Cleaning up client data

As tax authorities across the world increasingly move towards automatic sharing of citizens’ financial data, wealth managers cannot afford to make mistakes with the information they hold on their customers
by Paul Golden


In 2006, UK mathematician Clive Humby described data as the ‘new oil’ because of its value. Governments across the world have recognised its potential in revenue collection, with countries as diverse as Estonia, Italy, Ireland, and New Zealand introducing pre-population of tax returns using data sourced from third parties.

While it is HMRC’s longer-term ambition, under a programme called Making Tax Digital, to have the personal tax returns of wealth managers’ clients automatically populated by data uploaded by the manager, the UK still has some way to go to reach that stage, notes Ali Kazimi, managing director of tax specialist firm Hansuke Consulting.

In the meantime, it is worth remembering that data, like oil, is only useful once it has been cleaned.

New products and technology “increase data volumes and complexity, making it harder to manage, maintain and mine data”, says a 2016 Deloitte report, Data challenges in wealth management. The report states that “gaps in data fields are particularly prevalent in client profile information”, which “may cause client service issues as well as pose a significant regulatory risk”. Incomplete or inaccurate customer data also creates problems for wealth managers seeking to automate the client onboarding process for digital services such as robo-advice, it says.

Disconnected data

Data alignment is not easy to achieve for wealth managers and is even more difficult for those firms that are still working with legacy back-office technology systems, cobbled together and disconnected.

“Some firms are still working this way and need to change,” says Meghna Mukerjee, senior analyst at research and consulting firm Aite-Novarica Group. “Even incremental changes made strategically are better than nothing. There is a clear need for a client-centric data infrastructure that connects front-end and back-end operations.”

Meghna is referring to the several different types of data points that go across the entire infrastructure front to back. One of the most important is data relating to banking transactions processing, which is handled by the core banking system at the back end but matters across the value chain.

Wealth managers who want to ensure their client data is accurate and up-to-date need to consolidate data from multiple sources into a single ‘golden source’, explains Ali. He says that some wealth managers have invested in optical character recognition technology (which converts images of typed, handwritten or printed text into machine-encoded text), but this is not infallible, and there have been instances where the technology has mixed up addresses.

“They also need to take account of issues around naming conventions,” he says, explaining that it’s “not always easy to identify the first name, middle name and surname of a client”.

Operational changes

According to James Alexander, a director in Deloitte’s consulting practice, wealth managers could start by defining the different types of data they hold (such as investment and administrative) and creating clear and robust data strategies, governance and policies around collecting, maintaining, and managing data, and business processes to capture and record appropriate levels of client data across each interaction.

Key aspects of a data strategy are to create a customer engagement platform to manage interactions with existing customers and prospects across all segments, products and channels, connecting the information necessary to support automation, customer experience and actionable insight using the full data set to take action on what matters most to customers.

“For customers this means interactions can be personalised utilising every bit of information to help them make the best decisions possible, while for wealth managers it means a simpler data set and a single source of truth,” says James.

Managers also need to leverage appropriate technology for efficient storing, accessing and analysis of data and ensuring that customers have a clear, simple and frictionless way to manage their data and preferences, he says. “Only once these processes are in place can firms start to think about leveraging this data to drive better client outcomes and improve efficiency.”
The ability to combine data from across the firm to create a single client view is essential

Deloitte is still at the stage of putting in place the foundation of a good strategy for most of the wealth managers it works with, and deploying appropriate systems and technology on a function-by-function basis, without yet thinking about integrating data from different systems.

The ability to combine data from across the firm to create a single client view is essential to enable more granular client segmentation and personalised reporting, driving better client experience and more effective engagement and conversion of prospects.

“Wealth managers are challenged by the risks and costs of a full transformation programme across their whole business,” says James. “However, with the use of the APIs [application programming interfaces] and microservices, these programmes can be broken into more manageable projects. By adopting an iterative approach to transforming these services, wealth managers can reduce the risk and realise business value more quickly.”

Data wrangling

Wealth managers need to make data available to different systems within the organisation, which is where data wrangling – combining and blending data from multiple sources and preparing it for analysis – comes into play.

The data “needs to be deduplicated, enriched, filtered and then joined to create a single data set, with machine learning algorithms used to resolve missing links”, says Awaad Aamir, a wealth management analyst at financial research and advisory firm Celent.

Awaad outlines three major implications of basing investment decisions on inaccurate, incomplete, or out-of-date data.

“First, compliance and monitoring systems won’t work effectively and could generate serious legal liabilities – for instance, overlooking the necessary RegBI disclosures [the US Securities and Exchange Commission’s Regulation Best Interest code of conduct] in the US could result in penalties,” he says. “Second, the manager would be unable to identify dormant or low-activity accounts/clients and third, the manager would be unable to automate its portfolio performance analysis.”
“Good data is essential in having a clear view on what is going on across a firm”

The negative impact on client loyalty is potentially even more damaging as wealth managers will end up losing clients if they misunderstand their risk appetite or investment profile and if the clients take hits on their portfolios due to inaccurate internal data usage.

“Good data is essential in having a clear view on what is going on across a firm, especially in keeping on top of what advisers are doing with their books of clients,” says Meghna. “No two clients are the same, so identifying patterns of behaviour and specific needs can enrich the adviser’s relationship with their clients.”

David Bailey, chief operating officer of RBC Wealth Management, says the firm validates data in several ways throughout the client lifecycle.

“We work with clients to validate that the data we have collected is appropriate and accurate at the onboarding stage,” he says. “Where relevant and possible, we validate the data against third party or open-source verification data sets.”

For example, RBC Wealth Management uses World-Check databases for client verification and Fircosoft for screening payments, both of which screen for risks associated with individuals and organisations.

Any change of circumstance is a trigger event for a data refresh, while the firm performs periodic data quality checks within its record-keeping systems. These are either completeness checks, for example, ‘Do we have a data element that we need or would like to have?’ or validation checks.

David acknowledges that it is almost impossible to find a technology solution to solve all the data requirements of a wealth manager. Rather, application layers are solving specific challenges with technology that automates the data flow between applications to service a particular client need.

For example, during client onboarding, RBC uses a workflow application to manage the data through the process to ensure it has transparency of what data it has, where it is in the process, and how long it has taken. “This allows us to move data between core applications we use for client and product management,” explains David.

In this scenario, a wealth manager might have a client relationship management application; an administration engine for investment management, custody and banking applications; and support systems for risk management, decision support or analytics.

The benefits of making investment decisions based on accurate and complete data include better value for the client, less operational drag for the organisation, lower risk, and better operational change outcomes, says David.

Consequences of bad data

“There is a large hidden inherent cost of moving data around any organisation,” he adds. “Good quality data effectively reduces operational risk, mitigates reputational risk arising from operational failures, suitability issues and regulatory sanctions, and improves business decision-making.”

There are also compliance considerations, such as the requirement for accurate client information under regulations and directives such as anti-money laundering (AML), explains Hansuke Consulting principal Ahmed Nawab.

“Someone in the wealth management firm will have to declare that the information they pass to the tax authority is complete and accurate,” he says. “In the case of North American reporting, under the Foreign Account Tax Compliance Act and qualified intermediary regimes this is done under the penalty of perjury, so it is vital to get the information right.”

Most managers will have an AML screening process that validates their data against a real-time list of appropriate sanctions such as travel bans and asset freezes. However, the key data points they test against will have varying ‘time’ attributes and the processes used to capture and validate the qualitative attributes of the data should reflect this.

David expects data to be increasingly collected from open sources with less of a reliance on manual input before being sent back to the appropriate person for verification. “This allows more standardisation of definitions, greater focus on outcomes rather than inputs, and an improved client experience,” he says.

Firms can and should be using multiple sources of data to make sure that they are not in relationships with customers or advisers who could expose them to regulatory, financial or reputational damage or penalties.
“Firms need to know who their clients are, when their clients’ situations change and if that impacts their profiles”

“Wealth managers already use pre-trade compliance technology to automate monitoring procedures, including regulatory and client guidelines, while post-trade compliance systems monitor activities after trades are completed,” says Awaad. By using ‘big data’ tools, managers are able to aggregate and analyse large sets of structured and unstructured data for compliance and fraud prevention.

“Firms need to know who their clients are, when their clients’ situations change and if that impacts their profiles in terms of know your customer or AML provisions so that any red flags can be picked up in a timely fashion,” adds Meghna. “The right measures – and dynamic information-gathering capabilities – are critical to remaining compliant across the different jurisdictions the wealth manager operates in.”

BCG’s 2021 global wealth report notes that access to more extensive client profile data would allow wealth managers to deliver more customised services and even provide recommendations for services the client might not have even realised they needed.

Looking ahead, McKinsey predicts that by 2030, up to 80% of new wealth management clients will want to access advice in a data-driven model where customer data is used to gain a deeper understanding of their preferences and develop hyper-personalised recommendations, according to a January 2020 report focused on North America,.

The report says this could "change the terms of success" for wealth managers. In this context, those managers with a firm grip on their customer data will enjoy a considerable head start.

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Published: 28 Oct 2021
  • Wealth Management
  • Operations
  • International regulation
  • Compliance
  • wealth management
  • Risk in Financial Services
  • RegBI disclosures
  • data protection
  • data mining
  • AML

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