As digital technology continues to change the shape of the financial services industry, customer expectations are trending in a much more personalized direction.
This isn’t exactly “news” to most financial services professionals; 84% of them acknowledge the importance of knowing their customers well enough to provide them with more personalized offerings.
Customers, on the other hand, wonder whether their financial providers have truly taken their demands for personalization seriously. In fact, one survey found that only 31% of customers agree with the statement: “My bank knows me and my financial needs very well.” On average, only 24% agree that their banks know their needs at all.
What’s more, According to the same study of large national or regional banks (ostensibly those with resources to implement wide-scale advances in personalization), only 17% consider themselves advanced. 53% have made some baby steps and a very sad 30% haven’t even bothered.
Algorithms to the Rescue?
What’s a bank to do? Clearly, personalization requires a personal touch. But, how can financial service providers ever hope to muster the resources necessary to deliver on such bespoke expectations?
Enter the power of big data.
Writing for the Financial Brand, Jon Ogden gives an apt summary of Yuval Harari’s Homo Deus. In it, Harari makes the case that “[the] transition to super intelligent algorithms will give humans access to almost ‘godlike abilities’, including the ability to know precisely what we should do with our money to find long-term well being”.
Ogden applies Harari’s futuristic vision—dataism, he calls it—specifically to the financial services industry. Customers, in search of more personalized banking solutions, are willing to fork over more and more of their data. As they do, banks will have a deeper well from which to draw pertinent customer data.
Artificial intelligence —specifically machine learning—and communities will be the key to extrapolating and synthesizing useful trends from this data to predict customer behavior and tailor offerings to match.
What Does That Look Like?
Here are a few ways financial service providers are already using AI to provide personalized insights and offerings based on a customer’s unique financial picture:
- Spotting Unusual Activity – Banks have been using data analysis for years to identify potential fraud and identity theft scenarios. As neural networks grow and the machines ‘get smarter’, this form of identity protection will only get stronger.
- Highlighting Changes in Spending – Budgeting apps like Mint have been aggregating and analyzing data for years to alert users to precipitous jumps in spending behavior. Using this type of analysis, banks can offer personalized financial advice without shelling out resources to assign a personal advisor to each customer.
- Contextualized Warnings – Deeper analytics allow for more contextually sensitive alerts and warnings. For example, a more sophisticated bank/financial institution will monitor account levels and spending patterns in order to alert its customers that their checking balance may be too low to cover an upcoming mortgage payment.
- Relevant Product Recommendations – Machine learning algorithms can be employed to identify and analyze the correlation between credit card spending and personal lending. In English: a local credit union can train its AI to recognize when customers are getting into trouble with credit card debt and trigger a low-interest personal loan offer at just the right time.
- Contextualized Third Party Referrals – Similarly, banks can partner with third parties to offer products and solutions precisely targeted to fall within the customer's normal sphere of spending behavior.
By Using the Power of Community to Broaden ‘Self-Service’
So how can banks take advantage of fintech to provide more personalized self-service solutions to their customers without diminishing their brand? The answer, of course, isn't to back away from technology. Nor is it to attempt to beat the startups at their own game. Instead, banks must embrace these new technologies and offset their disintermediating impact with a healthy dose of online community.
By developing and utilizing a robust online community of customers and advocates, banks enable their customers to serve themselves by serving one another.
To be sure, ‘community’ isn’t a panacea for every challenge faced by financial services in the digital age. Nevertheless, there’s power in numbers, which–when wielded appropriately–can strengthen a bank’s brand without necessitating more than just a modicum of input and direction.
The reality is, a community can't conduct an automated spending analysis. A forum isn’t able to monitor your account for suspicious activity. A user-generated knowledge base isn’t able to notify you how much more you paid in bank fees this month and offer tailored product recommendations. That’s where evolving financial technologies come in.
But what a community can do is help customers find answers to the mundane questions that take up valuable call-center time, like: how do I open a new account, how do I set up automatic bill payment, how do I transfer money between accounts?
More importantly, digital communities offer space for customers to seek personalized advice and offer contextualized solutions with limited labor input from the bank itself.
As the digital marketplace (in finance and beyond) continues to trend towards personalization, financial service providers will have no choice but to use AI to meet those demands.