Data: The competitive advantage in wealth management

Private banks and wealth managers possess a true treasure trove of client data. To gain a true competitive edge, however, it has become crucial for financial institutions to harness this data and turn it into knowledge about the exact needs of clients.

September 20, 2021

Private banks and wealth managers possess a true treasure trove of client data. To gain a true competitive edge, however, it has become crucial for financial institutions to harness this data and turn it into knowledge about the exact needs of clients.

Financial institutions have always had the advantage of customer’s entrusting them with their data. Today, in the wealth management sector, holding this data has a double benefit: automated data analysis and artificial intelligence (AI) can reduce the workload of advisors while improving the client experience. This is allowing advisors to provide a much more personalised advice built around a customer’s unique preferences and needs – making wealth management more efficient, needs-based and intensive.

Pure robo-advisory models with fully automated digital advice usually address entirely new client segments – especially the mass market. In contrast, the traditionally wealthier clients at private banks and wealth advisors still place great value on the personal component in their advisory relationship. Here, AI and data analytics can play a decisive role in increasing the efficiency and quality of advisory activities.

NLP and the rise of conversational banking

In addition, the number of channels through which affluent customers are keen to communicate with their advisors today has multiplied due to digitalisation. Today, a digitally savvy generation of clients expects to be able to communicate with their wealth manager or private banker through channels and messaging platforms such as WhatsApp, WeChat and Signal.

In principle, this digital generation wants access and recommendations for products and services that match their ideas, investment strategy and risk profiles as closely as possible, at any time and in any way.

Innovative AI solutions for Natural Language Processing (NLP) have become especially helpful in supporting advisors since they understand natural language.

By using NLP, an advisory system can capture a client’s request, regardless of the channel through which it arrives. It can then support the advisor in providing a timely response – for example, by immediately showing the status of the client’s portfolio or even suggesting specific answers to the client’s query. Digital support empowers advisors to respond in near real-time. As such, AI and NLP create the conditions for moving to continuous interaction with the client without unduly burdening advisors.

In the near future, speech-to-text solutions for relationship managers and their clients will also be applicable by translating spoken language to text form. Here, it will be important to train the AI solutions that do this translation work specifically for the needs of the financial industry.

All these new forms of communication – whether via social media, messaging platforms or smartphone app – are usually bundled under the term ‘conversational banking’.

In addition to all the data that a financial institution has on its customers, conversational banking also provides a wealth of relevant information about the customers. Evaluating all this data using NLP and AI helps an institution gain further customer insights – which in turn enables a more targeted personalisation of offers and suggestions.

Segmenting clients and personalising investment proposals 

A particularly significant aspect of current best-practice data analysis platforms is being able to segment clients and personalise investment proposals. Another is being able to react in a very timely manner not only to the communication processes initiated by the customer but also on occasions that arise due to external factors such as company announcements.

Advisors can see all relevant market alerts in a dashboard in almost real time – and, at the same time, recognise which specific portfolios, assets or client segments these messages are relevant.

Transforming the previous, traditional business model of an institution into a new, data-driven one requires commitment and resources. The strategic importance of digitalisation requires dedication and prioritisation at the highest level. The benefits of implementing a data strategy should also be clearly defined. There can be user cases in numerous areas of an organisation, from the front office to the back office. Only when these parameters are established – for example, to improve upselling or reduce churn – the results become measurable and tangible.

Fast Data and Smart Data bring true agility

Another important technological trend is the transformation to Data-as-a-Service or Data-as-a-Product. With the help of data virtualisation, these approaches enable a lean, business-driven view of data and significantly simplifies access within a financial institution. Concepts such as Fast Data and Smart Data are also poised to make data use even more agile. Both are advancements over traditional Big Data approaches, which deal with very large amounts of data but have less agility.

The primary goal of Fast Data is to make data available very quickly, often in real time, to a wide variety of places in an organisation. Avaloq’s live dashboard support for wealth and investment advisors is a good example of Fast Data application. Smart Data, on the other hand, aims to overcome disadvantages of a Big Data approach by extracting sub-areas using specific algorithms. In this way, specific, user-appropriate views of the data become possible. 

Compliance officers, on the other hand, need a different view of the available data. For them, data analyses and AI serve to identify fraud cases more easily or to minimise the number of false positive suspicions. Instead of always checking customers against sanctions lists, for example, unstructured public messages from external sources can also be used thanks to NLP to monitor customers and minimise risks.

Whether Fast or Smart Data – both approaches are supported by a modern data lake architecture that uses a flexible data model with different business domains. This pool of data, which replaces old, siloed structures in the organisation and centralises data, can include structured, semi-structured and unstructured data, including external messages and communications.

The data-driven investment advisory

Data analytics, artificial intelligence, and especially NLP as a sub-domain will shape the future of the wealth management industry and allow new customer service models such as hybrid advice models. Financial institutions that embrace the data-driven future will identify promising leads, exploit upselling potential and increase revenue as a result. Companies will also be better equipped to avoid risk, facilitate compliance and increase efficiency.

According to Avaloq’s latest industry survey “Front-to-Back Office Report”, 73 per cent of end investors consider artificial intelligence, robotics and automation to be the major trends shaping the future of the industry. And rightly so. Making good use of all available data is becoming an indispensable competitive factor in the wealth management industry.

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