AI is hardly a new concept for the banking industry, but adoption varies widely across asset classes, departments and more. Generative AI in banking is an opportunity to create tremendous value for banks, but banking professionals at every level need to understand how to smartly implement and manage the risk associated with the technology.
Research shows that a whopping 97 percent of industry professionals said they were either very or somewhat familiar with AI. While bank leaders and rank-and-file bankers alike understand how it works, many feel the pace of innovation may be moving too quickly.
Whether you’re a banking professional at the forefront of this change or working to overcome your own skepticism, it can be helpful to understand why generative AI is so important to banking’s future.
So let’s dig-in to explore AI trends in banking and check out eight real-world use cases for the technology.
Why is generative AI in banking so important?
As every financial institution considers its own technology investments, generative AI is a critical part of a larger digital transformation aimed at creating, sustaining, and scaling value.
Generative AI contributes to value for banks in three important ways:
Creates efficiency
Banks of all sizes still rely heavily on manual processes. These routine, mundane tasks not only claim countless hours, they leave room for error and drive up operational costs. Generative AI is particularly well-suited to handle the data, processing and compliance needs of the industry, potentially unlocking time, staff and budget for more strategic pursuits.
Contributes to stronger financial performance
Generative AI is a game-changer for banks’ relationships with their customers. From always-on support to personalized marketing, banks can more effectively compete for new customers and retain existing ones with AI–contributing to stronger financial performance along the way.
Helps prevent loss
With the unique ability to process data, identify patterns, and make predictions, generative AI is a powerful tool to detect fraud and protect a bank and its customers.
The value generative AI can deliver for banks is immense. According to some estimates, it could be as much as $340 billion per year. But in order for banks to see the return on their tech investment, they need to be smart about the ways they implement generative AI.
How is generative AI being used by banks?
Before a bank–especially a large one– can start executing AI-driven tactics, they must determine an AI operating model to guide their approach and implementation.
McKinsey identifies four generative AI operating model archetypes for banks:
- Highly Centralized: A central team oversees all aspects of generative AI initiatives.
- Centrally Led, Business Unit Executed: Strategic decisions are centralized, while execution is delegated to business units.
- Business Unit Led, Centrally Supported: Business units lead generative AI projects with support from a central team.
- Highly Decentralized: Business units independently manage generative AI initiatives.
McKinsey’s research further shows that a centrally led model boasts the highest success rate in scaling generative AI in banking. As your institution considers its broader AI strategy, this model may lead to the greatest value creation.
Benefits of AI for banking
With the ability to solve for some of the industry’s greatest challenges, the potential for AI is astounding in banking.
Imagine the time and resources you could save if your bank could:
Automate routine internal tasks to reduce costs
Generative AI is well-suited to handle repetitive, manual tasks associated with data entry, account servicing, and reporting. With workflows and automations from Capacity, your team can streamline manual processes and automate next steps, ultimately freeing up time to focus on higher-level work.
Deliver faster, always-on support
Customer expectations are higher than ever and they want support outside of traditional banking hours. AI-powered Intelligent Virtual Agents, including chatbots and voice agents, can answer routine questions at all hours and keep your customer service team focused on complex issues.
Higher deflection rates can decrease costs and deliver instant answers to customers, increasing satisfaction. SMS automations can further streamline call centers and automate follow-ups across channels.
Improve customer service representative efficiency
Bank customer service representatives can only be as good as the information available to them. When knowledge is hard to find, call times increase and customers get impatient. Capacity’s Answer Engine® gives customer service representatives real-time access to customer data, internal documents, and policies to ensure they can resolve issues accurately and quickly.
Enhance compliance and risk oversight
Generative AI can help bank employees at all levels maintain compliance and reduce risk for the institution. A wide range of AI tools can analyze communications, flag non-compliant activity, and document violations.
What’s more, AI-powered automations and workflows can ensure compliance review processes aren’t a bottleneck in the flow of business.
Grow accounts and product revenue
With generative AI, every employee responsible for driving revenue can easily identify opportunities with customers and personalize an automated sales approach.
Tools like Capacity Analytics, offer real-time insights and can help your team predict demand. Whether it’s identifying a new product for an existing customer based on a recent interaction or surfacing a cross-sell or upsell opportunity from historical data, generative AI can increase the efficiency and effectiveness of your sales organization.
Shorten onboarding and training time for new employees
For institutions with large or geographically-dispersed workforces, it’s a persistent challenge to effectively onboard new employees. AI-powered knowledge bases can help your team transition away from tribal siloed knowledge and instead rely on a continually updated and consistent source of information for new hires. With the right information, every employee can ramp us faster.
Managing AI risks in banking
For all of the promise generative AI brings to banks, it’s not without risks. For the industry professionals who report that the pace of innovation may be moving too quickly, risk may be at the forefront of their concerns.
When it comes to implementing generative AI across financial institutions, there are several risks to be aware of:
Transparency
The Black Box problem in AI refers to the difficulty in understanding how complex models, especially deep learning systems, arrive at their decisions or predictions. This lack of transparency makes it challenging to interpret, trust, or verify the AI’s outputs, especially in high-stakes use cases.
Customer privacy
Banks handle customers’ most private personal and financial information and a data breach can be catastrophic for trust in an institution. When implementing AI, banks need to seek systems with airtight access controls, encryption features, compliance checks and more.
Regulatory compliance
As financial regulations continually evolve, banks must ensure their AI systems do not unintentionally create exposures that draw unwanted attention from regulatory bodies. While there are risks, AI-powered tools and systems can help banks more effectively document processes, log interactions, and stay audit-ready.
Implementation and integration complexity
Whether your institution is on the cutting edge or working with legacy systems, integrating AI is more difficult than in other industries. Bank leaders should seek systems that offer options for data storage and integrate flexibly with other tools like CRMs and document repositories.
Low AI literacy and internal resistance
Understanding of AI may vary widely, especially in large institutions. Low AI literacy leads to misunderstandings about how AI works, causing fear, mistrust, and unrealistic expectations.
In one study, just 31% of decision-makers said employees were excited about AI in the workplace. But interestingly, the same study found that 73% of workers want their company to explore more ways to bring AI into the organization.
While there may be resistance to adopt or misunderstanding, many employees could be relieved of repetitive work with the help of new technology. AI providers who provide implementation support and easy-to-train tools can help teams embrace the transition.
Eight real-world applications of generative AI in banking and finance
The possibilities are endless for banks and generative AI, but like all tech implementations, financial institutions should cater their strategies to their scale, target customer, and employee skill level.
We get it; the options can feel overwhelming. So here are a few use cases to help get you started.
Customer service and support
Generative AI for customer service can be transformative. In the world of conversational banking, AI-powered intelligent virtual agents can handle tickets across chat, SMS, voice and more, so that human agents don’t have to.
Drive down costs and increase first contact resolution rates with quicker and more accessible support.
Customer service representative assistance and training
AI-powered agent assist tools can help human customer service representatives access the knowledge and training they need to effectively and efficiently handle complex customer inquiries and issues. For example, agent assist tools can flag when a customer is feeling frustrated, enabling agents to get the conversation back on track. With the right support and training, customer service representatives can cut handle times and improve customer satisfaction.
Credit approval and loan underwriting
Banks can use generative AI to analyze vast amounts of financial data, including credit history, income patterns, and spending behavior, to assess a borrower’s creditworthiness more accurately and efficiently. In loan underwriting, AI can help your team automate decision-making, reduce bias, and speed up approvals while staying compliant.
Debt collection
With the power of AI, banks can optimize debt collection by predicting which borrowers are most likely to repay and identify the most effective times and channels for contact. For example, banks can use automated SMS outreach to reach customers who may not read or respond to traditional mail or email. AI-powered chatbots and automation tools can streamline communication across channels with customers, improving recovery rates while reducing operational costs.
Fraud detection
AI is particularly effective for fraud detection because it can analyze transaction patterns in real time to identify unusual or suspicious behavior that may indicate fraudulent activity. With AI, banks can detect fraudulent account activity by geography or even the type of transaction. They can also securely verify customers with enhanced biometrics. AI tools can continuously learn from new data, allowing them to adapt to evolving fraud tactics.
Personalized marketing
Banks use AI to analyze customer data and behavior, enabling them to deliver personalized marketing messages, product recommendations, and financial education tailored to individual needs. This targeted approach increases engagement, improves customer satisfaction, and boosts the effectiveness of marketing campaigns. For example, AI can flag mortgage customers who may be eligible for a refinance and then automate a cross-channel campaign to get them engaged with a loan officer. All-in-one AI platforms, like Capacity, can help banks launch and manage proactive, outbound campaigns across channels.
Employee training
With the ability to personalize training based on someone’s role or learning needs, AI can be invaluable for employee education. Say “so long” to dated onboarding binders and instead provide new hires consistent and up-to-date information. AI also helps simulate real-world scenarios, enhancing skill development and improving retention through interactive, adaptive learning experiences.
Investment advisory
Increasingly, banks are using AI-powered robo-advisors to offer automated, algorithm-based investment advice tailored to clients’ financial goals, risk tolerance, and market conditions. These systems provide cost-effective and accessible wealth management services. In some cases robo-advisors have minimal human intervention and in others financial institutions use robo-advisors to assist human financial advisors and wealth managers.
Wrapping Up
Generative AI can be inspiring and overwhelming to banking professionals. What’s most important to understand is that AI presents a big opportunity for your financial institution to create value and transform itself for the future.
With the right tools and implementation, AI can solve some of the industry’s greatest challenges and help every banking professional reduce the amount of time they spend on routine tasks. Imagine what you could accomplish with more time!
Want to learn more? Request a demo to see how Capacity can help lower expenses, improve customer and team satisfaction, and create more value for your bank.
Not sure where to start with AI?
FAQs
AI is widely used by financial institutions of all sizes to automate internal processes, improve customer service, enhance compliance and increase the effectiveness of marketing.
Yes, AI provides fast, accurate and personalized support, improving customer experiences. It reduces friction by offering 24/7 self-service and proactive assistance. AI-driven insights help tailor interactions, boosting satisfaction and loyalty.
Yes, AI enhances security with encryption, fraud detection and biometric authentication. It ensures compliance with banking regulations like GDPR and SOC 2. AI also monitors transactions in real-time to prevent fraud and unauthorized access.
As banks continue to adopt and optimize AI, they will be able to provide increased personalization and support for customers. Additionally, bank employees will spend less of their time on routine tasks and manual processes.