Blog Financial Institution
Artificial Intelligence is at the heart of a seismic shift in the financial industry, and we’re just beginning to feel the tremors of a far-reaching revolution in how banks conduct business: machines are the future, and early adopters have a serious advantage. Midsize banks and credit unions can’t afford to be left behind, but it can be difficult to make a case for artificial intelligence integration without first understanding its colossal impact on banking. Here, we’ll explore how AI is changing banking and its future financial impact on the financial industry.
Over the next 10 to 15 years, analysts predict that AI-powered applications will create $1 trillion in savings for the financial industry. Those savings will be achieved through a mix of back and front office efficiencies that encompass everything from improved data processing and reporting to shifts in staffing levels and automated customer service.
One trillion dollars is a huge number, but it doesn’t exactly explain the impact these applications will have on midsize FIs competing for market share. It’s easier to translate the power of AI for your bank when you consider these figures:
Imagine what those results would do for your bottom line! Clearly, AI is poised to spur unprecedented gains in the financial industry – for those who are prepared. Already, more than 70% of big banks have planned to implement artificially intelligent solutions for front- or back-office functions.
Unfortunately, midsize banks are struggling. Just 2% have deployed similar technology, and only 13% plan to invest in AI in the year future. More than 50% of midsize banks say it’s “not even on the radar.”
It’s a challenge, for sure, but forward-thinking midsize banks and credit unions should recognize it as an opportunity. If your direct competitors are ignoring its promise, now is the time to begin implementation.
Still, it can be difficult for even the most progressive thinkers to convince leadership and stakeholders to invest in AI. That appeal is much easier, however, when you can confidently explain exactly how it can improve your margins.
Analysists predict that AI-powered applications will boost revenues by 34% by the year 2030. How? By leveraging the power of machine learning. Deep learning applications can scan millions of records and examine consumer behavior to identify motivations and sales triggers. Computers can then automatically apply that knowledge to deliver targeted messages to customers and motivate response. Keep reading for specific examples of how technology will fuel revenue growth.
Delivering the right offer, to the right people, at the right time is the foundation of sound marketing practices. Bankers draw on experience and intuition to try to achieve that trifecta and drive customers to their branches. AI can take a deeper dive, however, and automatically deliver personalized offers customers are likely to act on. And it can do it without the need for staff intervention: organizations that implement chatbots and virtual assistants for customer service realize 30% higher sales conversion rates.
Similarly, AI can learn consumer behavior trends and automatically suggest upsells and cross-sells to customers who are apt to take advantage of them. It can also suggest appropriate upsells and cross-sells to banking staff who have face-to-face conversations with customers.
Consider your current web interface. You probably post banner ads and pop-ups to automate upsells and cross-sells. Sure, sometimes they work – but are they as efficient as they could be? They don’t necessarily offer superior targeting.
What if, instead, a chatbot greeted customers by name and voice? What if they initiated conversations based on a user’s transaction history? For example:
“Joe Treasury, I see you sent five international wires last week. Did you know other electronic payment options are available at less cost?”
Unsurprisingly, customers are more likely to respond to that prompt than click a banner ad. And the technology is here: in our scenario, the interface could complete simple upsells on its own, or transfer warm leads to a banker for further discussion.
Take financial advice from a machine? It’s not that far-fetched, and it can yield bigger dividends than personalized advice that’s prone to human error. The reality is people can only process so much data, not to mention they’re tasked (and possibly fatigued) with juggling multiple accounts at once. AI can funnel data through numerous neural network processing layers and deliver solid market advice that builds wealth and keeps customers coming back.
AI-driven applications can recognize warning signs that a customer is about to jump ship: reduced platform login frequency and large withdrawals, for example. Through constant monitoring, computers can automatically alert banking staff when high-valued customers exhibit these warning signs, giving staff a chance to intervene and save accounts.
These automated processes not only grow revenues on their own, they free banking staff to focus on deeper, more valuable engagements with customers. That, in turn, yields greater profitability by helping you provide a better customer experience, foster trust and, ultimately, earn more sales from priority customers.
Increased revenues are just one benefit of AI implementation. Banks know saving is the name of the game, and there’s no better way to cut costs without jeopardizing quality of service than with artificially intelligent applications. In fact, the technology can deliver a better customer experience because it frees staff to focus on customer satisfaction and retention. Let’s look at some real-world savings scenarios.
Chatbots and virtual assistants can answer customer questions, onboard new customers, and help customers manage their own accounts. No longer will banking staff be needed to move money between accounts, help customers reset their passwords, or find a copy of a months-old bank statement.
Moreover, image recognition programs can eliminate the need for passwords altogether through advanced biometrics and facial recognition – another way to enhance the customer experience, save staff time and reduce costly security breaches (more on that below).
Through the power of Natural Language Processing (NLP), computers can interpret and respond to human communication in text or speech forms. It can even be used to interact directly with customers via virtual assistants like Siri and Alexa. Chatbots can be deployed on platforms such as Facebook Messenger, reaching customers in a familiar environment they’re comfortable doing business in.
Indeed, when organizations implement AI for customer service, they realize a 33% savings compared to a call with a live agent. Not only that, but they receive up to 70% fewer calls and email inquiries – a massive savings in staff time. When computers provide fast, accurate assistance for simple, yet important, customer needs, you’re able to free staff time to focus on revenue growth efforts.
Experts say banks that implement AI in their back offices will realize a 22% reduction in operating expenses. Those savings come through saved staff hours and error elimination. The real-world scenarios below illustrate how operational efficiency can be improved through technology.
Integrated receivables are a high priority for 70% of banks, and for good reason: NACHA estimates more than 60% of ACH payments arrive separately from remittance information. These “stranded” receivables force staff to track down email remittances and manually enter data – delaying posting, lengthening DSO and negatively impacting cash flow.
AI-powered applications can use intelligent automation to analyze vast amounts of unstructured data and reassociate payments with corresponding remittances, all without human intervention. In fact, AI can increase straight-through processing rates by up to 95%. How many staff hours would that save at your bank?
Using staff to analyze and organize unstructured data results in tedious, error-prone and costly work. AI utilizes algorithms and Robotic Process Automation to quickly automate workflows such as contract reviews and reporting, eliminating the need for human involvement. Over time, AI learns to become even more efficient. Ultimately, this will lead to billions of saved dollars across the financial industry.
It’s no secret fraud costs banks money. Even if you’re able to reclaim funds lost through fraudulent transactions, you must relegate staff to fraud management. Sophisticated algorithms can prevent fraud by scanning millions of credit card transactions to detect and flag potentially fraudulent transactions.
Moreover, artificially intelligent applications can automate Know Your Customer (KYC) and Anti-Money Laundering (AML) compliance. Such tools review and extract data from a variety of sources during onboarding or examine millions of transactions to quickly flag suspicious activity. Again, this ultimately results in savings for your bank.
Finally, AI supports reliable, stronger credit decision-making. Lending platforms can analyze millions of data points and process them against both traditional and non-traditional criteria (such as borrower education and job history) to arrive at instant credit decisions. The financial benefits are three-fold: banks minimize risks, they can confidently invest in high-value customers and they can quickly lend funds to avoid losing business to competitors. Not only that, but again – human intervention is not needed.
We’ve demonstrated how AI will save the financial industry tremendously over the next decade, and how those savings will impact forward-thinking midsize banks and credit unions. We’ve also shown that most midsize FIs are woefully behind – and how the gap will only increase over time. So, how can you get started with AI?
Convincing leadership and other stakeholders is one step, and the information presented here should at minimum prick their ears. After all, who doesn’t want to increase revenue by 34% and save 22% on operational expenses? Demonstrating that AI is the pathway to such results should be easy enough, especially when you consider the challenge and opportunity: do nothing, and you risk falling behind competitors and losing market share. Act now, and you can gain a steep advantage over competitors and dominate market share.
The next step is to explore AI partnerships. You don’t need to hire in-house talent to implement AI in your financial institution. Rather, you need an outside resource that understands needs and challenges within the banking industry and how AI can address them.
In fact, addressing business needs first is a staple of successful AI implementation. A common pitfall occurs when organizations attempt to harmonize every aspect of their data before moving forward. Business and end-user needs should be prioritized, which allows you to address data architecture and quality issues incrementally as you progress toward full AI functionality. Under this model, both the data and AI improve over time.
Don’t be afraid to consult the experts. Outside resources can accelerate your time to market and speed learning while allowing you to retain your core focus on customers and financial services. They work in lockstep with your internal talent to implement solutions that save money and fuel revenue growth. AI partners can even help you make your case to bank leadership and stakeholders. Have conversations with potential AI partners about your challenges, and they’ll be able to identify AI-driven solutions and opportunities for growth – perhaps even some you haven’t thought of.
Banks that successfully implement AI start small and seek quick wins. They launch pilot projects and measure the outcomes, which can be a powerful way to convince leadership and stakeholders to invest additional funds into AI. They make AI cross-functional across the organization by creating an AI Center of Excellence that collaborates between departments, similar to a shared services model. They build digital cultures predicated on defined roles, responsibilities and leadership.
There’s a lot that goes into AI implantation for financial institutions, which underscores the importance of working with an experienced partner capable of integrating proven AI standards that have immediate impact and ultimately result in greater future gains – all within your current organizational hierarchy and culture.
The future impact of AI for financial institutions is more than increased revenue and reduced costs. It’s going to be necessary to remain competitive in the evolving FI landscape. As the next disruptor for FI products and services, AI isn’t just part of the future – it is the future – and it’s going to be critical to your bank’s survival. The future starts now.