TechnologySep 16 2021

Can machine learning improve client outcomes?

  • Describe how machine learning can be used by financial services
  • Identify some of the challenges of machine learning in the sector
  • Explain how to think about machine learning in financial services
  • Describe how machine learning can be used by financial services
  • Identify some of the challenges of machine learning in the sector
  • Explain how to think about machine learning in financial services
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Can machine learning improve client outcomes?
Photo by Tima Miroshnichenko from Pexels

Players across financial services are constantly seeking inspiration from other business sectors in terms of how they conduct their operations. This might be through advertising or branding, or aspects of customer experience through to their digital offerings. 

Yet one warning I am always keen to issue, especially when it comes to how to digitise, is akin to the concept of cargo cult programming, which refers to a style of design characterised by the blind adoption of patterns or technologies that are appropriate in one context but not perhaps in ours.

We know that Google and everyone who works there are pretty much at the top of their game when it comes to digital solutions. But I often have to remind myself Bravura is not Google, and you are not Google. Therefore, what is the point in trying to be? Just because another industry or provider does something, does not mean we have to try and emulate it.

Take digitising of information processing. What we have a tendency to do in financial services is take a load of pre-digital processes that existed under a pre-digital regulatory environment, and effectively use technology to optimise the hell out of them. It often makes no sense.

Why would we spend a large amount applying ML to pull apart company reports when we should probably be producing those reports in a machine-readable way in the first place? 

It is up there with producing digital versions of forms and paperwork to then insist on printing them out and sending them in the post. But I digress.

We hold up companies like Amazon as a stalwart example of online shopping experience and digital delivery. Yet, there is a reason Amazon does not sell financial products (for now). There is an enormous degree of commitment and building and nurturing of long-term relationships that are inherent in selling a financial product.

There is the rightful and entirely appropriate step change in regulatory oversight required when selling a pension versus selling a TV. The model Amazon uses for its vast range of items is purely transactional and their distribution is therefore far easier to scale.

That said, in certain areas from these sectors, such as supply chain management and logistics, the potential application of AI can be used to great effect.

The ability of AI to analyse huge data pools, understand relationships, and gain insights over logistical strengths and weaknesses can create massive benefits throughout the supply chain, from procurement to sales.

Market volatility shone a light on the need for operational agility, flexibility and resilience. According to McKinsey, supply chain management that is rooted in AI is expected to be paramount to helping companies face these growing challenges. 

Keep people at the heart of it

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