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Time to embrace customer-facing AI opportunities

Act now to create a first mover advantage with high-quality data and ethical approach to AI.

Micaela Vernmark / January 15, 2021

8 weeks. It took 8 weeks and a pandemic to make a 5-year leap in digital payments adoption. As a result of this digital acceleration – working remotely, buying online – more data is being created every day.

Artificial Intelligence 

/ˌɑː.tɪ.fɪʃ.əl ɪnˈtel.ɪ.dʒəns/ 

The study of how to produce machines that have some of the qualities that the human mind has, such as the ability to understand language, recognize pictures, solve problems and learn. 

AI in banking – The hyper-personalization of services  

In recent times we have seen more and more banks, especially large ones like USAA and HSBC, use new technology to manage their operations. Not surprisingly, these banks are adopting AI to help them stay at the forefront of the game and ahead of the competition.   

Like them, we believe the applications for AI are endless within bankingFor instance, analysis of customer patterns, such as spending and use of apps, combined with state-of-the-art data analytics – the so-called hyper-personalized offerings – opens up the potential for enormous customer product and card offering improvements. AI enabled chatbots can also support customers with many of their inquiries – saving time and resources. Beyond customer facing services, we have seen AI used to simplify back-office operations, improve Know Your Customer (KYC) and Anti-Money Laundering (AML) procedures, and monitor and prevent fraud.  

Slow AI adoption in customer-facing services  

With that said, utilization of AI in banking and the financial sector has been slower than in many other industries, particularly on the consumer side of the business. In part, this can be put down to the inherent mistrust of AI among consumers, and indeed employees. Cases of biased algorithms, data hacking and privacy breaches, have all contributed to this. But in reality, today’s consumers are exposed to AI on a daily basis, most of the time without being fully aware of it.  

Changing attitudes to AI  

Now, more than ever, the digital ecosystems and interactions are blurring, i.e. banking, texting, social media, online shopping, gaming, etc. And we can clearly see how this is driving changes. This is being further accelerated by the pandemic-triggered increase in digital adoption – whereby remote working and online shopping have become the norm for many more people – and attitudes towards AI have changed in line with this.  

If AI is to transform the payments industry – through the customization of services – in the way it has done for the communications and media industry, data must be leveraged and transformed into insights. This leaves us with two key aspects to consider:  

  1. Quality. Not quantity. The data collected must be high-quality data, or at least be categorized suitably in order for it to be made sense of and utilized. Large volumes of data are worth nothing without intelligence.    
  2. Ethics. An ethical line must be drawn between the moment AI ceases to enhance the customer experience and the moment it becomes intrusive. Banks will need to win the trust of customers and must act ethically.  

Putting a value of AI in banking  

The financial opportunity of AI in banking is hard to quantify. However, from the studies we have seen, the incremental potential could amount to hundreds of billions of USD. However, taking advantage of this requires investment. In our opinion, the full potential of AI will only open up to those banks that reimagine how they engage with customers, and that deploy new technologies and leverage customer data in an ethical, integrated way.  

AI a double-edged sword   

Of course, there is a caveat to implementing AI, which cannot be ignored. Implementing AI technology is not simple. As mentioned earlier, you need clean data, but you also need qualified people to deploy it. Qualified people who may fear that that they will be replaced by AI. But AI is here to stay, and these concerns must be dealt with if you are going to provide the optimal personalized experiences to your customers.   

Act now  

As a bank you need to ask: What are we trying to solve with AI and for whom? Are we ready to take this step? And can we afford to wait especially when digital giants and new agile players are entering the financial space with access to richer customer data? 


Related blogs:

Don’t just replace a card platform with a new card platform, deliver a completely new experience for your customers instead

The Fintech dilemma: how to evaluate and collaborate to bring added value to the cards business

Waterfall, agile, devops. What is the most effective process for your collaboration in delivering cards platforms?


Learn more on Card and financial product management related blog and insight series, case studies and more - click here.

Micaela Vernmark
Tietoevry alumni

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