Community banking and AI opportunities

18 Sep, 2023

Community banking and AI: opportunities and challenges

Artificial Intelligence (AI) has been part of the digital transformation journey for community banks for a number of years. More recently, awareness of AI has entered the mainstream, with very public discussions on the themes of ethics, privacy and security.

On the other hand, advocates of AI think of it as a tool that will enable us to solve some really big problems, not just within the community banking sector.

The truth is, the adoption of AI by community banks brings with it a range of opportunities and challenges. We talk about a few of them here, with Alex Crommelin, Senior Consultant at 9Yards.

Community banking customer service


Chat bots and apps are used widely by banks to help and engage with customers online. On computers and mobile devices alike, AI-driven chatbots and virtual assistants can offer round-the-clock customer support, answering questions and resolving issues.

A growing level of comfort with these automated service models enhances customer satisfaction with convenience and personalization, with technology that’s available, affordable and manageable for smaller Australian banks with limited staff.


Not all customers want an AI-only experience. Understanding your bank’s vision – how you bring the ‘member first’ approach to life in your bank – should inform how you’ll use AI to engage with your customers.

“Community banks should consider the risk to their brand when adopting AI for those customer service interactions,” warns Alex Crommelin. “If your customer journey includes engaging with a chatbot, you also need to consider how you’ll let the customer escape and speak to a human. And as part of the handover to your contact centre, you need to take care that the customer doesn’t have to repeat the information they’ve already provided to the chat bot. Your business architecture should enable this information to be passed across.”

Efficiencies in data handling

AI has data at its heart, and data is critically important for decision making in the banking sector. It seems like AI and community banking are a match made in heaven.

Opportunities: improved credit risk assessment and fraud prevention

Let’s look at two areas in particular where AI can provide benefits for community banks: credit risk assessment and fraud prevention.

When assessing credit risk for loan applications, the algorithms embedded in AI tools can analyse enormous datasets – larger than ever before – quickly and efficiently.

The shorthand description of this benefit is that the larger the dataset, the more accurate assessment of credit risk, the better the lending decisions, reduced default rates and an overall improved performance for the bank.

In the area of fraud detection and prevention, AI tools can analyse transaction patterns and spot suspicious activities, with very little lag. This helps mitigate financial losses, provides a more secure, reliable banking environment for member funds.

Challenges: information security and cost of implementation

Developing and integrating AI solutions can be costly. Community banks often operate with constrained budgets compared to their larger counterparts. It may be challenging to justify the initial investment required to adopt and implement AI technologies. Costs and budget just need to be balanced carefully with the expected benefits and returns on investment, and the cost of falling behind.

The more data you’re handling, the greater the risk. More data from more sources can introduce vulnerabilities, and the integration of systems also needs to be managed with information security in mind. With AI systems handling and analysing large volumes of data, the risk of data breaches and cyber attacks is a major concern.

Whatever AI solutions you may be considering, it’s essential that your innovations comply with national and international data protection regulations, including the Australian Privacy Principles (APPs).

Alex Crommelin adds that while AI data handling can feel like an opportunity, there may be difficulties in accessing the data. “It’s important to remember that AI is only as good as the dataset it accesses. Not only are there legal, privacy and consent frameworks required, there is also the requirement to hold and structure correctly. It’s a specialist skill set that smaller organisations like community banks may not have access to.”

Operational efficiencies

Opportunities: reduced process bottlenecks

AI can play a role in automating manual and repetitive tasks with AI-powered robotic process automation (RPA). Automation can take on tasks like data entry, document processing, and compliance checks. Using AI can accelerate processes and reduce errors, contributing to a smoother customer experience.

AI can enhance back-end operations by predicting maintenance needs and identifying process bottlenecks. The ability to predict issues can reduce disruptions to services. reducing operational costs.

Challenges: skill gaps and data quality

The use of AI within the operations of a community bank requires specialised skills to design, deploy, and maintain. This skill and talent risk can be mitigated by engaging skilled, specialised digital transformation consultants to provide strategic advice and even assist during implementation and follow up reviews.

Data quality and availability may also be a challenge. Community banks are likely to have smaller customer bases and transaction volumes compared to larger financial institutions. This can result in limited historical data for training AI models. In a related challenge, poor data quality, including missing values, outliers, and inconsistent data, can lead to unreliable AI predictions and decisions.

“There’s also the consideration of whether products and services have been partnered out,” adds Alex Crommelin. “Credit cards are a good example of this. A dataset from credit card transactions is good for understanding customer or member behaviour and preferences, particularly when you link it to demographics. The data can also feed into fraud assessment and credit policy and decisions. But if you’ve partnered with a credit card service provider, there’s the challenge of getting that dataset into your own environment. Keeping in mind that a community bank may also have sourced this data management out as well.”

If you are a community bank adopting AI as part of your day to day operations or digital transformation strategy, 9Yards has the skills and experience to help you succeed. Contact our digital transformation consultants to start the conversation.