks and expert guidance. Our team in New South Wales can help turn your regulatory challenges into strategic opportunities, ensuring your fintech platform is built on a solid foundation of compliance.

Frequently Asked Questions (FAQ)