In the adviser market, ‘robo advice’ has been touted as a low-cost – albeit stripped back – alternative as spiralling costs, compounded by a decline in adviser numbers following RDR, has made the traditional advice process unaffordable for many.
The viability of these online investment management sites, based on algorithms that originally served the traditional advisory community, is being taken seriously by the City watchdog – which launched a joint consultation in the autumn with the Treasury to explore how they can improve access to financial advice.
Now a new form of artificial intelligence has entered the fray, promising to transform front line customer support for mortgage companies while helping them to allay dual pressures of driving costs down in a competitive environment while maintaining high satisfaction levels.
Its name is Amelia.
Named after Amelia Earhart, the US aviation pioneer and author, the algorithm-based system has been designed to be able to undertake the tasks of a typical mortgage firm’s call centre operative, including determining mortgage eligibility and providing product information, to allow them to take on more creative roles.
More than two decades in the making, the second iteration of the ‘virtual employee’ understands 21 languages, absorbs information from textbooks, transcriptions of conversations, email chains and just about any other written text in a matter of seconds.
Its creator, Chetan Dube, chief executive of tech firm IPSoft, said: “My wife thinks I’m having an affair because I have spent so much time with Amelia.”
He added: “People have been talking about the second machine age. The basic platforms on how services are rendered are shifting and Amelia is emblematic of that shift. Cognitive technology is going to be the trend of the 21st century.”
Call centres are typically the front line of customer support, but are often characterised by long waits and unwieldy menus, leading to unsatisfactory experiences and ultimately loss of business, Mr Dube said.
He added that traditional automated response systems, which are pre-recorded menus, equipped with speech recognition software that guides customers through a range of options, are cumbersome.
Amelia, however, has contextual filters which allows it to understand loosely stated problems and recognise sentences that have the same meaning but are structured differently.
When faced with foreign queries, the system will call upon a more experienced human agent to help resolve the issue. It will then listen in to the human-to-human interaction and create new steps in its process ontology, which will enable Amelia to address the same type of issue with subsequent callers.
Amelia is currently being used by an unnamed ‘leading’ UK bank to support staff who have questions on mortgage eligibility, according to Mr Dube, adding that the system has also been courted and tested by a number of multinationals in areas such as manning technology helpdesks and financial trading operations support.
Research conducted by analysts at US information technology research and advisory firm Gartner, found that five US media companies which implemented the system saw their mean time to resolve a telephone query reduced from 18 minutes and 20 seconds to four minutes and 30 seconds.