Harnessing AI for Process Automation in Banking: Smarter, Faster, and Yes More Human
By Katie Reiser
Banks face mounting pressure to streamline operations, enhance customer service, and stay competitive in a fast-evolving digital world. Artificial intelligence (AI) is no longer a buzzword — it’s a key driver of modern banking operations. Among its most impactful uses: process automation.
AI-assisted process automation falls into two main categories: Robotic Process Automation (RPA) and Business Process Automation (BPA). While similar, they serve distinct functions.
RPA vs. BPA: What’s the Difference?
(In the spirit of embracing AI, I asked ChatGPT to explain the difference.)
Robotic Process Automation (RPA) involves using software “bots” to mimic repetitive human tasks. Common examples in banking include extracting data from forms, moving data between systems, and sending routine notifications. When paired with AI, RPA becomes smarter — bots can understand natural language, process unstructured data, and even make basic decisions. For instance, AI can enable bots to read loan applications and extract relevant data, reducing manual input errors.
Business Process Automation (BPA), on the other hand, focuses on end-to-end workflows. AI-assisted BPA doesn’t just mimic tasks — it analyzes and optimizes entire processes. Think of onboarding a customer: BPA can automate ID verification, credit checks, and document collection, all while AI determines the fastest and most efficient path based on real-time data. BPA is typically broader in scope than RPA and integrates more deeply with business systems.
Streamlining Banking Operations with AI
• Customer Service: AI chatbots and virtual assistants can manage routine inquiries 24/7, freeing staff to handle more complex needs. Bank of America’s Erica handles millions of monthly interactions, helping with tasks like transaction searches and bill reminders.
• Fraud Detection: AI rapidly scans transactions for anomalies, flagging potential fraud in real time — faster and more accurately than manual reviews.
• Loan Processing: AI tools analyze credit reports, verify income, and assess risk more efficiently. JPMorgan Chase has slashed contract review times from thousands of hours to seconds using AI.
• Compliance and Risk: AI helps monitor regulations and analyze internal policies. Automation reduces errors and allows teams to focus on higher-value compliance work.
• Back Office Operations: AI driven RPA automates data entry, reconciliations, and report generation, cutting turnaround time and cost.
Making Work More Human
Rather than replacing employees, AI often enhances their roles. By offloading repetitive or low-value tasks to automation, bank employees can focus on work that requires judgment, creativity, and human interaction.
For example, instead of spending hours manually checking documents, a loan officer can spend more time consulting with customers on the best financial products for their needs. This not only improves employee satisfaction but also enhances the overall customer experience.
Kathy Strasser, EVP, chief operating officer/ chief information officer with IncredibleBank in Wausau, was a panelist for the popular AI in Action session during WBA’s Bank Executives Conference. When asked by panel moderator Eric Cook, chief digital strategist with WSI Digital (who also runs GSB’s Digital Banking School and teaches for the WBA’s School of Bank Management), about reassuring staff who may be uneasy with AI and concerned that it will ultimately replace employees, Strasser shared, “It’s about helping our employees get rid of the mundane part [of their jobs] so they can do the higher value things they want for their career path.”
Pitfalls and Considerations
While the benefits are compelling, banks should be aware of the challenges and risks associated with AI-assisted automation:
• Data Quality: AI models are only as good as the data they are trained on. Poor data quality can lead to incorrect decisions or biased outcomes.
• Over-Reliance on Automation: Not all processes should be automated. Some tasks require human oversight, especially when ethical or legal considerations are involved.
• Cybersecurity and Privacy: Automated systems that handle sensitive customer data must be secure and compliant with data protection laws like GDPR or GLBA.
• Change Management: Integrating AI into banking operations requires a shift in mindset and process. Employee training, clear communication, and stakeholder buy-in are crucial to successful implementation.
• Regulatory Scrutiny: Regulators are increasingly focusing on how AI is used in financial services. Banks must ensure transparency and explainability in their AI systems.
Getting Started
For banks considering AI-assisted automation, the following steps can help:
1. Start Small and Have a Goal: Be clear on what you want to accomplish. Often the need to gain speed, cut costs or improve the customer experience are different. Identify low-risk, high-volume tasks suitable for automation.
Gain quick wins and build confidence.
2. Assess Readiness: Evaluate your current tech infrastructure and data maturity.
3. Choose the Right Tools: Consider whether RPA, BPA, or a hybrid approach best suits your needs.
4. Ensure Governance: Establish frameworks to monitor AI performance, ensure compliance, and manage risks.
5. Invest in People: Train staff on new tools and encourage a culture of innovation and continuous improvement.
Also serving on the AI in Action panel were Forward Bank’s COO Sheri Dick and Chris Nichols, Director of Capital Markets at SouthState Bank, who shared their approaches to governance. Nichols said, “We started with a
steering committee and that evolved into an AI working group. This led to a charter and policy which evolved into having a full governance framework that outlines procedures and requirements and then added an AI strategy.” Nichols added, “I’m a big fan of aligning your business goals with your AI goals.”
Dick explained, “We have a governance policy and a practical use policy, but what we did from a foundational standpoint is we had an AI education session for all employees. A lunch and learn to help them understand… What is AI? What are the definitions of AI? What are the practical uses of AI?” She added, “These training sessions got everyone level set and gave us a foundation for each employee and we started from there.”
Regarding to investing in people, Nichols highlighted an unexpected benefit, “It makes for more confident employees. That’s not something that we really thought about when we originally made the AI use case. But what we found when we do our employee satisfaction survey every quarter, the more they use AI, the more confidence they have.”
Putting AI into Action
Cyrene Wilke, Chief Operations & Information Officer, Executive Vice President with Horicon Bank and another AI in Action panelist, offered this advice to banks considering using AI for process automation, “Start with focusing on opportunities to gain efficiencies in the back of the house in operations. Most banks will find processes being performed manually that can be fully or at least partially automated. Finding ways to be more efficient can free up time to provide better, more personalized customer service.”
Cook shared this insight, “I don’t want to make it sound like AI is going to be the panacea that’s going to solve all of your problems, but if it sparks that dialogue in your organization to make you question why you’re doing what you’re doing and [suggests] maybe there’s a better idea, I think that can be a really healthy dialogue for everybody to have.” AI-assisted automation offers banks a path to operate more efficiently, improve customer service, and empower employees.
By understanding the differences between RPA and BPA, choosing the right tools, and planning ahead, banks can move toward innovative and adaptable ways of working.
