Russell Bennett, chief technology officer, Fraedom
Chatbots, robo-advice, digital twins… the world of banking and financial service may be seen as one of the more conservative sectors of today’s economy, but recently its customer service methods sound increasingly akin to a sci-fi novel.
Yet, there’s plenty of evidence to show these human-light customer service tools are effective. For example, Swedbank says that its chatbot ‘Nina’ has 40,000 conversations a month and these resolve 81% of issues.
It seems that, as consumers, we’ve all grown accustomed to artificial intelligence and its consequences. We no longer blink an eye as details of the shirt or coffee machine the algorithms predict might buy next pop up on our screens. A recent Accenture survey of 33,000 consumers found that more than 70 per cent were willing to receive computer-generated banking advice. “Comfort with computer-generated support is growing, bolstered by lower costs, increased consistency and high reliability,” says the report.
But while consumer banking focuses on using artificial intelligence (AI) mainly for customer service and sales applications, commercial banking is eyeing their success for different reasons. Increasingly, the industry is looking at ways it can use AI where it needs it most – to streamline operational processes.
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AI, like big data before it, is one of those terms that has pervaded the business world, becoming a shorthand for a whole family of technologies, from machine learning to natural language processing. Upfront, at the sharp end, AI is pushing boundaries in human/machine interaction. However, increasingly, the same technology is also performing less glamorous functions in the business banking sector. As one financial services blogger puts it: “AI-based services don’t need to take over the world in order to actually be helpful”.
In many ways, it’s all about automation and making processes faster and more efficient. 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 such as 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 raft of applications for AI in the payments management field. These are all around analysing data with the end objective of spotting anomalies. 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. Here it’s better for a search robot of this kind to be given unstructured information with a few presumptions inherent to the information as possible. This way AI technology can tease out any odd patterns it spots without other priorities or preconceptions masking the reality.
But this isn’t to say that commercial banking can carry on ignoring the customer experience, either. Consumer technology is changing the way in which business workers view their commercial experiences. Millennials are increasingly taking on more senior roles and the first truly digital native generation is now entering the workplace.
While this area remains in its infancy within the commercial 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 in this area. For example, helping customers 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 and the personalisation we’ve come to expect in the consumer world translated to commercial customers too.