While a few industry leaders do get it, many discussions around artificial intelligence (AI) in banking shows that the industry at large still views AI in very abstract terms. While banks seem to be thinking about AI more and more, there still seems to be a consistent struggle in understanding when or where to apply this analytic tool. This struggle often leads to hesitation to actually testing and implementing the benefits of AI at financial institutions.
Theory and speculation have surrounded AI for decades, from the idea that machines will eventually “take over the world,” to now seeing early applications via self-driving vehicles and virtual personal assistants. Contrary to what many people believe, AI replacing professionals in the banking industry anytime soon is unlikely. Its real value is in augmentation – to replicate human-like behaviors or tasks, not people at a more rapid rate, leading to new advancements and discoveries.
Developments around AI could not have come at a better time. Advanced analytics is growing rapidly, which inherently benefits AI, though business intelligence doesn’t go far enough. Banks have had data for a while, and have reported on that data for years, but with today’s data being so significant in size, much of it is going unused. Additionally, if financial institutions have made the substantial investment to normalize the data so it can be used, it’s often cumbersome to even get it into a format that’s valuable to the business.
Consider the financial analyst who spends a tremendous amount of time keying in details from a financial statement. What he really needs is a way to remove the tedious nature of his job to shift his time and focus to distilling information, and then take action on what’s uncovered. Or similarly, with a bank’s credit policy – if an organizations knows that a certain number of loans are being waived, a particular number of times, with a specific type of performance, then the institution might want to remove or change the policy because it’s costing the bank. AI offers a deeper analysis, faster, of that broad credit policy and empowers the bank to create change.
With so much opportunity for AI in banking right now, too many players are trying to just either make “cool stuff”, or using it in iterative ways, without focusing on arguably the most important part – how the bank delivers customer and shareholder value. As financial institutions are constantly seeking improvement, where does AI make sense? There are four areas where banks can prioritize their strategy.
1. Enhanced Customer Insights
Understanding your customer involves moving beyond common place demographics. Instead of looking at basic information such as age, gender or income, AI can give a deeper analysis of customers’ behaviors, patterns and financial history.
This is one of those areas that really lets banks start to be customer-centric by knowing as much, if not more, about the customer than they may know about themselves. If you look at what’s been going on in the industry today, this sort of customer insight combined with the speed of AI is in part what lead to an increase in robo-advisors.
Enhancing customer insights, in particular, is one of those areas where AI can really be leveraged to take advantage of amazing pattern recognition to churn through data and discover things that humans either couldn’t, or would take far too long to be effective. The customer expectation of real-time insights and recommendations is only optimized with AI capabilities.
2. Expedited Customer Onboarding
With enhanced customer insights, AI can also help create a frictionless and expedited onboarding process by eliminating steps for the customer and the bank. For instance, chat bots can enhance the onboarding experience when postured as help desk agents.
Chat bots today use AI to solve basic questions faster, automating repetitive or commonly recurring customer requests and other service tasks easily. This also extends a bank’s pursuit of delivering more on-demand service and support across channels. These sorts of capabilities can help us move past just wanting to not “re-key” information, and into a realm where we can start talking about eliminating keying entirely.
Taken a step further, AI can be applied to the deployment of a voice digital assistant process, allowing the early engagement with the consumer to be simplified beyond what could be done with traditional keystroke entry. Moving the new customer through engagement and new product sales by voice prompts can improve and personalize the customer journey.
3. Improved Risk Management
Considered as the heartbeat of a bank, risk management has the largest opportunity for incorporating and strengthening the use of AI. Without strong, sound risk practices, banks leave profits and reputation vulnerable. With AI, risk modeling and pricing are more advanced, optimized without the need for a rate sheet.
Consider how Amazon and other retailers are implementing dynamic pricing, with real-time adjustments personalized on a customer-level basis. Today’s bank and credit union pricing models largely tend to be sub-optimized, where profitability could be defined on a relationship level which could benefit both the bank and customer.
There is wonderful momentum in AI being applied to fraud detection and prevention in our industry. Fraud tools are more effectively mining data to uncover meaningful patterns, which then translates to information bankers can use. Being able to identify accounts, customers or transactions, for instance, that have unusual characteristics can expedite consideration of anomalies and verify suspicions to the likelihood that fraud is taking place.
The automated analyses banks field today for payments are only possible because of AI, allowing for inclusion of device and locational analysis. Probing such large amounts of data manually could not be done as quickly or reliably by humans alone.
4. Increased Operational Efficiency
Finally, many areas in operations can begin to be automated through AI. Whether it’s leveraging something like robotics, bubbling up details about existing customers, or leveraging chatbots to improve the customer experience, there’s tremendous opportunity to make operations more efficient through AI.
At the heart of every financial organization there are critical systems and processes driving operations across functions including product management, distribution, customer service, and resource management to name a few. The continuous testing, refinement and optimization of the many variables, behaviors and configurations that drive the outputs of these systems could lead to significant competitive advantage, productivity improvements, and cost savings.
The biggest challenge for any enterprise looking to optimize any one of these operations up to now has been the breadth and complexity of the effort. Manually modeling and optimizing every variable within multi-dimensional systems outruns the time, budget and skillset of many development teams. Now, you can leverage internal skills and expertise to program AI models to improve prediction accuracy and real time decision support, resulting in greater operational efficiency from these sophisticated back office systems.
Embrace AI Now
If your organization is having a challenge determining where AI can bring the most value – don’t think about AI. Think about the outcomes that you want to drive for your institution. What would you like to see more of, faster? What are areas of the industry that you know are going to have to change, or should change, over the next five years?
No matter how big or small your organization, with more focus on what actionable outcome AI provides, rather than AI as a technology, virtually any financial services organization can solve daily hurdles in new ways.
There are immeasurable volumes of overwhelming data available, but being able to have a global picture of what is occurring, by making sense of data, is the most important. The real value isn’t just in providing a simple answer because you have the data; it’s about providing insights that you can take action on right now.