How to Use Data to Drive Business Efficiency and Boost Revenue of Your Wealth Management Firm

In today’s wealth management sector, data is being leveraged to drive business value, improve efficiency, and boost revenue. However, the adoption of data-driven practices remains inconsistent, with quality issues impacting company performance.

This article explores the current use of data in wealth management, the potential improvements if data is utilised effectively, and the challenges faced in doing so. It also explains how we empower firms to maximise the benefits of data, establish a strong data foundation, and unlock true business value in the digital era.

The Role of Data in Wealth Management

Ultimately, most wealth managers are looking to leverage data to achieve one thing; to increase the value of their business. This is no doubt a crude perspective, but in the final analysis wealth managers are capitalist ventures funded by investors – whether private or public – looking to make a return on their investment.

Whether it is using data to drive product recommendations, identify at risk customers, or automate AML or KYC processes, the ‘lower level’ initiatives commonly found within technologically aspirational wealth managers are generally steps towards that overarching business value goal, which can be supported in one of two ways:

1. Increased efficiency / reduced cost
2. Increased revenue / improved sales

The more interesting questions, and the primary focus for this article, is how are successful wealth management firms currently using data to support (1) and (2) if there are shortcomings, what can be done to address those shortcomings?

The status quo

The current success of wealth managers in effectively using data to support improvements in business efficiency and revenue can be summed up in a single word; inconsistent.

In any industry, the successful use of data is derived from how well a firm’s data measures against three fundamentals:

• Data scope: Essentially, a measure of the depth and breadth of data captured, or, in other words, the richness of data as determined considering, principally, the number of useful data points per record as opposed to the absolute volume of records.
• Data consistency: The consistency of the data captured, considering the percentage of data points completed per like record and consistency of method of capture for a given attribute(s) (For example, always capturing date of birth or address in the same format).
• Data accuracy: The quality of the actual data captured, considering the degree to which the data is free from manual error on entry, the frequency of data updates and minimisation of duplicates.

Whilst there is no doubt some firms are scoring very highly on all these factors; few would claim this to be commonplace. A survey by AI Data & Analytics Network, assessed the quality of firms’ data, 77% listed quality issues and 91% stated that it’s impacting their company’s performance. 67% of CEOs indicate they often prefer to make decisions based on their own intuition and experience over insights generated through data analytics because they lack trust in the data.

The financial industry, particularly wealth management, has been relatively slow in adopting data-driven practices. According to McKinsey, advisors spend almost 60% – 70% of their time on non-advisory activities due to lack of automation. Wealth management firms need to invest in technology which can automate, streamline and, crucially, support a strong scoring against the three data fundamentals. Through, by way of example, solutions for systematically controlled data input, automated validation and coherency checks or elimination of rekeying.

The hard truth

It is interesting to compare the above observations on the relative data immaturity of wealth management with another observation; the growing use of data-driven insights and analytics in the financial services industry:

• According to PwC, wealth management firms are using data to segment its client base and personalise its services. By analysing client demographics, financial behaviour, and investment preferences, firms tailor their offerings to meet individual needs more effectively.
• It is becoming increasingly common for banks to use cloud-based solutions to store, process, and analyse large amounts of data to reduce costs and improve scalability (Forbes).
• Artificial intelligence (AI) and machine learning (ML) are being used to detect and prevent fraud. Machine learning algorithms can analyse transaction data to detect patterns of fraudulent activity, as well as behavioural biometrics, such as fingerprints and facial recognition (Forbes).

Whether it is in response to a pervasive media narrative on the power of ‘Big Data’ and ‘AI’, or the threat to incumbents of a new wave of FinTech enabled innovators, one might reasonably ask if there is a risk that the industry is scrambling to reach the end game before putting the data foundations in place.

Where data quality and consistency are not there, the use of advanced analytics and insights can in fact do more harm than good, with the potential to produce false or misleading insights on which critical business decisions are made with a false sense of confidence. In this sense, advanced analytics are potentially a ‘double-edged sword their transformation potential is real, but so is the risk to the business. In short, firms need a data strategy as well as a business strategy.

Steps to take

It is important to re-emphasise the variation across the industry and that there are firms getting it right; putting the technology in place to support the sort of data maturity that enables advanced insights and analytics tools to provide a very real competitive advantage in the common drive to continually improve efficiency and increase revenues. For firms that are perhaps a little further behind on their journey, there are many positive actions that can be taken, including:

Higher priority: short to medium term

• Ensure there is a known single source of data for all data attributes.
• Review systems landscape to reduce the total number of systems and data repositories to a minimum.
• Enable the validation of data on entry (field by field).
• Ensure data points are captured and structured to support reporting and analytics requirements without the need for manual data wrangling.
• Pre-populate documentation using integrated documentation along pre-defined business processes to minimise data rekeying.

Lower priority: longer term

• Introduce data quality and consistency KPIs, supporting both ‘point in time’ and ‘cross-time’ analysis, in addition to identification of key areas for further improvement.
• Introduce more sophisticated cross-field data coherency checks.
• Enable self-service solutions to encourage frequent update of data and reduce the internal overheads.

Getting the foundations right

The importance of a firm getting its data foundation right before attempting to introduce ambitious intelligence layers on top of that data cannot be overstated. To do otherwise is, unfortunately, an all-too-common mistake for firms feeling the pressure to do ‘something’ in response to the noise on ‘Big Data and AI’. It is to build an edifice on sand.

When introducing new technology solutions with the intention to reap the full benefit from the data held within their databases, firms must be conscious of three things:

1. Ensuring initial data migration is with clean data.
2. Ensuring that the solutions ensure new data is consistently and accurately captured.
3. Ensuring the solution supports the ongoing maintenance of data in a best practice fashion.

Wealth Dynamix supports all these pointers, with data migration (1) being a key focus point within solution implementation and with (2) and (3) being concepts not only reflected into the bedrock of the products but also carefully considered in the introduction of a new feature or the evolution of an existing one.

To highlight some key capabilities in this area:

• Data entry controls – Validation of data at the point of entry on a per field basis and on a cross-field coherency basis.
• Data amendment controls – Controlled processes for the amendment of data, including appropriate rule-driven document capture and approval steps.
• Periodic reviews – Automatically scheduled periodic reviews of client data files, combined into wider regulatory-driven review processes.
• Advanced data mastering rules – Configurable mastering rules with the ability to automatically switch mastering based on client journey stage; for example, to master certain prospect data attributes within the CLM and then, post-onboarding, to master the client data attributes within the core-banking system.
• Intelligent data re-use – The concept of ‘capture-once, use-many’ not only avoids client frustration by asking for the same data multiple times, but also supports data consistency by ensuring an individual or entity’s data can be reused across all their client relationships.
• Data interfacing – Comprehensive API and file-based interfaces that facilitate seamless data flow between CLM systems and downstream systems. This enables automated processing of all data mastered in CLM and allows for easy integration of externally sourced data into the CLM system.
• Self-service capabilities – Self-service portals that streamline the onboarding and ongoing servicing processes. These portals aim to minimise obstacles and make it as quick and easy for the end provider of the data.
• GDPR & data privacy – Solutions to ensure that there is a lawful basis (consent or otherwise) for all personal data held and processed, combined with processes for the management of automatically identified exceptions.

Delivering true value

By supporting firms in establishing a foundation of rich, consistent, and accurate data, Wealth Dynamix’s solutions put firms in a position where they can be confident that the layering of data-driven intelligence will deliver genuine business insight and business value.

To highlight some key capabilities in this area:

• Market-leading reporting & analytics tools – Wealth Dynamix seamlessly embeds Microsoft’s PowerBI reporting and analytics technologies into the solutions. The underlying technology has been recognised by Gartner as the most complete, innovative offering on the market for the 4^th year running.
• AI-driven insights – As well as supporting the self-configuration of insights and analytics, Wealth Dynamix’s solutions also support AI-generated insights. This solution allows the system to automatically identify and visualise trends in the underlying data that may otherwise be difficult or extremely time consuming to achieve. Additionally, the products can support the natural language querying of data, with insights and analytics automatically suggested and visualised in response.
• Notifications & calls to action – Rules-driven notifications and calls to action generated on a per-record and cross-system basis, with automated classification and prioritisation.
• Product recommendations – AI-generated product and service recommendations (developed in partnership with DreamQuark), with configurable inputs including propensity matching, suitability alignment, available funds, recent engagement and current sentiment.
• Intelligent segmentation – Wealth Dynamix’s products intelligently and accurately segment prospects, clients, and other intermediary contacts to allow firms to identify (for example):
  • Highest value clients – Considering such inputs as assets under management (AuM), total wealth, length of relationship, number of connections (to other prospects or customers) and total fees generated.
  • Clients at risk – Considering inputs such as sentiment score, level of digital engagement, level of direct engagement and key themes across recent communications

Built for the long-term

The possibilities of what can be done with good data are growing at an alarming rate, as are the practical applications of that theoretical possibility by leading firms within the financial services industry.

Our solutions are not just beneficial to the front-line advisors but to everyone within the firm (e.g. middle office, back office, management, compliance). With all teams and roles on a single platform and sharing the same data set, collaborations become seamless and processes more efficient.

Are you seeking to create an extraordinary digital-first client prospecting, onboarding, and servicing experience for your wealth management firm?

If you would like to learn about how we can assist, please don’t hesitate to get in touch.