Banks Warned on AI Use: What Can We Learn?
Australia’s financial regulator, APRA, has raised the alarm over banks’ expanding use of AI, citing lagging governance and cybersecurity measures.
At first glance, this might seem like a concern only for large corporations. However, I believe that SME owners, in particular, have much to learn from this warning.
Why? Because as the barriers to AI adoption lower, the “blind spots” in governance widen. Large companies have dedicated departments and budgets, but SME owners must make these judgments themselves. This difference creates fatal risks.
This article uses APRA’s findings as a starting point to explore governance design for SMEs adopting AI. I will share concrete actions that business owners can take to transform their AI use into a “safe design.”
APRA’s Warning: Three Blind Spots in AI Adoption
APRA’s warning can be summarized into three main points.
First, lack of governance. Who manages and is responsible for AI decision-making processes is unclear. Second, lagging cybersecurity measures. AI systems can become new targets for attacks. Third, unclear accountability. There is no system in place to explain the cause when an AI makes a wrong decision.
These are precisely the risks SMEs face when adopting AI. Unlike large companies, without specialized departments, these three points are often left as “invisible risks.”
For example, when analyzing customer data with AI, who is responsible for managing that data? If an AI recommends a product to a customer and it’s inappropriate, who takes responsibility? Moving forward with implementation without answering these questions can lead to major trouble later on.
The Structure of “AI Governance Failure” in SMEs
Interpreting APRA’s warning in the context of SMEs reveals the following structure.
In many SMEs, decisions about AI adoption are made by the “technical department” or “at the owner’s discretion.” In such cases, legal and compliance perspectives are often overlooked.
This stems from a lack of understanding that governance is not just “defense” but a “design skill.” AI is an “offensive” tool to accelerate business, but its adoption process requires a “defensive” perspective of designing for risk.
For instance, imagine an SME starts a service that analyzes customer purchase history with AI and distributes personalized coupons. At this point, simply checking “Does this violate the Personal Information Protection Law?” is insufficient. You need to design for “risks 1 through 99,” such as whether the AI’s analysis contains discriminatory content, or how to respond if a customer finds the analysis inappropriate.
I call this state of incomplete design “AI governance failure.” This not only hinders business growth but can also lead to legal sanctions and reputational risk.
Three Steps to Visualize “Invisible Risks”
So, how can we specifically prevent “AI governance failure”? I recommend the following three steps.
Step 1: Clarify the Purpose and Scope of AI Adoption
First, the business owner must articulate *why* and *for what purpose* they are introducing AI. At this point, set specific numerical targets, such as “reduce customer response time by 50%,” rather than just an abstract goal like “improve operational efficiency.”
Then, clarify the scope of AI decision-making. For example, establish a rule that “the AI only makes suggestions, and the final decision is made by a human.” This delineation clarifies where responsibility lies.
Step 2: Design Risk Assessment and Countermeasures
Next, identify and assess the risks associated with AI adoption. Using a “probability × impact” matrix is effective here.
For example, the risk of an AI’s incorrect judgment causing harm to a customer may have a low probability but a high impact, so it requires priority countermeasures. Specifically, build a system that logs AI decisions for later verification.
Additionally, as a cybersecurity measure, minimize access rights to the AI system and conduct regular vulnerability assessments. As APRA pointed out, AI is a new attack target. Neglecting these measures could lead to serious incidents like customer data leaks.
Step 3: Create a System for Accountability
Finally, establish a system to explain the reasoning behind an AI’s decision if it is questioned. This is a countermeasure to the so-called “AI black box problem.”
For SMEs, implementing advanced Explainable AI (XAI) like large corporations might be difficult. However, at a minimum, it is crucial to record “what data the AI based its decision on” and be able to explain this to relevant parties.
Also, set up a contact point to handle customer inquiries and clarify the consultation route for customers who are not satisfied with an AI’s decision. Having this system alone can significantly reduce the risk of losing customer trust.
What Business Owners Should Do Now: Redefining Governance Design
APRA’s warning shows that AI adoption is not a “technical issue” but a “governance issue.” What is required of SME owners is not just “using” AI, but “designing it correctly.”
First, take stock of your company’s AI adoption projects and check whether the three steps above are fulfilled. If implementation is already decided but these considerations are insufficient, you need the courage to temporarily halt the project.
Governance exists not to “stop business,” but to “accelerate business safely.” Before installing the powerful engine of AI, make sure to design the brakes and steering wheel properly. This is the most reliable way for SMEs to achieve sustainable growth.
Business owners, now is the time to visualize the “invisible risks” of AI adoption. That is the first step to protecting your company’s future.
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