PRESENTATION SUMMARY - Robo-advisory for a more efficient front-office
Speaking at Hubbis’ Digital Wealth event in Singapore in June – Mark Buesser of IMTF Group says robo-advisory is now finally finding more takers, especially when it leads to a more efficient front-office.
Robo-advisory technology has been adopted to date mainly for investment offering and management – with the focus on middle-office tasks and applying and combining market data. However, a combination of data available should allow investment managers to see where the full effects of digitalisation can impact client relationships, says Mark Buesser, chief executive officer of IMTF Group.
He believes that the centre of the digital transformation remains the experience delivered with the digitised client relationship.
This is in the form of digital marketing – in terms of personal content curation and social media / digital collaboration tools – and acquiring and engaging front-to-back – via multiple channels, staying compliant and creating trusted relationships.
Industry challenges which remain include: the level, complexity and changes of compliance requirements; the fact that many fintech services, technologies and new ways of communication are mostly non-integrated silo-solutions; and that back-office processing is often legacy based, batch oriented and not talking to middle and front-office systems.
Buesser says that improvements will result from a strategy with business models, technology and processes aligned.
This means a focus on interaction between multiple systems and services; improving RM support and advice regarding regulations and risk management; bundling expertise, knowledge and technologies by providing shared resources and networking services; and respond to new job profiles.
RegTech solutions are the missing link to create a front-office robo-adviser, adds Buesser, in terms of integrating all existing systems and external data, adding new technologies for automation, enforcing the rules to control the cost of non-compliance, building on a dynamic risk assessment, and preventing inefficiencies from high error rates of unstructured, multiple, manual data entries.
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