Role Of Data Analytics In Wealth Management

The financial crisis of 2008-2009 shook the financial industry to its core, driving corporations to become more adaptable than ever before. With the rise of data analytics, the need to manage an ever-increasing volume of information while responding quickly to market shifts and regulatory requirements presents challenges for wealth management firms, particularly in the face of rising overhead costs, shrinking IT budgets, and customer demands for greater personalization.

The amount of data produced each year is increasing at an exponential rate. In the last two years, more data has been produced than in the whole history of human mankind. According to a September 2015 blog article by Forbes technology writer and big data specialist Bernard Maar, around 1.7 gigabytes of information will be produced every second for every individual in the world by 2020. To be competitive, organisations must organise, understand, and act on data in real-time, while the volume of data grows exponentially. Business executives’ capacity to recognise patterns in a vivid, pictorial context is enhanced by data visualisation approaches. It also enables them to detect and comprehend significant connections as well as recognise fresh chances.

The visualisation of data in a clear, visual context that helps consumers better comprehend and absorb the relevance of the data is known as data visualisation. Using digital visualisation tools to study data and track key performance indicators allows you to stay on top of your clients and peers. Managers, sponsors, and sales teams may more effectively plumb the data and understand how to use it using digital visualisation approaches. Managers, for example, may want to know how productive their sales teams are, while sponsors may want to know how widely their data is distributed across managers. One central database allows various users to access the same information in ways that match their needs by allowing diverse audiences access to the same information.

According to Investopedia’s affluent millennial poll, financial advisers are the most trusted source of investing advice for roughly 65 percent of millennials. The public’s exposure to the newest data management advances has increased the volume and complexity of data. As a result, data management and mining become more complicated. This clearly demonstrated a variety of advantages that the data visualisation process may provide, such as:

  • For an accurate “big picture” view of customer demands and financial trends, derive insights from structured and unstructured data sources.
  • Predict customer behaviour and investment trends using sophisticated predictive analytics.
  • Have a transparent sense of operational status in the business.
  • By making regulatory compliance more efficient, you can save money and streamline your operations.
  • Data from any system can be integrated and delivered in real-time.
  • To make custom-tailored offers, gain a better understanding of customer preferences.
  • Provide interactive reports and analysis tools via dynamic web and mobile portals.
  • Customers should be able to self-serve their accounts from any device.

Predictive Analytics for Business Growth

“Big data and advanced analytics are on the verge of transforming the wealth management industry, with new ways to engage with new clients, manage client relationships, and manage risks,” according to Deloitte. The industry is transitioning from insight to foresight – from knowing what is happening to anticipating what may or will occur. Predictive analytics can help a good company become even better, and a great company becomes even better.

Advanced analytics has an impact on every stage of the customer acquisition funnel and relationship today:

  • Getting New Clients: Create comprehensive prospect profiles, map relationships, identify new markets, and generate better leads using( internal and external data.
  • Sales to new and current clients: You may develop Net Worth and Share of Wallet profiles, calculate a client’s potential lifetime worth, and assess risk tolerance for various types of funds and advise by linking transaction and channel data with market events.
  • Client Recommendations: Client surveys and other data sources are used to tailor portfolio allocations and provide real-time trade/investment ideas based on preferences and market events. Enhance Q&A capabilities and human-to-machine communication channels.
  • Existing Client Supervision: Compare personality and investment profiles with investment and trading activities on a regular basis to assess the suitability of wealth management approaches in order to keep each client optimally positioned.
  • Client Retention: Gather and compare client channel and social data to stay informed about their current satisfaction, risk tolerance, interests, and connections, resulting in increased retention and referrals.

Companies utilise predictive analytics at every stage of the funnel to measure and implement crucial business results and key drivers such as client segmentation, advisor books, and product penetration, as well as to evaluate the acceptance and efficacy of tools and processes.


Wealth managers continue to face a variety of industry dynamics, from increased regulation to new digital delivery channels, shifting wealth demographics to fee pressure, and now cyber security threats. Firms are launching new initiatives to stay competitive and compliant, such as rolling out alternative investment products and creating a single client identifier. These advancements increase data volume and complexity, making data management, maintenance, and mining more difficult. As wealth managers look to expand globally, improve sales effectiveness, and add robo-investing capabilities, the need for high-quality data has never been greater. ​