Five steps to successful AI adoption in small and medium-sized financial institutions

Defining a company-wide strategy with clear objectives

Many small and medium-sized banks and asset managers do not have the necessary expertise to identify where AI technology can be used effectively in their organizations. This is because they lack dedicated internal teams with experience in both finance and AI development. However, the potential benefits of AI, such as significant cost reductions and efficiency gains, make its introduction a strategic necessity – AI requires discussions at board level. This also ensures that AI is considered for implementation in all departments and is aligned with the overall business strategy.

First, financial institutions need to use data analytics and industry research to identify key problem areas and opportunities where AI can make a substantial contribution, such as improving the customer experience or ensuring regulatory compliance. This will then allow them to objectively evaluate specific use cases, such as customer churn prediction, automated risk management or AI-supported customer communication.

Seeking support from experts in AI technology

Small and medium-sized financial institutions with fewer internal resources can particularly benefit from external AI expertise – not only in terms of strategy design, but also in project planning, implementation and operation of the solution. Experience shows that a successful AI team is typically made up of specialists in the fields of data science, software development and product management, business analysts as well as domain experts from various banking areas.

However, it can be a challenge for financial institutions to access and retain these professionals – both in terms of the high costs and the expertise required. The recruitment costs alone often exceed the budgets of small and medium-sized institutions. This makes it difficult to attract and retain the necessary talent. In this situation, a trusted AI service provider can coordinate all these expert roles, enabling the financial institution to launch its AI project immediately and without any lead time. In this way, the institution gains access to the skills it needs for successful AI implementation.

Considering a cloud infrastructure for AI projects

Once the required expertise has been secured, the next step is to set up a suitable infrastructure. AI from the cloud offers many advantages over conventional on-premises use. Firstly, a cloud-based architecture facilitates the seamless scalability of AI projects as it gives banks and asset managers immediate access to the necessary computing resources. The cloud also makes it easy to expand AI initiatives to another market or customer segment without the need for significant investment in infrastructure. Furthermore, the cloud approach is cost-efficient because expenditure is directly linked to usage. This allows for better cost management and budgeting. Substantial upfront investment in infrastructure is not required.

In addition, cloud providers have integrated disaster recovery mechanisms that reduce the risk of downtime, data loss and regulatory violations. Finally, some of the latest AI technologies, such as natural language processing (NLP) algorithms or investment recommendation engines, are often only available in the cloud due to their special infrastructure requirements. Working with a cloud-agnostic AI service provider can help banks and asset managers select the optimal cloud provider and ideal setup for their specific business needs.

Ensuring regulatory compliance and high ethical standards

It is crucial for banks and asset managers to continuously prioritize ethical and compliance considerations during the implementation and operation of their AI. This also means complying with regulatory requirements at every stage of the project. The EU Commission’s pioneering AI Act, for example, will create a regulatory basis for the use of AI in Europe.

It calls for transparency in AI implementation and the retention of human control over machine-learning algorithms. These measures are intended to ensure that AI always works without bias or discrimination, especially in processes such as the creation of investor profiles or personalized investment proposals.

Assuring quality through ongoing monitoring and adjustments

The success of an AI project depends on more than just successful implementation. Every AI project requires ongoing maintenance, constant learning and continuous adaptation to changing business requirements. Company-wide AI success is also based on getting data quality under control as this data serves as the basis for training the AI engine. It is also important to proactively avoid errors and bias – to ensure that AI results remain fair, compliant and reliable. Financial institutions maintain the integrity and effectiveness of their AI-driven solutions by constantly assessing data quality, monitoring errors and biases, and continuously improving AI initiatives.

It is crucial for financial institutions to integrate AI into their business plans. After all, the use of AI offers numerous advantages. For example, it promises greater cost efficiency and improved decision-making processes. These days, end customers are also increasingly receptive to the idea of integrating AI into their investment processes.

According to a recent Avaloq survey, 77 percent of private investors are comfortable with AI supporting or even leading the analysis of their portfolio data. 73 percent are open to AI-supported investment advice, and 74 percent are ready for AI-based product recommendations. It is to be expected that the acceptance of AI among end customers will continue to grow, especially if their familiarity with AI-based tools such as ChatGPT increases further. Banks and asset managers that do not embrace AI run the risk of being overtaken by the competition in the near future.