The banking, wealth management and financial services software and systems business Avaloq casts its eye over what AI means for those trying to put this technology into practice.

There’s a great deal of appetite among wealth managers for learning how to apply artificial intelligence to their businesses in order to boost the bottom line and profit from opportunities. A company that is well placed to consider the ramifications of AI, and wealth management, is Switzerland-headquartered Avaloq. In this article, Gery Zollinger, head of data science and analytics at the business, gives an overview. The editors are pleased to share these insights and invite readers to respond. The usual caveats apply to views of guest writers. Email tom.burroughes@wealthbriefing.com

Wealth managers are facing higher operational costs, margin pressure, expenses associated with digital transformation and regulations, such as the recently introduced Consumer Duty. Against this backdrop, many firms are turning to new technology as they seek to comply with rapidly evolving regulations and gain a competitive edge.

Technologies such as generative artificial intelligence (AI) are already transforming wealth managers’ operations, reducing manual processing, improving data analysis and increasing operational efficiency through the automation of routine tasks. This is especially the case in the back office where the ability of AI to sort vast quantities of data, automate repetitive processes and accurately identify outliers is proving particularly valuable. 

But now, we are also seeing increased AI adoption in the front and investment office, where wealth managers have traditionally been reluctant to incorporate AI. The technology is supporting wealth management firms by augmenting the role of relationship managers, allowing them to serve a larger, more diverse client base – including the underserved mass-affluent segment. In client lifecycle management, for example, network analytics software can automate prospect mapping and churn prediction engines, alerting relationship managers when a client is at risk of leaving the firm. This AI adoption is increasing the productivity of relationship managers, giving them more time to spend building relationships with a larger number of clients.

Despite these benefits, many smaller and mid-size firms struggle to benefit from AI advances due to limited resources. Fortunately, there are several measures that firms can take to avoid falling behind the curve when it comes to AI adoption. 

Establish a clear strategy 
While AI brings undeniable benefits to organisations, wealth management firms may lack the knowledge to determine its optimal application within their business. With AI having the potential to optimise costs and increase efficiency across the organisation, discussions on adoption must be held at board level to align it with the firm’s overall business strategy. Firms should consult AI experts and research to establish where AI technology can be of most benefit to their business, for example in areas such as risk management or client churn prediction. But successful AI adoption extends beyond simply selecting the right technology. It relies on ongoing monitoring and maintenance, especially as new solutions are constantly emerging. Firms must also monitor the quality of any input data used in the training of the AI technology and ensure that it is free of errors and bias which could hinder the AI’s reliability, increasing the risk of compliance issues.  

Look to the cloud
When adopting AI technologies, wealth managers must also consider which hosting environment is the most suitable. Since on-premises infrastructure can quickly reach capacity, cloud-based architecture is often needed for AI applications. There are a number of advantages to this approach. Firstly, the on-demand resources of cloud-based infrastructure enables wealth managers to rapidly scale their AI projects for new markets or client segments, without significant upfront investment. Cloud-based infrastructure also gives wealth managers added security, as providers offer built-in disaster recovery mechanisms. Finally, the latest AI technologies, including generative AI, are often only available on the cloud, as they require specialised infrastructure. 

Keep up with compliance
While AI provides wealth managers with numerous benefits, including enhanced operational efficiency, relationship manager productivity and improved data analysis, compliance and ethics need to be kept top of mind. Regulators around the world, such as the EU Commission, are beginning to increase their scrutiny of AI, introducing regulations to ensure ethical use and to prevent discriminatory decision-making. It is essential that wealth managers prioritise compliance with these evolving regulations to ensure the smooth integration of AI and to avoid potential bias in their operations.

Ask the experts
Successful AI projects require input from a range of experts, with business analysts, software developers, data scientists, and product management specialists all essential for supporting wealth managers across the entire AI integration progress. Yet sourcing and retaining such talent in-house is not an easy task, with the costs required to hire such experts often well beyond the budgets of small and mid-size firms.

One solution to this is for wealth managers to outsource the service by partnering with an AI service provider that already has an established team in place for quick access the best AI talent and expertise. 

The outlook for AI in finance 
The wealth management industry’s AI transformation is only just beginning – and we expect to see more and more solutions available in the coming years. However, the biggest change on the horizon is new regulation on the use and ethics of AI. A prime example is the pending AI Act by the EU Commission, which will set out clear guidance with respect to fairness, verifiability and non-discrimination. We believe that this guidance will give decision-makers the regulatory confidence to implement value-adding AI tools to leverage their vast datasets, which will ultimately boost innovation in the financial sector.