Changing the Face of Financial Advice with Data and AI
At 6 a.m., my Google Home begins its morning routine by turning on my lights, showing the local weather and reading me relevant news stories I care about. It also provides a personalised update of my total financial situation, including recent transactions in my current account.
In my inbox, there’s a synopsis of curated financial stories aligned to my goals. Stories about home purchases, travel, providing education for children and planning for a future job change. It also has articles about caring for elderly parents and planning for unexpected medical costs – certainly relevant in the current times.
Later, I receive a call from my financial adviser about market volatility. She offers insights that help me better understand not only the impact to my financial situation, but how the conditions might create unique opportunities for the purchase of a new home. During the call, I inquire about learning more regarding insurance for my parents as they continue to age. The relationship I have with my adviser is unique, as she provides me truly relevant products, services and personalised experiences – all while building a deep customer relationship. Can this experience be a reality in financial services, or is this something that we might aspire to but never be able to achieve?
The reality is that consumers have come to expect this personalised experience in the world of Facebook, Apple, Amazon, Netflix and Google – all of which are known for providing personalised products, services and experiences by leveraging data. They implicitly and explicitly develop a deep understanding of who you are through continuous interactions. They apply advanced artificial intelligence technologies against data to predict behaviours, wants and needs. They tailor communications, curate content and automatically learn from interactions for continuous refinement. They learn not just from your interactions, but also from others, to predict your needs before you even know you might have them.
In the financial services space, most would agree that the amount of data collected is, by any measure, minimal by comparison. Most firms have knowledge of information provided during the Know Your Client (KYC) data collection process or from a typical Risk Tolerance Questionnaire (RTQ). Some may have insights into a client’s goals, but those are often limited to retirement, education, a rainy day fund or major purchase. Interactions, if captured in a Customer Relationship Management system, are static snapshots of a point in time, most often never to be revisited unless there’s an issue or question.
But in the age of personalised engagement, curated content and predictive behaviours, relying upon essentially the same data that’s been collected for decades is no longer enough. Clients now expect the same experiences with their financial service providers that they receive when engaging with ‘big tech’ players, and they are sorely disappointed when those interactions fall short. They expect their financial advisers to have a deep understanding of their total financial life, not just a portion of it. And they expect their financial advisers to anticipate their wants and needs, not simply rely on what they’ve been explicitly told.
For today’s financial service providers to truly meet customers where they are and deliver on expectations, they must intensely focus on data and leverage artificial intelligence or other emergent technologies to deliver even greater value for clients.
The first step: Focus on data beyond simple demographics, KYC and RTQ. Expanding the data collected on prospects and clients is critical to enabling the delivery of highly relevant solutions, customised products and experiences, as well as necessary in delivering reciprocal value. It’s easy to ask for more data, but if clients do not derive reciprocal value, it won’t be provided. The data collected should go beyond ‘structured’ information – data about the client’s current accounts, savings, pensions and taxable accounts – into ‘unstructured’ data. This is information resides in emails, notes from client conversations and qualitative insights about their goals. It’s information about family, life concerns, lifestyle, needs and wants. Too often, this information is collected during an initial client meeting but then tossed aside in lieu of data points necessary to open an account, move funds, process a transaction or meet regulatory requirements.
Once the data is collected, it’s critical to apply advanced artificial intelligence technologies to synthesise it and provide meaningful insights, personalisation and productive analytics. Technologies such as Natural Language Processing can read and synthesise emails, client’s notes, news sources and product marketing materials. Predictive analytics can provide insight on life events that may impact the client’s financial situation, leveraging Machine Learning (ML) from the adviser’s entire book of business combined with external data. Technologies and tools exist today that enable advisers to provide curated content that leverages both structured and unstructured data. When the curated content is provided to the client, the system ‘learns’ – through ML – what the client values and continuously adapts its content to meet the client’s expectations. The advisers are equally informed of this learning and better equipped to provide relevant products and services, while the system also ‘learns’ through the entire lifecycle of product and service engagement, client behaviours and adviser interactions.
These unique insights will create implicit value for clients, and more importantly, transform the client relationships from being ‘transactional’ to ‘consultative,’ creating enhanced shared value, differentiation, increased wallet share and improved client retention. Learning from ‘big tech’ is about discovering how to deliver truly relevant and customised products, services and experiences through data and advanced data science. These capabilities supercharge the financial adviser, enabling them to build deep client relationships while equally delivering the value customers expect in the ‘big-tech’ world.
By Russ Kliman, Global Leader of SEI Ventures, SEI’s corporate venture capital program.
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SEI is a global provider of investment processing, investment management, and investment operations solutions. The company was founded in 1968 and is based in Oaks, Pennsylvania.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.