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Regulation Chatbots: The Machine versus Human Redline

The growing recognition of new value creation from using chatbots has amplified market hype, even though there are many genuine cases delivering benefits.

This growing momentum has cultivated a populist belief that natural language processing, powered by machine learning, is fit for purpose in any context. Furthermore, it has cultivated a sense-making framework that without machine learning or indeed deep learning then it cannot be artificial intelligence.

Let’s explore one chatbot redline, which represents a clear boundary between the machine and humans.

This redline is regulations.

Regulations are complex by nature. Just the simple nature of cross-referencing to other regulations makes it more complicated. This complexity increases as the regulation links with other sources such as statutes, directives, guidelines, standards, regulatory bodies and case law. Further complexity is amplified when extended to international regulation such as trade, tariffs, treaties and sanctions.

Regulations contain rules to govern the conduct of organisations and their people. As regulatory decision-tree rules increase in complexity, they impair human decisions. The complexity of regulatory rules has led to the growth of negligence, errors, false positives and false negatives, handoffs, rework and much more.

A recent government study found that the documents used to apply regulations in practice are simply ‘not fit for purpose’.

The case for change is compelling.

Now back to basics.

Regulations are defined and controlled by humans.

The notion of empowering the machine to upend and change regulation is simply nonsensical. Who in their right mind would envisage presenting to the Regulator or their Board that they are empowering the machine to change regulations or policies?

Regulations is a simplistic argument for the need of a clear separation of machine-controlled learning versus human-controlled learning.

Regulation is a knowledge challenge.

The technology is readily available to meet this challenge leading to a future of machines and humans working together.  

The lag factor is the marketplace as it requires to embrace new sense-making frameworks for how regulation is represented as seamless digital knowledge for informed and accurate decisions and its importance to overall business performance.  

 

 

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Freddie McMahon

Freddie McMahon

Chatbot Thought Leader

DF2020 Ltd

Member since

04 Aug 2017

Location

London

Blog posts

55

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This post is from a series of posts in the group:

Innovation in Financial Services

A discussion of trends in innovation management within financial institutions, and the key processes, technology and cultural shifts driving innovation.


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