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Implementing Artificial Intelligence

Key considerations for businesses wishing to implement Artificial Intelligence.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a field of computer science which aims to simulate human intelligence and cognitive ability. By emulating our cognitive intelligence, AI is powering the apps that humans frequently use to ‘offload’ time-consuming tasks. But, as AI is becoming increasingly capable of also mirroring our emotional intelligence, there is a growing trend for the technology to be used for conversation and companionship. 

In business, AI is augmenting the human workforce, helping to solve complex problems by identifying patterns in large data sets and performing difficult tasks more effectively, by learning rather than following defined rules. This is leading to improvements in operational efficiency, sales and customer experience.

The technology is often used in conjunction with ‘The Internet of Things’. Where IoT is about connecting devices and generating data points, AI allows us to effectively process the vast reams of data to generate intelligence. 

Artificial Intelligence and the financial sector

The financial services sector is a pioneer in the adoption of AI, applying the technologies to a wide range of business activities, from fraud detection and conducting surveillance, through to chatbots complimenting customer service offerings. There is a huge potential for AI to be adopted widely within transaction banking in the future. As Pushkaraj Gumaste, Barclays’ Head of International Corporates highlights, multinationals are increasingly becoming tech savvy, hungry for excitement and are demanding user-friendly interactions.

Our survey at Money20/20 global conference found that 39% of respondents thought Artificial Intelligence was the technology that was most likely to revolutionise the way they work.

At one of our previous Tech Forums, speakers highlighted the key considerations for implementing AI.

What are the benefits of Artificial Intelligence?

Although often associated with consumer applications, Artificial Intelligence is able to accelerate business processes, drive profit, and identify solutions to problems far too complex for human ability. 

When AI applies cognitive logic to data sets, it is able to find insights that humans may never have thought to investigate, however collaboration is critical to unlocking these advantages. William Maunder-Taylor, AI Impact Strategist at SparkBeyond, said that where there is a strong working relationship between the business, technology and data science teams, this insight can be used to drive significant business impact. 

Maunder-Taylor noted that this business structure is being increasingly deployed in the Insurance sector; “We’ve seen CFOs taking ownership of some of the digital transformation as they sit above the business KPIs. They have a team of financial analysts doing the reporting, who can then be supercharged by the technology, by working in tandem with data scientists.”

By constantly analysing the data behind genuine and fraudulent transactions, Barclays’ AI technology is able to create dynamic criteria for suspected fraudulent payment transactions, thus keeping pace with evolving criminal tactics and protecting customers from fraud.

Dr. Dimitrios Emmanoulopoulos, Barclays’ Lead Data Scientist, said that this certainly wouldn’t be possible without AI, due to the celerity required and the complexity and scale of the data.

The business case

It is crucial to focus on the strategic priorities of the business when considering how and when to implement AI. This is often overlooked by businesses in their haste to harness the benefits of AI.

Angelique Mohring, Founder and CEO of GainX, suggested that businesses should map out and audit the key processes required to achieve their objectives, and then use AI to address any barriers to success that are identified.

“Everything you are doing around AI should absolutely have a direct path to your top strategic imperative” said Mohring, who added that those not considering the business need are “using AI for problems that are not worth solving” at a time when digital talent is incredibly valuable. 

Another common mistake is the unnecessary overuse of AI for problems that can be resolved with a standard, pre-existing solution, leading to excessive costs for the business.

Understanding potential bias

It is important to be aware of the bias within Artificial Intelligence, which can have an impact on the technology’s outputs. 

“Data inherently has bias, and that bias is dictated by our society at that time,” said Kate Rosenshine, Head of Data and AI Cloud Solution Architecture for Financial Services at Microsoft UK. This is problematic when this biased data is used to make future decisions. 

To minimise bias and increase accuracy, Mohring suggested that data should be up-to-date and triangulated across multiple data sets, rather than using just one. Mohring also touched on the importance of having diverse technology talent, “If you do not have a diverse team of developers, you absolutely will have bias built in.” 

As businesses need to regularly revisit the data and code behind AI to mitigate against this bias, this is not a technology project that can be left after its implementation - an important consideration for businesses contemplating this technology. 

Ethics and responsibility

As the technology continues to evolve, there is growing concern around the ethics and responsibility of Artificial Intelligence, particularly regarding how it is applied and who can gain access to the technology. 

In many industries, it is imperative that the outputs are explainable; for example, in the financial services industry where customer loan or credit applications can be approved by models including AI, customers have the right to ask for an explanation if their request has been declined. “The simpler the model, the easier it is to communicate to the customer why their application was declined,” said Dr. Emmanoulopoulos, in a caution to avoid excessive complication. 

Summary

Jeremy Wilson, Vice Chairman, Barclays Corporate Banking, closed by highlighting the optimism of the future workforce surrounding this technology and sheer scale of the opportunity; with Mohring cautioning that with this opportunity comes a great responsibility for corporates creating and wielding this technology.

  • Artificial Intelligence is only able to deliver significant value when aligned to business objectives, so companies need to allocate resource carefully to ensure they are driving the most impact from their digital talent. 

  • All AI has built-in bias which can be exaggerated by aged data. It is essential to regularly revisit the code and the data to mitigate against this bias; this ongoing maintenance should be considered when deciding whether to implement the technology. 

  • The ethics of AI should not be overlooked as the onus is on companies that implement it to ensure that it is applied ethically and that the technology doesn’t enter the wrong hands. 

  • Companies, therefore, need to take the time to consider the business case, the intrinsic bias and the ethics of the technology, before implementing AI. 

Further reading:

The Future Computed^ – Artificial Intelligence and its role in society. 

AI Business School^ – For business leaders to gain specific, practical knowledge to define and implement AI strategy, foster an “AI-ready” culture and learn how to use AI responsibly and with confidence.

Maximising the AI Opportunity^  – How to harness the power of AI effectively and ethically. 

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