The Future of Financial Decision-Making Lies With AI

By Dr. Hossein Rahnama, Founder and Chief Executive Officer of Flybits

As the landscape of financial services continues to change, it’s critical to stay ahead of the game. ATMs were groundbreaking achievements once upon a time, while more recently, mobile baking was the logical next step in banking’s maturation.

As in most cases, technology plays a large role in that evolution, helping hungry startups leverage new tools to disrupt the old guard. In data intelligence, Bank of America is taking the lead with its Erica chatbot, which, like a bank teller, can access a customer’s account, execute transactions, and even offer financial advice.

It’s an interesting technological application, but true AI has proven to be a scarier concept. We’re used to giving machines instructions for how to respond to triggers or situations, but a paradigm shift has occurred in which the computers are looking at a lot of data, understanding patterns, and starting to make their own decisions based on those patterns. This is a fundamental shift in terms of how computing will change, how we write programs for a computer, and how we consume digital services. Because of that, true AI brings up issues of governance, ethics, and trust.

But it also means greater capabilities that apply to a variety of industries, which is why big players like IBM, Salesforce, and SAP lead in implementing AI technology. It’s a multibillion-dollar industry that’s projected to grow exponentially, and it won’t be long before AI finds ways to establish a stronger footprint in the financial industry. But there is a key difference between true AI and what we perceive to be AI through grammar-driven chatbots. Understanding those distinguishing characteristics will be key to understanding how AI can benefit financial institutions in the coming years.

The Rise of AI in Banking

Banks aren’t dealing with life-or-death scenarios like missiles and car crashes. That said, they can inform a customer’s short- and long-term decision-making, from filling up a tank of gas to saving up for a first house or a child’s education. AI can be a complementary resource for those scenarios and several others.

Today’s AI relies on feeding millions of data points to help complete split-second decisions from mortgage prequalifications to credit limit upgrades. These mounds of data and the contracts to access them are actually what slow the banking industry down, so it almost seems like a symbiotic relationship that was meant to be.

Bank tellers have become more like marketers due to ATMs. Applications of AI like Erica can perform account maintenance, verify funds, and do background checks more quickly and cheaply than a human would. U.S. Bank teamed with Salesforce to develop Einstein, an AI platform that allows customers to access services on their mobile devices, on a desktop, and in person. JPMorgan Chase’s COIN, or contract intelligence, application uses machine learning to complete commercial loan agreement contracts, a move that could eliminate as many as 360,000 hours of annual work for lawyers and loan officers.

U.S. Bank and JPMorgan show AI’s capability to assist with routine and high-level financial tasks. Institutions are facing stiff competition from new companies armed with everything from cryptocurrency and blockchain tech to crowdfunding and cloud-based tools; AI and machine learning can help in that adjustment. Using the technology correctly and in a more scalable fashion is the solution that will keep big players in the game, but it’s not without barriers.

Embracing the New Paradigm

AI and machine learning’s strength rests in their powerful abilities to process and resolve complicated issues. But cognition and institutional buy-in are still needed. For example, a computer needs to be fed millions of images of muffins to recognize one with 60 percent accuracy; by contrast, my 2-year-old daughter just needs to see a muffin once to know what she’s asking for.

A 2016 PricewaterhouseCoopers study revealed that two-thirds of U.S. financial organizations are limited by budgets, regulations, and resources in AI implementation. If you think onboarding a new employee is difficult, imagine how hard it is to transition a Google neural network into financial documents.

On top of this, customer-facing AI requires public education and comfort levels. A recent ServiceNow survey found that two-thirds of HR leaders believe employees are comfortable accessing AI chatbots to obtain work-related information. That’s a step in the right direction, and an influx of consumer-facing chatbots is helping even more.

But AI isn’t just about chatbots. Consider a bank’s call center: Calls are constantly recorded for quality purposes, but only a small percentage of calls are ever actually monitored by quality control reps. AI can monitor all calls to find patterns among individual reps, customers, and much more and can even generate insights from these calls while protecting customers’ privacy.

This balance between technological innovation and human context can provide a boost for any bank that adopts it. An Accenture study revealed that banks that pour money into AI and human collaboration at the same rate as top companies will see a 34 percent increase in revenue and their employee retention numbers improve by 14 percent.

Our current relationship with banks is purely tactical. Regardless of whether you’re online or in a branch; you’re essentially there to perform a task like checking your account balance or paying a bill. It’s a one-to-one relationship, but that isn’t a realistic depiction of financial reality. Financial services cover so many areas and AI can assist in doing so with human assistance.

We don’t always like to admit it, but our financial health drives a lot of our life decisions. Whether it’s our jobs, the trips we’ve planned, or our worries about retirement and aging, money factors heavily into all our choices. Banks have the power to leverage financial data to drive consumer behavior. Instead of just holding money and reporting our account balances or credit card debts, banks can provide personalized recommendations, insights that tell a customer the right time to ask for a raise or how much he can spend for his next car. AI will allow banks to be more ingrained in the life of the customer.

Understanding the context behind why someone is performing an action with his financial accounts isn’t always easy for humans to do. Future AI tools serve as another set of eyes and ears that can help wealth managers contextualize financial decisions and provide better-informed financial advice to their clientele. The future of AI enables banks to go beyond classic core banking and become a more horizontal lifestyle player.