Expert: Chris Williams, Proposition Director, Nucleus Facilitator: Ben Wright, Director of Progress, Change Squared Ltd
Headlines:
- Current use of technology and automation - Advisers shared examples of using technology like robo-advice, cashflow modelling tools, document automation and platform integrations to drive efficiency. But poor system integrations and data issues remain a key challenge.
- Data integrity as the priority - Many firms are still focused on cleansing bad data, getting accurate data flows and definitions first before optimising and automating processes. Data gaps and quality issues still hamper progress.
- Efficiency can enable more personalisation - Automating repeatable tasks frees up adviser time to focus on tailored advice. But some warned generic centralised propositions can limit personalisation, even if service feels tailored.
- Client experience remains key - Personalised service and experiences drive satisfaction. Digital channels can augment, not replace human interaction. Firms are not yet using data to predict and tailor like Netflix.
Discussion:
The discussion covered topics around improving efficiency and productivity in financial advice through technology and automation, and whether this leads to less personalisation for clients.
Technology like onboarding automation, centralised investment propositions, cashflow modelling tools and document automation have helped improve adviser productivity and efficiency. However, poor integrations between systems can hamper productivity.
Data quality and integrity issues need addressing first before automation - there's no point automating bad data. Firms are at different stages here.
Efficiency doesn't have to mean less personalisation - it frees up adviser time to focus more on tailored advice. But generic, streamlined advice propositions can serve simpler client needs well.
Personalised service and experience are what clients ultimately want, regardless of tech used behind the scenes. Digital channels can enhance, not replace human interaction.
Firms aren't yet using customer data to a Netflix/Spotify level to tailor and predict experience - many are still focused on getting accurate data first.
Key takeaways:
- Review our current use of technology and automation in advice processes - what's working well and what pain points remain?
- Assess data integrity in our key systems - where are the gaps in quality and accuracy, and fixes needed?
- Consider how we balance efficiency with personalisation in different advice models - what's right for different client segments?
- Discuss how we can deepen personalisation with client data - what quick wins vs longer-term opportunities?
