With unstructured data estimated to make up 93 percent of all data by 2022, automating the interpretation of communications for accurate, effective and timely decision-making is critical for success in today’s financial enterprises.
According to McKinsey, the average worker spends roughly 11 hours of their week reading, answering, and forwarding emails. That’s around 28 percent of their working hours.
Within the investment banking sector, that figure is as high as 40 percent.
Human language is a valuable commodity, but with most communication now taking place digitally, conversational data is largely unstructured and, as a result, unusable. To tap into that data’s potential, financial enterprises are now applying natural language processing (NLP), a branch of artificial intelligence (AI), to derive understanding from vast volumes of communications data.
There have already been several implementations of AI across a broad spectrum of the financial services industry (FSI), and the rapid pace of adoption paves the way for greater efficiencies across all divisions and functions. By unlocking the power of communications data, operations teams can go one step further, improving products, services, and the overall user experience.
But first – what is natural language processing and how does it work?
What Is Natural Language Processing (NLP)?
Natural language processing, or NLP, essentially trains a computer to understand, process, and generate human language. It utilises algorithms to derive meaning associated with every sentence and collects the essential data from them. Simply put, it turns unstructured data – such as emails, text messages and chats – into data that is quantifiable, searchable and, most importantly, actionable.
Today, the sheer volume of unstructured data is proving to be a problem for 95 percent of businesses. But harnessing the potential of AI-based communications mining means leaders across FSI can benefit from a 360-degree view of their enterprise, enabling them to make targeted decisions at speed.
And if there was ever a time to implement such processes, it’s now.
The Fabric of Interaction Has Changed
Following last year’s dramatic shift to working-from-home, financial firms around the world witnessed a sudden proliferation of instant messaging and digital collaboration. So much so, it’s now estimated that unstructured data will make up 93 percent of all data by 2022.
This trend suggests that the fabric of interaction has changed – and as a result, so have consumer expectations.
We’ve become accustomed to the immediacy and convenience of consumer applications, of speedy and efficient resolutions (think Monzo and Amazon). And yet the more reliant we are on technology, the more pressure there is for organisations to create personalised and interactive user experiences that deliver optimum results.
While financial institutions already use AI to analyse stock market data, and machine learning to improve fraud detection – technology that Jamie Dimon (CEO, JPMorgan Chase) has claimed saves the US bank $150m annually – applying NLP and automation to unstructured data will be revolutionary in unlocking business value and optimising operational efficiency.
It’s no surprise, then, that financial services companies are getting hooked on AI.
37% of Financial Services firms globally adopt AI to reduce operational costs, followed by greater predictive analytics to improve decisions and scale up employee capacity to handle volume-based tasks.
The Proliferation of AI Automation in Financial Services
It’s reported that 86 percent of FSI executives plan on increasing their AI-related investments through 2025. In doing so they aim to capitalise on the data from new digitally driven channels and optimise their internal processes. Here are just a couple of ways NLP can help:
Improving Customer Experiences
By applying NLP and automation, unstructured data is given context making it more measurable and more useful. Rather than manually trawling through vast email inboxes, machine learning can enrich that raw text with meaning, flagging repeated queries, complaints, and other topics of interest.
By enhancing back-office functions that ensure quicker and easier settlements with minimal human error, knowledge workers will be free to prioritise workloads that rely on human skill, such as empathy, and perform their jobs at scale.
Knowledge workers can therefore be proactive – not reactive – in dealing with incoming issues. Processes can be made scalable and repeatable with the aim of resolving enquiries at speed, thereby driving efficiency and delivering elevated customer experiences.
Reducing Latency & Driving Productivity
Financial enterprises are inundated with millions – in some cases, billions – of emails each year. The vast majority of these will be transactional. As a result, 40 percent of employee time is spent in email-based processing applications.
For vast operations teams, this presents an opportunity for process simplification and, ultimately, elimination. By leveraging NLP and automation, intellectual waste can be measured, and productivity augmented by dynamically routing and automating transactional requests.
Redefining where employees need to prioritise their work efforts will ultimately reduce operational costs, improve decision-making and remove time roadblocks for smoother, more efficient operations.
Make Every Conversation Valuable
While operations leaders within capital markets are already harnessing the power of AI, new possibilities are emerging to unlock more value from their ever-expanding bank of data. And communications-based technology is leading the way.
Not too long ago, the idea that a business user could create an NLP model without requiring the expertise of an IT professional was inconceivable. Now, the people who are close to the client, the data, and the problem are being put in the driving seat. They’re establishing automation processes that speak to their unique business challenges – and this is how value can really scale.
In a world of digital interconnectedness, natural language processing and automation is bridging the gap between IT and humans, and maximising the power and proficiency of both machines.
There’s no denying the potential of AI-based communications mining will continue to transform radically. But one thing’s for certain: it won’t be long until NLP will be inherent in every channel across the financial services industry.