“Artificial intelligence can help people make faster, better, and cheaper decisions. But you have to be willing to collaborate with the machine, and not just treat it as either a servant or an overlord,” says Anand Rao, PwC Innovation Lead, Analytics.
The quote neatly sums up our relationship with AI technology. Although we appreciate its potential, we feel edgy about its power and possibilities. However, despite this, it’s pervading our lives as consumers, whether we like it or not. Every time we receive a marketing email or product recommendation, we can be sure the algorithms have been at work and we are a far from random target.
Despite its image of being cautious and conservative, the banking industry as a whole appears to have had few qualms about adopting the technology – and it seems that, as consumers, we are happy with this. A mammoth survey of around 33,000 consumers by Accenture found that more than 70 per cent of us would be willing to receive computer-generated banking advice. “Automated servicing can be the sole source of data form some customers, even when making complex decisions around products,” says the report.
One of the main uses of AI so far has been in customer service. Chatbots are becoming the de facto alternative to banking apps. These use AI to simulate conversion through written or spoken text. Just as Amazon has humanised its digital assistant by calling it Alexa, so has the Nordic banking group Swedbank created ‘Nina’. This chatbot is clearly popular; within three months of being deployed, Nina was averaging around 30,000 conversations per month.
However, this is the sharp end of AI – the human/machine interface mainly used in the consumer-facing world of retail banks. But how does – or will – AI play out in a commercial finance environment?
The business sector is understandably more cautious, prudent perhaps, about adopting new technologies until they have matured. But as millennials take up more senior roles in the commercial banking world, they will be increasingly pushing for the rich functionality they know as consumers to also be integrated into their working environment.
Today, we are seeing signs that adoption rates of AI-based technology are set to take off in business banking too. More and more banks are borrowing retail banking experience to build out their commercial and business strategies. But while the focus of its use in the retail banking world has mainly been for customer service and sales applications, in commercial banking, use cases (initially at least) are likely to be more around streamlining operational processes.
In a sense, AI as it stands today, in this environment is all about automation, about making processes faster and more efficient. And there are a raft of applications here where automation is having a hugely positive impact.
Take the introduction of digital expenses platforms and integrated payments tools, both of which have the potential to significantly improve a business’s approach to how it manages cash flow. By having an immediate oversight, through live reporting of all spending from business cards and invoice payments, as well as balances and credit limits across departments and individuals, businesses can foresee potential problems more quickly and react accordingly. All these services become even more powerful when combined with technologies like machine learning, data analytics and task automation.
We are already seeing growing instances of AI and automation being used to streamline payment processes in banks. Cards can be cancelled or at least suspended quickly and easily and without the need to contact the issuing bank, while invoices can also be automated, to streamline business payments. This means businesses can effectively keep hold of money longer and at the same time pay creditors more quickly. Moving beyond straightforward invoice processing, intelligent payments systems can be deployed to maximise this use of company credit lines automatically.
Looking ahead, we see a string of applications for AI in the payments management field around analysing data with the end objective of spotting anomalies in it. With the short and frequent batches of payments data used within most enterprises today, it is unlikely that even the best trained administrator would be able to spot transactions that were out of the normal pattern. The latest AI technology could be used here to tease out anomalies and pinpoint unusual patterns or trends in spending that could then be investigated and addressed.
While this area remains in its infancy within the banking and financial services sector, with technology advancing, financial services organisations and the enterprise customers they deal with will in the future will be well placed to make active use of AI that will help clients track not just what they have been spending historically but also to predict what they are likely to spend in the future. AI will ultimately enable businesses to move from reactive historical reporting to proactive anticipation of likely future trends.
Source: Russell Bennett, chief technology officer, Fraedom