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FASEA code to encourage adviser takeup of AI

FASEA code to encourage adviser takeup of AI

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By Adrian Flores ·
January 24 2019

FASEA code to encourage adviser takeup of AI

A financial education provider says the new FASEA Code of Ethics provides a good opportunity for advisers to leverage the full potential of artificial intelligence and machine learning technology.

According to Kaplan Professional chief executive Brian Knight, with FASEA's Code of Ethics coming to effect on 1 January 2020, now is a suitable time for the industry to look to maximise the assistance of technology.

"Over the next 12 months, the industry needs to see this as an opportunity to be much clearer and crisper in its documentation and procedures," he said.

"It needs to be down to the level where the industry can utilise these technologies to assist those on the front line, while training the next generation and retraining existing advisers on their exact obligations."

Regtech firm Red Marker said there needed to be a mindset change within the advice industry on how AI and machine learning tools can best assist the industry.

"It is important both dealer groups and vendors progress with realistic expectations, particularly around the 'pre-work' that needs to be done to ensure financial advice can become an ideal candidate for automated solutions," said Red Marker CEO Matt Symons.

"If the financial services industry wants to increase the likelihood that effective statement of advice (SoA) review solutions emerge at a faster rate, then we need to come together and collaborate … working together is going to be key to developing highly-reliable, automated review solutions."


However, both Kaplan Professional and Red Marker noted that, before the industry can benefit from advice review technologies built using AI and machine learning, existing pre-conditions needed to be in place, including:

  1. Expectations need to be managed - automated solutions will not be available 'off-the-shelf' to replace or meaningfully reduce the amount of supervision required.
  2. Although significant training data exists, there are limitations, including:

    a. Many files require optical character recognition, and the quality of data extraction is challenging for natural language processing and machine learning techniques.

    b. Regulations have evolved in recent times and what once might have been acceptable may not be any more, limiting the 'training' value of this data record.

  3. The industry seems to be diverging, rather than converging on standard approaches to SoA construction, automatic programming language and product comparison logic.

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