The financial and banking sector is under constant and ever-increasing pressure to improve efficiency. In this demanding environment, AI is becoming less a luxury, more necessity, thanks to its transformative powers on areas such as compliance, and, indeed, transactions.
AI can improve personalisation, immediately identify patterns oft-missed by humans, rapidly answer consumer queries, and profile and blacklist customers who do not pass compliance checks, in real-time.
While the futuristic notion of a bank filled with enlightened virtual assistants acquiescing to consumers’ every request is still some way off, pragmatic AI has been transforming banking processes and streamlining the payments lifecycle for some time. It’s not hyperbole to say that it is now mission critical for those institutions wishing to stay competitive.
What is the Current Relationship Between AI and the Financial Sector?
Retail banking company, Santander, were one of the first to show-off their technological prestige in 2010, introducing AI-led robot butlers to show guests around their Spanish visitor centre.
Investment powerhouse JPMorgan currently uses AI to monitor patterns and execute trades, while Morgan Stanley has an AI fraud detection team.
Gartner believes at least five percent of all economic transactions will be handled by autonomous software by 2020, and a 2017 study from Statista found that out of all EMEA financial services industry companies surveyed, only 11% were not already planning on implementing AI for their operation-specific back office processes.
AI isn’t the “next big thing”, it’s already a driving force within the banking sphere, and one that no bank can afford to ignore.
Why is Finding Efficiencies in Transaction Banking Operations Key?
The biggest operational workloads in the banking industry are routine, repetitive, and administrative-heavy tasks such as status report analysis, regulation and compliance tracking, and IT reconciliation processes.
This kind of work is critical but also time-consuming, adding up to thousands of wasted hours every year and the cost that goes with that, and it frustrates employees who are unable to work to their full potential on more strategic tasks.
Key Ways AI is Improving Efficiency
Machine learning empowers technology to make decisions and perform actions previously only available to humans, with powerful implications not just for payments processing but also compliance, risk and fraud detection.
AI systems can automate many tasks including those time-consuming and repetitive activities that divert employees away from higher-value work, often eliminating manual actions and human intervention altogether. Not only does this improve efficiency and speed, it also eliminates human-error from the process.
When delivered effectively, AI helps a bank cut costs and delight its customers and also greatly streamlines key financial processes to free up valuable staff time and overhead.
A Case in Point: Cognitive Automation and Natural Language Processing (NLP)
When asked to consider NLP, most minds go immediately to consumer assistance use cases, such as chatbots or the likes of Siri and Alexa. Of course, intelligent interactions and AI-powered self-service systems have transformative effects upon customer experience in transaction banks, but NLP can also have far more nuanced and interesting applications, particularly for the financial sector.
AI technology that incorporates NLP can revolutionise routing across the transaction lifecycle, dramatically improving straight-through processing rates and introducing the ability to ‘self-repair’ – ‘cognitive automation’ of payments.
For example: Because back-office operations process vast numbers of instructions created by humans, free format information creeps into much of the messaging, making it unreadable for standard computers, and therefore requiring manual intervention. International transfers are a key efficiency battleground – indeed, many banks find that anywhere between 50 and 70 per cent of their international transfers need to be manually repaired and routed.
But NLP understands this free format information – just as a human can, but far more quickly and without the need for manual intervention.
Cognitive automation – which powers intelligent routing, self-repair, and false positive reductions – draws upon a range of AI disciplines, including of course NLP. Machine learning processes that learn from human operator behaviour patterns can further reduce the need for human intervention and automate actions which NLP cannot address on its own. Cognitive automation’s improvements to efficiency also have side benefits – a dramatic decrease in the number of customer queries staff have to deal with, as well as significant reductions in payment initiation issues.
What does the Future of AI Look Like in the Financial Sector?
The artificial intelligence phenomenon was once thought a fad – today we see it is anything but.
AI will continue to revolutionise the banking sector and wider business practices for years to come, and those unable to adapt their processes to enable its inclusion are being left behind.
The answer isn’t to indiscriminately “throw AI at everything”, but to identify those areas in which its application will generate greater efficiency, and ultimately savings.
As digital transformation continues to change the face of the transaction banking sector, AI will transform the payments lifecycle, aiding efficiency at every touch-point and enabling banks to provide the slick, secure experience that today’s customers expect.
