AI, with the use of right mentality, wasn’t designed to replace manpower.  Instead, it augments people by creating human capital efficiency. The value proposition of AI can be applied across all functions from front to back-office roles. It enables private banks to fully leverage their data to re-design the entire customer approach and employee experience by improving operational productivity through automation and process re-engineering. Al can also proactively contribute to minimize churn well before it occurs, making it a great proof of concept for organizations to maximize customer satisfaction. In the same survey*, affluent to UHNW clients with financial advisors also revealed which factors would make them consider switching their advisor. The top reasons are as shown below: 

1) High fees 58% 

2) Weak portfolio performance 51% 

3) Lack of adaption to changing needs 43% 

4) Infrequent communication 42% 

5) Lack of transparency 41% 

6) Lack of attentive listening skills 34%

7) Reluctancy to adopt new technologies 30%

Read through the following paragraphs to understand how AI can be positioned to improve and refrain from the reasons above. 

– High fees and lack of transparency: Private banks can capitalize on AI infused solutions for automation to reduce operational costs and generate new revenue lines. They can re-examine their manual operational processes for improvements such as enabling faster client-onboarding, transaction settlements across from front to back-office workflows. By introducing low touch operations that prevent human errors and taking actions on cost-cutting allows bank to offer lower fees to their customers whilst ensuring margins. Furthermore, depending on each bank’s strategy, the customer base can now be broadened to the affluents by crafting the bank’s own robo-advisory service based on their existing investment vehicles. The AI technology can power the platform to handle risk profiling, suggest trading strategies based on the customer’s investment objectives which, in turn, builds a substantial pipeline for potential private banking clients in the future.

– Weak portfolio performance: Portfolio managers can develop their own AI “alpha engine” strategies by deploying natural language processing, big data and machine learning into their portfolio management process. For fundamental analysis, AI techniques can be trained to identify correlations between asset classes and spot stocks that may potentially outperform or underperform based on those correlations. Whilst traditional portfolio optimization focuses on quantitative analysis and hedging mechanisms, the complexity between risk and return can greatly exceed the limits of a traditional mathematical approach.  This can be easily processed and identified via machine learning algorithms. AI is also capable of solving complex optimization problems with constraints, such as restricting the number of assets or setting minimum holding thresholds to build a non-linear relationship and reduce dimensionality. Such algorithms will allow portfolio managers to apply different techniques and strategies to re-evaluate and reinforce a more thorough investment process that can lead to better performance.

– Lack of adaption to changing needs and reluctancy to adopt new technologies: Private banks collect KYC data from their customers upon onboarding.  To better anticipate client needs, AI can enrich the KYC data with third-party data as the process for machine learning, offering advisors a more holistic overview of their customer’s account. This in turn will allow them to perform deep dive analysis accompanying customers’ changing requirements. The transition will empower advisors to embark their clients into experiencing fluid, tailored and transparent banking journeys

– Infrequent communication and lack of attentive listening skills: The traditional pure relational model can be amplified to a hybrid digital human relationship through AI where human interactions only need to take place when necessary and digital solutions fill the gap when needed. It will significantly increase/release time for advisors to better serve the customer with more engagements and value-added activities to further identify and acquire potential prospects. 

In the private banking sector, the USP is ultra-personalization. Avaloq has perfected its own AI roadmap so that it not only delivers better client engagements but also leverage data points from any banking system to guarantee data consistency. Data fragmentation remains as one of the biggest challenges for most of our clients, it will be a hurdle for banks when trying to harness AI for deep learning to elevate their level of personalized client service. Based on the knowledge of patterns and insights Avaloq gained from our core banking enterprise data model – EWOM (Enterprise-Wide Object Model), we are now rolling out our next generation model to create a future-proof AI user journey. Our latest digital products are designed to extend the data model with flexibility and agility to meet private banks’ AI implementation requirements to subsequently avoid the churn scenarios highlighted above. 

To further the innovation, we also collaborate with FinTechs and form partnerships with third-party developers to create an ecosystem. The open banking environment ensures clients stay relevant and contend with the ever-changing waves of digital accelerations. We continue to work towards the path to AI adoption, in a meaningful way.  Private Banking organizations this is not a destination but rather a user journey to redefine the way they conduct business and offer a more personal touch. 

*About the survey
In May 2021, Avaloq surveyed 1,430 affluent to UHNWI with at least USD 250,000 in investable assets. Markets surveyed are the United Kingdom, Switzerland, Germany, France, China, Hong Kong, Singapore, Japan, Australia and India.

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