AI in banking has the potential to transform the customer experience, drive rapid innovation and much more. See how AI is disrupting banking in this blog post.
The banking industry isn’t exactly known for its innovation, with many financial institutions (FIs) favoring legacy systems and processes over emerging technology. That began to change back in 2020, when the COVID-19 pandemic accelerated the use of digital technology, including artificial intelligence (AI). This global shift toward digitization turned what was once a futuristic concept into a common fixture in the average banking customer’s everyday life.
As the times have changed, so too have the expectations around using AI in banking and finance. Today, both FinTechs and established FIs alike are exploring use cases for AI in banking, but challenges remain. FIs continue to lag when it comes to implementing AI across their organization due to a combination of factors, from a lack of actionable strategy to fragmented data assets and outdated operating models impeding cross-team collaboration.
To maintain their competitive standing within an increasingly crowded market, banks, credit unions and other FIs will need to further explore potential AI applications in the banking industry — and this article will provide insights on where to begin.
Why financial institutions need to adopt AI
FIs have long benefited from implementing new technologies, from ATMs, to card-based payments, to online banking. AI is the next logical step in this evolution, enabling FIs to automate business-critical processes and deliver scalable customer service through chatbot technology.
More recent advancements in AI, including automated data collection, advanced analytics, natural language processing and generative AI, have the potential to drive more informed decision-making, reduce operating costs and increase customer satisfaction. These gains can have a major impact, especially to FIs’ bottom lines: McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year for the global banking industry.
If the potential for revenue generation weren’t enough of an incentive to use AI in banking, FIs are also under mounting pressure to implement AI technology to maintain a competitive edge. FinTechs and other industry disruptors have let the digital banking genie out of the bottle, forever changing customer expectations.
In this digital era, customers have become accustomed to instant, round-the-clock service, which can be difficult to deliver with banks’ existing service teams. FinTechs and other non-banking companies looking to break into the industry are already leveraging AI to supplement their live support teams and meet demand; banks, credit unions and other FIs must do the same.
6 ways financial institutions can benefit from AI
For banks, credit unions and other FIs looking to transform both the customer experience and internal operations, AI could be the answer. With AI in banking, FIs can:
Optimize the customer experience
Always-on, near-instantaneous customer service is no longer a “nice to have” in today’s digital economy — it’s well on its way to becoming a minimum requirement for doing business in the banking industry. AI-powered virtual agents meet the demand for fast, round-the-clock service by instantly responding to all incoming customer service requests and either providing customers with the resources they need or escalating the issue to a live agent who can provide further assistance.
With conversational AI, FIs can take things one step further. Through the use of natural language processing (NLP), virtual agents powered by conversational AI algorithms can hold surprisingly lifelike conversations that address the specific pain points customers are experiencing — a major improvement over the simplistic, scripted responses chatbots are known for. NLP also enables virtual agents to understand the intent behind customer queries and respond with relevant recommendations, creating a more personalized customer experience.
Lower recruitment costs
Like many other industries, the banking industry is subject to seasonality, which can have a direct impact on customer support needs. It’s imperative that FIs be able to accommodate increased call volumes during peak season demand, which typically requires adding headcount to existing service teams. Though necessary, this can take a significant toll on financial resources.
According to the Society for Human Resources Management (SHRM), the average cost per new hire currently sits at roughly $4,700 — but depending on who you ask, it can be much higher. There’s also the time cost to consider. SHRM also reports that it takes an average of 36 days to fill an open role, which could be too long for FIs with immediate customer service needs.
By supplementing live agents with virtual agents, FIs can easily and instantly scale their customer service resources up or down based on demand, no costly or time-consuming recruitment process necessary.
Increase their omnichannel presence
In years past, customers would go to their local bank branch for service, regardless whether they were looking to make a deposit, to receive financial advice or to apply for a loan. While many retail banking customers still enjoy the in-person experience of visiting their local branch, both mobile banking and online banking have overtaken the traditional branch as customers’ primary banking channel. The key takeaway here is that banking now takes place across a variety of channels, and FIs must pivot their service strategy accordingly.
Artificially intelligent virtual agents can help FIs maintain a consistent and cohesive presence across all channels, rapidly responding to inquiries and delivering personalized recommendations regardless of which channel customers choose to interact with. Virtual agents can even help FIs create a more seamless omnichannel experience by facilitating a smooth handoff between channels. Customers can easily initiate a conversation with a virtual agent through one channel and then pick that same conversation back up through another channel, without missing a beat.
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Enhance operational efficiency
Beyond customer service, FIs can leverage AI to automate otherwise manual business processes, reducing the risk of error, streamlining business-critical workflows and enabling them to reassign personnel to value-adding tasks. By enhancing operational efficiency, FIs have the opportunity to reduce operating costs. Juniper Research predicts that, by the end of 2023, banks will have saved $7.3 billion globally on operating costs by implementing virtual agents.
This shift not only optimizes resource utilization, but also has the potential to improve employee engagement, job satisfaction and employee retention. Rather than dedicate their time, focus and energy to tedious tasks, bank employees can instead devote themselves to more rewarding work that requires them to use critical thinking and creativity. AI will also create new job opportunities within FIs, upskilling banks’ existing workforces and enabling them to adapt to changes within their industry.
Improve cybersecurity
Banks, credit unions and other FIs are a prime focus for cyberattacks, with the American Banking Association reporting that more than 60% of global FIs with at least $5 billion in assets were targeted by cyberattacks between 2022 and 2023.
AI algorithms can help FIs combat fraud and other cybersecurity by analyzing customer data, including transaction records, to establish behavioral baselines. These algorithms can then monitor customer behavior in real time, flagging anomalous — and potentially fraudulent — activity. FIs can also use AI to ingest and analyze market data and monitor emerging risk. The potential to enhance cybersecurity through the use of AI for banking is so great that 56% of financial services companies report that they’ve already implemented AI to support risk management.
Tap into new revenue streams
In an increasingly competitive market — one rife with disruption — FIs must do all they can to improve upon their existing products and services and develop new ones to meet customer demand. AI algorithms support rapid innovation by analyzing vast quantities of data, including customer data, market trends and competitor analysis, and generating insights that FIs can use to create innovative products and services tailored to specific customer segments.
Certain AI platforms can use application programming interfaces to integrate with third-party providers, facilitating open banking initiatives. These integrations enable FIs to develop value-added services, such as personalized financial management tools, budgeting apps or alternative payment methods, and open up new revenue streams.
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Applications of AI in banking
There are a wide variety of ways for FIs to leverage AI for banking, with new use cases emerging every day. Some possible applications to consider include:
- Supplementing live customer support representatives with virtual agents to provide 24/7 service without compromising quality of service
- Enhancing security by enabling FIs to better assess risk, detect and prevent payments fraud, improve processes for anti-money laundering and perform know your customer checks
- Supporting bank employees in middle office functions by automating low-value, manual tasks and providing an easily accessible knowledge base for common queries in the form of an internal virtual assistant
- Analyzing loan applicants’ credit history to assess their eligibility and creditworthiness, thereby reducing the risk associated with loan approvals
- Assisting sales and marketing teams by flagging potential upselling and cross-selling opportunities, providing personalized product recommendations and automating marketing campaigns
- Monitoring industry news and market trends and analyzing historical data to generate intelligent insights that inform trade decisions
- Building artificially intelligent robo-advisors to offer investment advice, support portfolio management and provide financial planning services to customers based on their risk profiles and stated goals
- Performing customer sentiment analysis on online reviews, social media posts and customer feedback to better gauge company reputation and identify areas of improvement with existing products and services
- Automating document processing, including data extraction, verification and classification, thereby reducing manual efforts and improving operational efficiency
- Developing new, AI-powered products and value-added services, such as expense tracking and smart savings tools, real-time fraud monitoring and alerting and voice-activated banking services
How to become an AI-first institution
In order to capitalize on AI in banking, FIs need to take a strategic approach to implementing AI technology across their organization. Rather than throw everything at the wall and see what sticks, it’s important that FIs take a measured approach, clearly defining their goals and objectives for using AI and identifying which use cases are most likely to deliver a return on investment. This will provide a solid foundation to the implementation of any AI platforms and increase FIs’ chances of success.
To accommodate AI technology, FIs should reevaluate their existing infrastructure and determine whether they need to modernize. Many traditional banks and credit unions continue to rely on legacy systems to manage day-to-day operations, and these systems may not be compatible with more advanced technologies, including AI. Institutions that fall into this category will need to update or replace those legacy systems in order to integrate with AI platforms. Modernization can also help FIs break down data silos, which allows for more thorough AI-powered data analysis.
Once a bank has done the legwork of preparing for implementation, the next step is to identify the AI solution that best meets its needs. Some platforms cater to specific use cases, while others can support a wide variety of applications; this is where taking a goal and use case-driven approach proves especially valuable.
It’s just as important to evaluate potential solution providers as it is to evaluate potential AI platforms. The right provider should go beyond basic implementation, delivering comprehensive training to a bank’s internal teams so that they can confidently use and continually develop the solution. As noted, implementing AI opens the door to new employment opportunities for current team members, and the right partner will help an FI upskill its talent.
After go-live, FIs must make every effort to encourage user adoption, whether those users are internal team members or customers. It can take some time for people to get used to new ways of doing things but with enough education, communication and support, employees and customers alike will realize the benefits that AI for banking has to offer.
The final step to become an AI-first is to continuously improve. AI is an evolving field, with new advancements and applications emerging every day. With that in mind, FIs should stay up to date on news about AI in the banking industry, explore new use cases for AI within their organizations and adjust their strategy accordingly.
The future of AI in banking
Speaking of AI being an evolving field, let’s take a look at some recent advancements that are expected to shape the future of AI in banking:
Large Language Models
Large language models (LLMs) are a form of AI capable of mimicking human intelligence. They’re able to achieve this by ingesting large quantities of data — often books, articles, web pages or other written content — and analyzing that data to identify patterns and connections between words. This analysis enables LLMs to “learn” and then generate convincingly human text.
This next generation of AI presents significant opportunities for FIs, which can leverage LLMs to improve technical support, onboard and train employees, automate loan origination, provide customer support and much more. It’s important to note, though, that LLMs are not without their limitations — they have a tendency to generate content that may be inaccurate or misleading. Some platforms solve for this by training LLMs on more reliable, internal data sources and then combining them with conversational AI programs for better accuracy and control.
Governance
AI is an incredibly powerful technology capable of great things when in the right hands. As the dialogue around AI and its applications — both in the banking industry and elsewhere — progresses, more attention is being paid to how to use AI responsibly. In the U.S., government officials are in the early stages of determining whether AI should be regulated and, if so, what an AI governance framework should look like.
If there’s one thing that’s clear, it’s that AI governance will continue to be an important topic of conversation for the foreseeable future. But what does this mean for FIs? To manage risk and prepare for regulation, FIs should look to create their own internal governance framework, including:
- Establishing an ethics committee to oversee AI usage
- Periodically reviewing AI-informed decisions across the organization to ensure fairness
- Integrating AI tools into existing risk management frameworks
- Implementing stringent data governance policies
Hyper-personalized service
Although the concept of hyper-personalization is nothing new, AI is pushing the limits of what’s possible. AI platforms for the banking industry have the ability to analyze customer data to develop a deep understanding of customers’ needs and enable FIs to design tailored experiences that meet those needs.
Using conversational AI, FIs can even create robo-advisors to assist retail banking customers at every stage of the customer lifecycle, providing proactive and targeted recommendations to help them achieve their personal finance and wealth management goals. Deloitte predicts that, by 2025, over $16 trillion in assets under management will be managed with support from robo-advisory services.
Looking to implement AI in your institution? Boost.ai delivers conversational AI solutions for the financial services industry — contact our team today to learn how you can capitalize on AI in banking.
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