THE FINAL WORD TECHNOLOGY
Where do you see the best application of data analytics/machine learning in private banking? Tee Fong Seng, Pictet Wealth Management Asia AI and machine learning are the technologies leading to an accelerating digital advance, which will dislocate the financial industry and our relationship with clients. As data becomes ever more valuable, data science that uses these technologies will transform the way investment information is gathered, analysed, employed and presented. However, technology is dual-use: for financial institutions, cyber is the new systemic risk. Resistance to cyber-attacks that seek to undermine the most basic banking services will become a cardinal test of resilience, as will the security afforded by a robust balance sheet. Client concerns about data privacy will only intensify. In the future, financial institutions, already trusted as data collectors, may find a new role as data custodians. Technology will ultimately make bankers and financial experts more effective, precise and targeted in the way they work. But it is key to keep the highly personal nature of wealth and asset management in mind. It takes extensive discussion and interaction person-to-person to develop the relationship and nuanced understanding necessary to tailor solutions to each client’s specific needs. Technology won’t be able to replace that. François Monnet, Credit Suisse Private Banking Asia Pacific We knew from very early on that technology is critical to our relevance in the future. As such, Credit Suisse was one of the early movers in embracing digital innovation and we remain committed to being at the forefront of digitalisation in the private banking sector. With the support of technology and data analytics, we augment what our relationship managers can offer to our clients to deliver personalised and timely content suited to the investment appetite of each client. Consumers in Asia tend to react very positively to digital innovations. Our clients here are also younger, more tech-savvy, and more demanding of digital innovations in banking services. We are simplifying access to the knowledge and resources of the bank, so our clients can communicate efficiently with their relationship managers, identify, and act on the information that is most important to them. This reinforces our strategy of having a technologysupported relationship manager that is able to provide tailored and relevant advice to our clients. Credit Suisse’s digital strategy will continue to focus
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on providing tailored advice and self-servicing, seamless onboarding of clients and greater usage of analytics.
programme with the goal to establish data & analytics as a core capability of Bank Julius Baer, enabled by a global data platform and a data science workbench.
Technology at Credit Suisse is of paramount importance; we have a firm belief that technology will shape the future by defining the way clients consume financial services. Therefore, we will continue to harness technology to deliver a higher level of client service.
This will enable data-driven insights for decision making on client service, product offering, content sharing and operational processing — thereby providing a more personalised client experience, while creating efficiencies and reducing risks.
Cedric Lizin, Standard Chartered Bank Investment advisory is one area where data analytics and machine learning can greatly improve the quality of our advice and client service. For instance, data analytics and machine learning can provide personalised content, in the form of investment ideas or product ideas, based on clients’ risk profile, portfolio holdings, recent transactions, client investment personas and preferences, and on what other clients with similarities are interested in. AI can also be used to identify the “next best action” for clients. Anirudha Taparia, IIFL Wealth Management Ltd One of the big gest b enef its of dat a analyt ics is the abi lity to better understand client requirements and create customised solutions. In the private banking space, data analytics can be used in multiple ways. Some examples would be to identify different client personas and use (segmentation), actionable alerts on portfolios to clients and RMs (advice), product structuring-innovation, improved reporting & analytics for clients, risk control and firm governance measures. In addition to client requirements, this will lead to better risk assessment. Further, artificial intelligence and machine learning (AI/ML) tools can be leveraged by wealth management companies to improve client engagements. For example, chat bots can be used to actively engage with clients and resolve basic clients queries in an efficient and seamless manner. Andreas Zingg, Bank Julius Baer We view data analytics and machine learning as one of the primary drivers of digital transformation in private banking. Therefore, we have launched a global
The initial focus areas for our Bank are on creating more personalised insights & recommendations for clients, on mitigating risks during client onboarding and lifecycle management, and on analysing clients’ digital user journey and trading behaviour. Sonjoy Phukan, Bank of Singapore Data analytics and machine learning are already widely used for private banking processes such as client risk analyses, chatbots and client onboarding, which have helped to reap savings in both time and costs. As private banks cater to the bespoke needs of high net worth individuals and families, it is important to look beyond the immediate transactions and focus on a customer’s surrounding journey, needs and aspirations. While data analytics can be optimised for investments, it can also be applied to improve client experiences through personalisation. Using data and insights to design products and services which specifically meet a client’s individual requirements based on their lifestyles and preferences can be a game-changer. Steven Lo, Citi Private Bank Asia Pacific For us we believe the value will come from being more data-driven, especially in how we look at our client base. The data will help us understand how we can improve the client experience journey by spotting trends and/ or situations more quickly and being able to react proactively and/or appropriately. In fact, we are undertaking a massive global project in a relationship management application. That is how important we feel data analytics will be to our business. Terence Chow, RBC Wealth Management Asia We s e e t h e m o s t p r o m i s i n g applications of data analytics/machine learning in the following areas of private banking: Understanding client behaviours in order to improve client servicing, customised investment product
