Artificial intelligence in financial services isn't some far-flung, science fiction daydream. It's real and happening right now. Ignore it and risk missing out on tangible benefits that can position your financial institution for success in an evolving market.
The first step toward embracing a digital workforce fueled by artificial intelligence is to simplify the concept. Understand its potential to change the way your organization gets work done, engages with consumers and protects data. The true opportunity of artificial intelligence is how it can help drive new value by innovating around the edges and in the seams of organizations.
Those benefits touch every part of financial services, in the back and front offices and from operational efficiency to the consumer experience. Consumers live in a world of integrated experiences that bridge the gap between physical and digital. And they expect that same integration when managing their finances.
But a true digital workforce isn't geared solely toward consumer engagement. It goes deeper to improve interior operations of financial institutions and, by extension, heighten the level of outward service. It can increase quality, reduce operating costs and provide smarter technology for better data access.
So what is this new digital workforce and how effective is it? It starts with five pillars of artificial intelligence.
1. Robotic Process Automation
Robotic process automation is the use of software to mimic the performance of a human worker.
For instance, when a consumer signs up for mobile banking, your financial institution benefits from knowing how often that customer or member is accessing the service. Certainly, back-office staff can track that information, but robotic process automation can create a specific report in just a sliver of the time it would take a person to do it.
It's about creating efficiencies and freeing back- and front-office employees from repetitive tasks such as file maintenance, payment processing and compliance monitoring. What may take a human worker three minutes to process could take the digital worker 15 seconds.
Without low-risk tasks on their to-do lists, those employees can instead focus on more complex, consumer-facing engagement.
2. Speech Recognition
Alexa comes to mind when thinking about this technology, which leverages voice to initiate action versus clicking a mouse or typing commands. In financial services, speech recognition is most often framed in the context of consumers using mobile devices or in-branch ITMs.
But there are emerging voice capabilities that can drive greater value for your organization. They revolve around information enablement and report delivery for associates, particularly for executives who need immediate access to the right information to make the right decision.
The request might still be directed toward Alexa, but the message might be different: "Hey, Alexa, please run this financial report."
3. Predictive Analytics
Say your financial institution wants to build a model around attrition. You want to gauge soft attrition, when consumers show signs of staying but use fewer services, as well as hard attrition, when consumers leave.
Predictive analytics can add greater precision to that model by drawing in multiple factors, such as direct deposit and bill pay frequency, to measure consumer satisfaction with services.
The technology, though, enhances various other tasks within your financial institution, from risk and fraud modeling to loan-application screening. Predictive analytics also can drive new marketing strategies that heighten engagement with consumers, helping your organization understand and respond to important life events even when people aren't walking into your branch.
It's digital dialogue at its finest and a key component to growth.
It's a simple equation: The human workforce plus digital workers equals new opportunities.
4. Machine Learning
A good way to think about machine learning is that it's a cousin of predictive analytics but without the parental oversight. Essentially, predictive is an algorithm built with fences around it to find patterns and behaviors, while machine learning is set loose without any predetermined rules and can recalibrate on its own.
Machine learning works particularly well in cybersecurity and fraud monitoring. Fraudsters are always looking for a way in through soft points in the defense, but machine learning can fortify those points by adapting to shifting data and changing areas of access.
Platforms that support machine learning can give your security team the tools it needs to keep pace with a changing threat landscape.
5. Natural Language Understanding
This pillar is among the bleeding-edge technologies that are helping financial institutions reimagine digital experiences in the context of replicating human skill sets.
Natural language understanding is the evolution of speech recognition to create a cohesive conversation that goes beyond commands.
The technology can be used in a banking experience using Alexa or through a digital bank advisor that can help drive new self-service capabilities in, for instance, personal loans.
Natural language understanding represents yet another step toward the voice-first digital world that is becoming more prevalent across all industries.
The Future Is Here
Those technologies aren't on the horizon; they're right now. It's a simple equation: The human workforce plus digital workers equals new opportunities.
And organizations across all industries are adopting those technologies and taking advantage of the opportunities. Whether through beating earnings estimates or enhancing consumer engagement, organizations are adhering to the principle that the strongest business strategies include strong data strategies.
Look at that data platform as the spoke in the wheel that lets your organization roll into the future.