What could AI mean for mutual banks?
AI has exploded into public view – and its transformative impact on industries like banking is only just beginning.
In 2022, AI leapt into the public domain and, throughout 2023, has been evolving rapidly in full public view.
As industries and societies grapple with AI's implications, Accenture notes that banking is at the forefront of potential impact, with processes ripe for transformation. McKinsey goes further, suggesting a complete overhaul of the banking business model. And, just to prove the point, cnvrg.io’s ML Insider 2023 survey indicated that financial services and banking are among the industries leading AI adoption (measured by the number of machine learning models in operation).
However, not all banks can exploit the potential of AI in the same way. Some may be more focused on customer experienced, others on efficiency, and still others may invest more on security and risk. Australian mutual banks face the task of balancing opportunity, member value, and resource capacity to fully harness AI's potential.
How could AI affect mutual banking?
Banks have used AI for some time, from fraud detection to guiding friendly bank tellers on what to talk to customers about. But the transformation journey has only just begun. We have identified seven areas that banks are already experiencing, or likely to experience, AI impact (see adjacent table).
However, the drivers of value from AI for mutual banks are likely to be concentrated in areas relevant to their relatively simple business model and focus on member value, whereas mainstream banks need to balance customer and shareholder value drivers.
We have identified four of the seven areas of impact as particularly important for mutual banks (updated in February 2025 with real case examples):
| Area of Impact | Description of Value | Examples |
|---|---|---|
| Customer Experience and Services | Enhances personalisation and support | Advanced chatbots, personalised financial advice, enhanced customer interactions |
| Risk Management and Compliance | Improves fraud detection and regulatory reporting | Fraud pattern recognition; automated compliance reports; dynamic credit scoring |
| Product Development and Innovation | Aids in creating tailored financial products | Market analysis tools; AI driven product design |
| Operational Efficiency | Streamlines processes and decision-making | Process automation; AI assisted operational decisions |
| Marketing and Personalisation | Enables targeted campaigns and segmentation | Data-driven marketing strategies; personalised content creation |
| Content Synthesis and Generation | Facilitates creation of marketing and communication content | Automated content generation; dynamic content adaptation |
| Strategic Integration of AI | Embedding AI into core bank strategies | Leadership and vision alignment; AI centric cultural shift |
- Customer Service Enhancements: mutual banks have differentiated themselves by focusing on communities with tightly defined banking needs and delivering exceptional service to those segments; AI can improve the quality and efficiency of personalised services and customer interactions, making banking more accessible and tailored to individual needs. CBA has demonstrated this with AI-powered messaging in their mobile app, leading to 40% reduction in call centre wait times.
- Boosting Operational Efficiency: because of their relative scale, mutual banks have wrestled with efficiency (cost-to-income ratios typically being ~70% or higher for Australian mutual banks); by automating routine decisions (and then automating related tasks), AI could allow staff to focus on value-added activities (e.g., service), enhancing overall bank efficiency. For example, NAB uses AI to expedite the review of trust deeds, reducing processing time from 45 minutes to just one minute per document.
- Advancements in Risk Management & Compliance: small banks are disproportionately prone to the impact of risks and the burden of compliance reporting (to regulators) can drain them of capacity to focus on direct member value; AI’s ability to identify and suggest mitigation approaches for risks, including fraudulent activities, is vital for maintaining trust and security in banking operations, while it could also lead to lowering the burden of regulatory reporting (regtech solutions). CBA has deployed AI-based security features, such as NameCheck, CallerCheck, and CustomerCheck, reducing customer scam losses by 50% and decreased reported fraud incidents by 30%.
- Strategic Integration: mutual banks will need to avoid the temptation to keep AI as a side conversation; incorporating AI into the bank’s strategic planning is essential for fully realising its benefits and aligning its potential with the bank’s purpose and strategic goals. CBA has been a leader in this field over several years (I was involved in early work defining a strategy and operating model for data & analytics for the bank) to implementing an AI Factory initiative that is working on a coordinated pipeline of AI solutions across multiple facets of the of the bank.
While the other dimensions may be valuable, we think these four are likely to generate the greatest value for mutual banks given their strategic focus on members, driving efficiency in operations and in risk & compliance, and using innovation for practical purposes.
How should sector leadership be thinking about AI in mutual banking?
Mutual banks have had to balance member value creation with business sustainability. With relatively small scale, growing costs, and stiffening competition, there’s little room to splash member equity on speculative investments.
But ignoring the industry-wide AI phenomenon is not an option either. So how to maximise the opportunity while controlling the risks?
- Tangible value creation: investment in AI needs to lead to real member value creation; going big bang on something experimental rarely ends well, so balance investment capacity, controllable risk, and promise to drive accretive value.
- On purpose: the AI agenda needs to support and fully align with your overarching purpose and, usually, your strategic goals (unless they’re being disrupted by AI); harness the potential of AI to deliver your strategy and purpose.
- Ethical foundation: with regulatory ambiguity around AI, there is much risk that implementations may need to be remediated in the future. By remaining focused on core values, ethics, and transparency, mutual banks may find a strategic advantage in their application of AI as the rule book catches up with more complex institutions.
- Core to innovation culture: by weaving AI into your bank's long-term plans and building it into your bank’s culture, you can rapidly build an organisation-wide capability and innovation culture that could lead to surprising strategic differentiation.
Practical applications of AI are becoming increasingly accessible as are the foundational enablers. The change is coming. Will mutual banks seize the initiative by incorporating AI as a strategic tool?
