As the impacts of artificial intelligence and machine learning creep their way into every corner of the financial industry, banking providers are finding new ways to transform their approach to creating, producing and distributing their marketing materials. Advanced data analytics can help you build an omniscient system that automatically knows what to send, to whom and when, and do so without raising any compliance red flags.
It’s easy to understand how some people are intimidated by subjects like machine learning and artificial intelligence. First, it involves math… a lot of it. And then of course there are the countless sci-fi thrillers like The Matrix, The Terminator and Ex Machina that have mythologized AI as a threat in disguise — how tech can run amok.
On a more practical level, we now hear about “robots” displacing jobs almost weekly, even in the financial industry, where artificially intelligent robo-advisors have generated much angst and hand-wringing, causing particular distress for wealth managers and investment firms.
In practice, however, AI isn’t scary. Institutions that will be best positioned to succeed in the AI-fueled future are those who view advanced analytics, AI and machine learning as something that should complement client-facing teams, not replace them. They will see the opportunity to combine the power of both human intelligence and artificial intelligence.
Early adopters in the banking industry are already taking steps in this direction. BNY Mellon has introduced robotic process automation into its operations to lower costs. While this initial deployment is really about how BNY can eliminate menial processes and free up more time for employees. After all, staff should have other value-add activities they can pursue that are more important than solving compliance consistency issues, right? The news from BNY was nonetheless a significant bellwether for the issue of AI in the financial industry.
“We can use technology to improve our efficiency and the customer experience.”
— John Cryan, CEO of Deutsche Bank
In fact, the subject of “robotics” ended up becoming a topic at the World Economic Forum in Davos this winter. John Cryan, CEO of Deutsche Bank, acknowledged the critical role technology will play in the immediate future, as banking providers strive to reduce costs and improve efficiency… while also delivering the experience customers have come to expect.
The stance that banks like Deutsche and BNY take towards AI and automation is bolstered by cold hard facts and the harsh realities of managing the bottom line. According to a report from Citibank, the banking industry spends $270 billion — 10% of all operating costs — simply on compliance and regulation, and that’s largely for employees who need to tackle oversight issues. The report also estimates that banks in the US and Europe have also forked over another $150 billion in litigation and conduct charges since 2011. The report’s overarching conclusion? Regulatory technology (or “regtech”) is a massive opportunity in the banking industry.
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Mark Schwanhausser with Javelin is one research analyst who believes AI isn’t just about cost savings and workforce reductions. In fact, he believes the relationship between AI, marketing automation and frontline staff is symbiotic.
“Yes, there is a move to automation,” Schwanhausser says. “But it’s a move to automation that complements the human element.”
Fair point. If AI is essentially used to provide consumers with ideas, insights and information they wouldn’t have thought to ask for to begin with, then the impact is incremental not substitutive. Consumers don’t necessarily want to talk to someone every time they have any financial question, but a data-driven automated marketing program can spur new, additional conversations.
Therefore, Schwanhausser says you can’t view interactions that wouldn’t have occurred otherwise as a threat to employees. Indeed it’s the opposite: AI is used to stimulate increased dialogue with consumers, which actually results in more job security for frontline staff, not less.
The following are three examples of this — ways in which machine learning will impact customer-facing content and power more conversations on the front line.
1. Automate Content Compliance
Machine learning algorithms can identify similarities in document language. They can then leverage these commonalities to automatically “flag and tag,” thereby categorizing those materials that need to meet certain specific compliance criteria. This ensures that documents which must remain compliant do so instantaneously — even when updated — providing a level of oversight, recording and tracking that should satisfy any regulatory inquisition.
Additionally, the algorithmic scanning of keyword combinations can also auto-tag documents based on region and product offering. This functionality of AI keeps content “actively curated” and organized, tailoring each document’s messaging to maximize relevancy for the unique characteristics and needs of each client.
2. Marketing Omniscience
With AI, you can know which materials are likely to move the client forward in essentially any situation. Artificially intelligent analytics take the guesswork out of managing a customer’s banking journey. By usage data collected from previous engagements, a data-driven machine learning platform can automatically recommend — with an unprecedented degree of accuracy — which content will be most effective based on a variety of identifying characteristics: opportunity stage, products used/offered, geographic region, risk profile, financial objectives, etc. These hyper-relevant recommendations shorten sales cycles and enhance the entire client experience.
When AI-powered platforms pull in data from all touchpoints and product lines, they allow managers across the organization to see what marketing materials are working. This capability further refines the overall content experience, while providing opportunities for collaboration among interdepartmental teams.
3. Accelerate Onboarding
When AI is used to uncover what works best and when, it results in a system that is both automated and scalable. It not only radically streamlines how you onboard new customers, it compresses the time necessary to onboard new employees and get them up to a productive level. AI makes it easier to bring new bankers up to speed, so they can start managing their relationships quickly and with more efficiency. This allows banks to scale their hiring more aggressively than otherwise possible, further broadening the avenues for increased revenue generation. Knowing that they are working off a proven content blueprint that generates results, new relationship managers come out of the gate with both the tools and the confidence to succeed from day one.
From manual tasks to regulatory compliance to almost every facet of backroom banking operations — including marketing — the industry is being propelled forward by AI, automation and machine learning in ways that complement employees’ work, deliver meaningful value to customers, improve the overall experience, and return value to shareholders’ investments.
Sure, data analytics can be a bit intimidating, but you have to admit: that kind of strategic calculus definitely adds up.