Lean thinking makes you look good: How to strengthen your client service and regulatory compliance

Lean thinking’s emphasis on implementing continuous process improvements and eliminating waste can have radical benefits for wealth managers’ internal operational efficiencies and costs. But this is only one part of the value proposition. Applying lean thinking to data management can have equally powerful benefits for firms’ external interactions with clients and regulators.

Deliver more value to clients

A lean operating model is ultimately about improving value. Increasing internal productivity performance and reducing waste can have a potent impact on wealth managers’ profitability. What really determines firms’ success long-term though is their ability to continue providing clients with better services – producing superior outcomes, faster delivery, fewer errors and greater trust.

Accurate, consistent, complete and timely data is central to delivering that high-value client offering. Wealth management firms that struggle to achieve the necessary data quality – as many do – will find it tough to thrive.

Four-eyes (or even six-eyes) data checks remain commonplace across the industry. While they may pick up some issues, they won’t catch everything. Human involvement means errors will be missed, or corrections applied imperfectly.

• Manual checks are a wasteful use of resources, eating up (often senior) staff time, requiring extra headcount, slowing processes down, delaying downstream tasks and instilling key person risk.
• Many firms don’t have a robust, standardised procedure for staff to follow when making amendments, nor a recheck process to ensure mistakes have been properly rectified.
• Remediation depends on staff actually completing the task, and the results being relayed to supervisors through the management reporting.
• Remediation efforts tend to be reactive, responding to errors that may have occurred days, weeks or even years before.

Any data errors not caught and corrected will infect downstream systems and processes, contaminating accounts, valuations, suitability assessments and client reporting.

In many cases, inaccuracies are only discovered when clients check their accounts or read their reports. A small one-time issue will be embarrassing, but may be overlooked. Significant and/or persistent problems risk compensation claims. The ensuing distrust and dissatisfaction can precipitate customer desertions and potential revenue loss. Reputational damage – amplified by social media and online reviews – risks putting off prospects.

As data’s importance and volumes grow, such manual fixes are clearly unsustainable.

A lean thinking approach focuses on “autonomation” – partnering automation with human intelligence.

In the Toyota Production System, this jidoka principle highlights problems as they arise, enabling countermeasures to be introduced immediately to prevent recurrence.

Similarly, in data management, the key to excellence lies in rules-based, automated data checks.

Employing automated rules to continuously monitor wealth managers’ data ensures any integrity and systemic issues are identified and evidenced quickly. Integrated workflow tools can then automatically assign errors to appropriate users or teams, along with comprehensive solution descriptions to promote rapid resolution.

A dashboard that details what issues are being investigated, where they stand, what has been resolved and when keeps management fully informed of the status of each task, enabling further prompt action where required.

Data excellence further depends on a lean thinking focus on continuous improvement (the Japanese notion of kaizen). Data quality is a never-ending BAU task. Automated data tools embody this spirit of kaizen, working 24/7 to check data, flag errors and facilitate prompt fixes. And by continually introducing new rules, firms can progressively expand the breadth and depth of their automated data checking coverage.

Armed with much higher quality data, wealth managers can generate information-rich client reports on time and with confidence. Trusted data allows for accurate tax reporting, avoiding nasty liability surprises. Advisors in turn have a complete and up-to-date picture of clients’ financial position, from where they can propose products and services that best support the customer’s financial health.

Enhance regulatory compliance

The same principles apply to regulatory compliance.

Bad data can feed through processes and show up as accounting errors and inaccurate regulatory report submissions. Any compliance-related problems may prompt legal censure and regulatory fines – with firms in turn risking reputational damage and customer attrition.

In addition, UK wealth managers face specific regulatory responsibilities for the quality of their data from:

• The Financial Conduct Authority’s Principle 3 on management and control, with its emphasis on “adequate risk management systems.”
• The Senior Management Arrangements, Systems and Controls (SYSC) sourcebook, which requires the data firms use to assess their risks to be fit for purpose.
• Senior Managers and Certification Regime (SM&CR), which centres on effective governance based on quality data.
• The EU’s General Data Protection Regulation (GDPR), where firms must “take all reasonable steps to ensure the personal data you hold is not incorrect or misleading”.

Non-compliance doesn’t just lead to a slap on the company wrists. There are significant personal liability issues – and financial penalties – at stake.

Lean thinking tackles regulatory vulnerabilities by building data integrity into processes. Embedding automated validation checks, mandatory data fields and real-time alerts directly into workflows, rather than relying on post-hoc reviews, helps wealth managers prevent non-compliant activities and outcomes before they occur. Any data issues that do arise are identified and resolved quickly. And because staff can trust in the data they see, they can report with confidence.

A continuous improvement culture that encourages employees to flag potential regulatory risks, suggest improvements and feed those back into standardised work processes also creates a more proactive compliance culture than traditional top-down approaches.

By progressively strengthening their data quality and compliance safeguards, firms become better able to meet today’s client and regulatory demands, and keep pace with whatever changes lie ahead.