Robo-advice  

Defining data science, AI and machine learning in financial services

  • Discuss how AI and machine learning can be applied to financial services.
  • Understand its benefits and limitations within the advisory field.
  • Explain the benefits and drawbacks of this technology for the advice process.
CPD
Approx.30min

From a regulatory standpoint, if an adviser is unable to fully explain the inner workings of the algorithms or programs on which their client’s decisions have been made, that presents a problem. Going back to my cat example – if a machine learning program cannot fundamentally tell you why it thinks that picture is of a cat or not of a cat, then an adviser hauled before the regulator is not going to be able to tell them why they did or did not recommend that product. This is a problem, and one that we will likely explore in more detail in a future article.

With regard to the quality of data, we often refer to the concept of ‘garbage in, garbage out’. This has now moved far beyond sheer quality of data input, missing fields, typos and inconsistent fields, for example, and into the sphere of mandated restrictions in the form of data protection.

The EU-driven rules around General Data Protection Regulation, which came into effect in 2018 (they were published in 2016 but came into legal effect in the UK in May 2018), present a particular tension for our sector and the advancement of, specifically, machine learning.

Machine learning is based on data, and the more data you have to help build your algorithm, the better its quality is likely to be. Therefore, the optimum scenario is to be able to collect as much data as you possibly can and hold on to it for as long as you possibly can. 

In financial services, we have the additional consideration that the nature of the data being collected is highly personal and potentially sensitive. This warrants tighter regulation around its use, around data privacy and therefore anyone using that data.

Building AI or machine learning models, for example, would have to adhere to particular restrictions and rules, in turn possibly limiting the way in which they are allowed to use the data and therefore also limiting its efficacy.

So, I see this as a healthy tension – one in which we need to strike a balance between ‘collect as much data as I can, for as long as possible’ and ‘I can only collect certain data, hold it for a certain length of time and have to throw everything else away’.

As a technology developer and solutions provider, part of my job is to work out how to best navigate that tension by continuing to understand that dynamic and the challenge it presents.

Simon Clare is global chief technology officer at Bravura

CPD
Approx.30min

Please answer the six multiple choice questions below in order to bank your CPD. Multiple attempts are available until all questions are correctly answered.

  1. What is weak AI?

  2. AI is a subset of data science according to the author.

  3. Machine learning is a subset of AI but not a subset of data science.

  4. What are two benefits of the use of technology in advice according to the author?

  5. What is one of the problems associated with building complex robotic advice processes, according to the author?

  6. And why in particular would this be a problem for advisers according to the author?

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  • Discuss how AI and machine learning can be applied to financial services.
  • Understand its benefits and limitations within the advisory field.
  • Explain the benefits and drawbacks of this technology for the advice process.

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