Part 3: FCA’s ongoing advice services review: long term compliance

What happened?

In the final part of our ongoing advice webinar series, TCC experts Gary Maude and David Boyhan discuss proportional sampling, data mining challenges, and client redress models.

While the FCA’s survey results were surprisingly positive, firms are struggling to gather historical client records from disjointed, legacy practice systems, making complete compliance auditing a significant hurdle.

Why does it matter?

A major risk is adviser non-adherence to the firm’s own processes, which carries severe professional indemnity (PI) and regulatory implications.

Firms are currently evaluating redress models, with high-profile cases like St James’ Place bringing widespread attention to how client contact, chronologies, and service delivery are documented and defended.

Who is affected?

Wealth management firms, financial advisory networks, and PI insurers managing exposure to ongoing advice fees.

Key risks

• Difficulty in extracting clean historical client data from legacy or paper-based systems.
• Adviser non-compliance with internal protocols, increasing conduct risk.
• Underestimating PI insurance implications when establishing past business reviews or calculating redress.

Actions to take

1. Perform proportional file sampling to evaluate advice quality and identify systemic risks.
2. Document a clear chronology of client touchpoints and attempts to contact non-responsive clients.
3. Ensure disengagement processes are actively used for clients who no longer require or use services.
4. Engage senior leadership to review root cause analysis and underlying compliance trends.

Wider implications

Proportionality is a vital tool. Large-scale past business reviews can sometimes be avoided by executing robust, representative sampling to prove a lack of systemic client detriment, keeping PI insurers and regulators satisfied.

Recommendations

Wealth managers should establish a clear data-mining strategy and seek expert compliance support to design defensible, proportional file sampling methodologies.