By Gery Zollinger
Artificial intelligence (AI) has gained significant attention lately, with numerous use cases emerging within the financial sector. Financial service institutions (FSIs) are leveraging AI to support client interaction, create personalized recommendations based on large data volumes, and accurately identify discrepancies as part of anti-money laundering (AML) and fraud prevention efforts.
These developments are also reflected in the budget projections of the region’s FSIs, with an IDC study revealing that almost two-thirds of banks in the Asia Pacific are planning to increase their budget for AI solutions.
In Singapore, IDC projects that overall technology spending on AI across major industries is expected to be over US$3.5 billion (S$4.6 billion) by 2026, almost three times the US$1.2 billion in 2022.
However, the beneficiaries of AI technology and its capabilities in the finance sector have mainly been larger banks and wealth managers.
Despite the potential upside, small and midsize FSIs risk falling behind in their AI adoption journey due to a variety of factors. These include having insufficient datasets to train AI, poor quality data, a lack of financial resources to invest adequately in AI technology, and inadequate in-house expertise to implement and manage AI applications. To ensure that FSIs of all sizes can leverage AI across their organization, here are five essential steps to consider.
1.Outline a company-wide strategy with clear objectives
Many small and midsize FSIs do not have the expertise to identify where AI technology can be effectively deployed within their organizations, brought about by a lack of in-house experts with experience in both finance and AI development. However, the potential advantages such as cost reduction and efficiency gains make AI adoption a strategic leadership priority, ensuring that any AI strategy cascades across the organization and aligns with overall business strategy. FSIs first need to identify where AI can make a significant impact such as improving client experience and ensuring compliance. Then, FSIs can consider applications such as client churn prediction, automated risk management or AI-powered client communication.
2.Seek AI expertise
Small and midsize FSIs with fewer in-house resources can particularly benefit from the AI expertise of managed service providers (MSPs) for support with designing strategy, project planning, implementation and solution operation. An AI team in a large bank would typically comprise data scientists, business analysts, software developers, product managers and domain experts from various banking functions. However, finding, hiring and retaining talent can be expensive, often surpassing the budgets of small and midsize FSIs. By partnering with an MSP that has AI expertise, FSIs can roll out their AI projects more quickly and reliably.
3.Leverage the cloud
Deploying AI in the cloud offers many benefits compared to a traditional on-premises setup. Cloud architecture facilitates seamless scalability for AI projects by providing instant access to the required computing resources for banks and wealth managers. By leveraging the cloud, FSIs can easily expand their AI initiatives to another market or another client segment without significant infrastructure investments. The pay-per-use model of cloud deployment is more cost effective and allows better budgeting and cost management without significant capex. Cloud providers also offer built-in disaster recovery functionalities that reduce the risk of downtime, data loss and regulatory breaches. Finally, advanced AI technologies such as natural language processing (NLP) algorithms or investment recommendation engines, are often only available on the cloud due to their specialized infrastructure requirements.
4.Prioritize regulatory compliance and high ethical standards
Compliance and ethics must be at the core of any AI project throughout the entire implementation and operation journey. The Monetary Authority of Singapore (MAS) has published a paper on Fairness, Ethics, Accountability and Transparency to promote the responsible deployment and use of AI within the finance industry, with recommended principles to adhere to when deploying AI. These principles aim to ensure that AI can always operate fairly and ethically, and is accountable and transparent, especially during processes such as investor profiling or making investment recommendations.
5.Ensure ongoing monitoring and adaptation
The success of AI projects requires ongoing monitoring and adaptation to evolving business needs. This means maintaining quality datasets, as they are the basis upon which AI engines are trained. It is also important to proactively prevent errors and biases to ensure that outcomes remain fair, compliant, and reliable over the long term. Through continuous evaluation and data quality, monitoring and correcting errors and biases, and consistently improving AI initiatives, FSIs can maintain the integrity and effectiveness of their AI-driven solutions.
Integrating AI into an FSI’s business strategy is critical in today’s competitive landscape. AI can help them realize benefits such as better cost efficiency and data-driven decision making. End clients too are increasingly receptive to the idea of incorporating AI into their investment processes and journey.
According to a recent Avaloq survey, 77% of individual investors expressed comfort with AI supporting or leading the analysis of their portfolio data, 73% are open to receiving AI-supported investment advice, and 74% are willing to receive AI-assisted product recommendations.
At Avaloq, we anticipate a growing acceptance of AI, especially as individuals become more acquainted with AI-augmented tools like ChatGPT. As AI continues to grow in acceptance, failing to adopt AI may leave small and midsize FSIs susceptible to being outpaced by competitors and fintechs.
Gery Zollinger is head of data science and analytics, Avaloq
