AI: the game changer for financial services marketing

Organisations operating within the financial services industry – such as banks, credit unions and hedge funds, to name just a few – are supposed to be some of the most trusted businesses in existence. Why? Well, for one, they handle and transfer extremely sensitive and personal information on a daily basis. And, oh yeah, they happen to look after all of your hard-earned cash. However – and somewhat ironically – one of the primary challenges facing the financial services industry currently is trust.

According to CISI, only 40% of people would trust a financial adviser when it comes to planning advice. That’s not a very encouraging statistic, seeing as one of the primary functions of a financial adviser is to give planning advice.

And if the issue wasn’t concerning enough, trust is also a substantial concern for retail banks as well. Most recently, leading retail bank Wells Fargo took another stab at salvaging its image through a marketing campaign geared towards re-instilling trust in its customers.

However, banks and other organisations within the financial services industry are taking it one step further. Traditional marketing campaigns aren’t going to be enough to address the current challenges, and they know that. This is where the usage of artificial intelligence (AI) comes in.

How AI is being used to address challenges in financial services
Some of the most exciting and effective uses of artificial intelligence in the financial and retail banking world include speed, data, and customer satisfaction applications. However, the financial services industry has lagged behind other industries when it comes to technological advancements, primarily due to the highly sensitive nature of the data these companies must manage. While companies in virtually every industry are clamouring to impress potential users via advanced tech, banks aren’t known for it.

According to Brain O’Donnell, an executive at the Global Risk Institute in Financial Services, “Bank customers can be forgiven for wondering how Facebook and Google can seamlessly anticipate and fulfil their requirements, while their bank of 30 years cannot do the same”.

User engagement, products, and services can be very hit or miss in financial services. Certain features and services can be marketed and implemented, costing considerable amounts, all to fall short in helping companies realise their expectations.

Leading causes for lacklustre results include:

Lack of buyer behaviour knowledge: Financial organisations have relied on demographic data in the past, but such indicators are no longer accurate. The connected world has given data that can point to potential financial decisions, yet some banks and financial services companies aren’t yet taking full advantage of these capabilities.

Outdated procedures: Paperwork and processes from decades past are still in place with many financial service providers, retail banks, and credit unions. With the mix of new and old, errors are more likely to occur, causing a poor customer experience and loss.

Competition: While it’s still in the early phases, there are institutions in the world of money and assets that are utilising AI for their (and their customers’) benefit. Lagging behind will become an increasing drag on performance in the months and years ahead.

How does AI impact financial services and banking?
For many, AI is already affecting day-to-day life, whether they recognise it or not. Companies use AI in ways to both predict the behaviours of their customers as well as serve them better. These programs are built to collect data, process the information, and react to the knowledge in certain ways. There are countless uses for AI, and believe it or not, some banks and financial institutions have been using AI in one form or another for over three decades. However, the biggest strides made possible by artificial intelligence will be those that help institutions understand their customers.

Historically, these advancements have been used by large, enterprise institutions. Speeding up processes, measuring risk, and consistently checking errors are valuable capabilities. More recently, technological advancements in AI and other areas favour all in the financial services industry — as well as consumers. For example, AI influences things like: fraud detection, credit applications, real-time analytics, mobile check deposits, as well as helping to streamline laborious administration processes.

Successful examples of AI in finance and banking

Bank of America’s Erica
Erica is the virtual assistant created by B of A (using AI). It’s a cross between an app and a banking assistant used to do several things for users — including learn. According to Bank of America, “Erica is new and still learning. The more you interact, the smarter Erica gets.”

Bank of America has continued to improve earnings and plans to spend billions in the fintech sector over the next several years. Creating a virtual assistant was a smart move considering the growing number of third-party apps doing the same. By offering their own option, it focuses their messaging and improves the user experience.

Pefin Financial Advisor
Claiming to be “the world’s first AI financial advisor“, this complex program tracks and learns user behaviour on over two million points. Pefin then suggests how to spend, save, and invest money based on the evolving data being interpreted.

It’s inexpensive (compared to many financial advice services), promises never to share data, and is growing in popularity.

What the future holds…
The impact of AI has already been incredible, and I’m sure it will only increase from here. One thing’s for sure, only those organisations dedicated to building teams well-versed in machine learning are likely to succeed. The data gathered about consumers and banking customers will paint a clear picture of the tools and services desired. Those financial services and banking institutions that create and market that experience are likely to ride the waves of disruption.

Author:  Ian Matthews, Data Evangelist, NGDATA

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