A new World Economic Forum and Deloitte Global report has examined what lies ahead for the financial services sector as AI changes the traditional operating models of institutions.
It's something that has started to be trialled on a small scale in New Zealand, particularly by banks offering digital AI "assistants". In recent years, mortgage advisers including Squirrel have introduced their own AI chat-bots to help customers through the mortgage application process.
But the report said the potential implications were wide-ranging and would require a re-think of many financial services structures. The bonds that had historically held financial institutions together were weakening and new models were being formed.
Instead of mass production, those who succeeded would offer tailored experiences and high customer retention benefits - not high barriers to switching.
As the move to digital distribution and servicing of financial services accelerated, extensive branch and adviser networks would no longer be needed and digital connections would become the norm, it said.
Under a new “self-driving” advice vision, customers would interact with an agent for advice and to customise products, which would then be provided by a one-step-removed manufacturer.
“There will be far fewer interactions between providers and customers as the customer experience is largely automated. However, the interaction points that persist will become increasingly important and advicecentric.”
AI would offer advisers advanced recommendation engines to customise the features and price for each financial product.
Automated, sophisticated advice would allow for a frequent, proactive and personalised service that was not economical under traditional human-based customer service models.
The report said it was not yet clear which AI-driven advice situations would require human advisers and whether that need would diminish.
AI could also have a role stopping impulsive investment decisions, it said.
“Mass-market investors sometimes fall into beginner traps (e.g. buying high and selling low, impulse investing, tax inefficiencies), because the oversight afforded to higher-net-worth clients – such as private bankers – is not available.
“Cross-product analysis can use machine learning to look across a customer’s financial products and automatically optimise areas of improvement.”
It said institutions would need to be highly focused on delivering what customers wanted.
"This means getting to know customers beyond just their finances and looking for opportunities to improve their day-to-day lives."