What is banking automation and how do banks use it?

Agentic process automation (APA) differs from traditional robotic process automation (RPA) in several important ways, particularly when applied to banking operations. With its ability to adapt, make informed decisions, and integrate across systems, APA enables banks to automate complex workflows more effectively, resulting in better efficiency, customer experiences, and compliance with regulatory standards.

Some of the core differences between RPA and APA in banking:

Level of intelligence: In banking, tasks like fraud detection and customer service require more than just rule-based automation. But traditional RPA is designed to automate repetitive tasks by mimicking human actions, such as data entry or transaction processing, following predefined rules. APA, on the other hand, incorporates advanced technologies like artificial intelligence (AI) and machine learning (ML), to analyze large and real-time datasets, recognize patterns, make decisions relevant to changing situations, and take action. This level of intelligence from agentic AI allows banks to handle complex processes more effectively, from identifying potential fraudulent transactions and initiating countermeasures to providing personalized customer support.

Adaptability: Banking is a dynamic industry facing changing regulations and evolving customer needs. Traditional RPA struggles to adapt to these changes, requiring manual updates to workflows when new scenarios arise. In contrast, APA is designed to be flexible and responsive. It can automatically adjust its actions based on new data or changes in the environment, making it particularly valuable for banks that need to quickly respond to regulatory shifts or market conditions.

Decision-making: In the context of banking operations, making informed decisions quickly is the nature of business, especially during processes like loan approvals. Traditional RPA executes tasks in a linear fashion, following a set sequence without the ability to make independent decisions. On the other hand, APA uses AI agents that can assess many factors—like credit history, income, and market trends—to determine the best course of action. By delivering faster, smarter decisions, APA enables banks to streamline complex workflows like loan processing, while at the same time boosting accuracy—and improving customer satisfaction.

Integration across systems: Banks typically operate with multiple systems that need to communicate effectively. Because traditional RPA is often used to automate specific tasks in isolation, it can lead to data silos and inefficiencies. In contrast, APA is designed to work seamlessly across banking systems and departments. It integrates across technologies and processes, driving end-to-end automation of complex workflows, from customer onboarding to compliance checks. The inherent interconnectedness of APA improves operational efficiency and powers cohesive banking services.

Focus on goals: In banking, achieving strategic objectives—such as improving customer satisfaction or reducing operational costs—is a major part of remaining competitive. Traditional RPA is task-oriented, focusing on executing specific actions without considering broader goals. In contrast, APA is goal-oriented, aiming to achieve overarching objectives. AI agents in an APA system can interpret these goals and autonomously decide how to reach them, leading to more strategically aligned process outcomes for banks.

For banks, prioritizing automation projects is at the heart of achieving strategic operational goals and growth targets. To that end, banks should prioritize automation projects by following a six-point approach to identifying, assessing, and refreshing the list of banking process candidates for automation. This structured approach will help banks maximize the benefits of automation and drive meaningful improvements across operations.

Six key considerations for banks when determining which automation initiatives to focus on:

• Identify high-impact areas: Banks should start by evaluating operations to identify processes that have the potential for significant automation impact. This includes looking for tasks that are repetitive, time-consuming, or prone to errors. For example, automating loan processing or customer onboarding can lead to substantial time savings and improved accuracy, delivering a direct positive impact on customer satisfaction.
• Assess feasibility and ROI: Working from the list of high-impact areas, banks need to assess feasibility, looking at factors like complexity, technology needs, available resources, and expected return on investment (ROI). Based on these assessments, prioritizing automation candidates that promise quick wins or substantial cost savings can demonstrate the value of automation right out of the gate.
• Consider regulatory compliance: Banking is a heavily regulated industry with regulatory compliance constantly top of mind. Many automation projects can directly enhance compliance—such as automating KYC (Know Your Customer) checks or transaction monitoring. Prioritizing these initiatives not only reduces the risk of non-compliance penalties but also streamlines processes that are critical to maintaining trust with customers and regulators.
• Align with strategic goals: It might seem obvious, but banks should ensure automation projects align with overall strategic objectives. Prioritizing projects that support key goals—whether improving customer service, operational efficiency, or expanding product offerings—will help drive long-term success. For example, if a bank aims to enhance customer experience, it may prioritize projects that automate customer service inquiries or streamline loan approvals.
• Engage stakeholders: Involving key stakeholders—such as department heads, IT teams, and frontline employees—in the prioritization process is valuable. Stakeholder perspectives can shed light on which processes are the biggest burden and where automation can make the most difference. Gathering this kind of feedback can help ensure that selected projects have broad support and meet the needs of different teams and departments.
• Monitor and reassess: Once automation projects are underway, banks should continuously monitor their performance and impact. Establishing key performance indicators (KPIs) to track the success of automation initiatives allows banks to reassess priorities based on evolving needs or new opportunities. Regularly revisiting the prioritization process is one of the key ways that banks can remain agile and adapt to market shifts and customer expectations.

When designing automated banking workflows, several important fraud detection and risk mitigation capabilities must be incorporated to protect both the bank and its customers.

Recommended tools for fraud detection and risk mitigation for automated banking processes:

• Real-time transaction monitoring: Implement systems that continuously monitor transactions as they occur. This allows banks to quickly identify unusual patterns or behaviors that could indicate fraudulent activity. For instance, if a customer’s credit card is suddenly used for a large purchase in a different country while their phone is still at home, the system should flag this for review.
• Machine learning algorithms: Use machine learning (ML) to enhance fraud detection. These algorithms can analyze historical transaction data to learn what normal behavior looks like for each customer. By recognizing deviations from these patterns, the system can better identify potential fraud. Over time, ML models improve their accuracy by learning from new data, making them more effective at catching fraud.
• Multi-factor authentication (MFA): Incorporate MFA into automated workflows to add an extra layer of security. This means that, in addition to entering a password, customers may need to verify their identity using another method, such as a text message code or a fingerprint. MFA helps ensure that only authorized users can access sensitive accounts or complete transactions.
• Automated alerts and notifications: Set up automated alerts to notify customers and bank staff of suspicious activities. For example, if a transaction exceeds a certain amount or occurs in a high-risk location, the system can send an immediate alert for further investigation. Quick notifications can help prevent losses and keep customers informed about their accounts.
• Risk scoring: Develop a risk scoring system that evaluates each transaction based on various factors, such as the amount, location, and customer behavior. Transactions with higher risk scores can be flagged for additional verification or manual review. This prioritization helps banks focus resources on transactions with highest potential to be problematic.
• Integration with external databases: Integrate automated workflows with external databases, such as fraud databases and watchlists. This allows the system to cross-reference customer information and transaction details against known fraud patterns or high-risk individuals.
• Regular audits and updates: Establish a process for regularly auditing and updating fraud detection capabilities. As fraud tactics evolve, it’s important to keep automated systems current—and ideally one step ahead. Regular reviews can help identify any gaps in the system and ensure banks are using the most effective tools and techniques for risk mitigation.

Designing automation to handle edge cases and exceptions without human intervention is a critical aspect of creating effective banking workflows. Agentic process automation (APA) is uniquely capable of addressing this need. Edge cases are unusual or unexpected situations that can occur in banking processes, for example, unusual customer requests, irregular transaction patterns, or exceptions in data. To handle these effectively, automation needs to be flexible and intelligent—which are APA’s strengths.

Here’s how APA works for designing automation to handle edge cases and exceptions:

• APA harnesses artificial intelligence (AI) and machine learning (ML) to analyze large volumes of data and learn from historical patterns. Based on its training on diverse scenarios, an APA workflow can recognize when something falls outside the norm and automatically adjust its response. For example, if a customer tries to transfer a large sum of money from an account that typically has very low activity, the system can identify this as an edge case and apply specific rules to manage it.
• Unlike traditional automation, which follows fixed rules, APA uses AI agents that make decisions based on real-time data. When an edge case comes up, these agents can evaluate multiple factors, like customer history, transaction context, and risk levels, to determine the best course of action. This dynamic decision-making allows the system to handle exceptions without needing human input.
• APA is flexible; it can incorporate multiple different paths for handling exceptions. For example, if a transaction is flagged as suspicious, the APA system may automatically initiate a secondary verification process or temporarily pause the transaction until further checks are completed. This ensures that edge cases are managed promptly and securely.
• APA includes feedback loops to learn from its decisions. When it encounters edge cases, it can analyze the outcomes of its actions and adjust algorithms accordingly. Over time, this leads to more accurate handling of exceptions, reducing the need for human intervention.

Yes, banks can implement modern automations even if they still rely on legacy mainframe systems.

Robotic process automation (RPA), intelligent process automation (IPA), and agentic process automation (APA) can all effectively integrate with legacy systems. RPA automates repetitive tasks by mimicking human interactions with applications, allowing it to work directly with the user interface of legacy mainframes. IPA extends RPA by adding AI capabilities while still connecting with legacy systems.

APA takes this further by incorporating agentic AI and machine learning for dynamic decision-making and handling complex workflows. Together, these automation technologies enable banks to automate without needing to modify existing infrastructure.

In addition, modern automation solutions are designed for integration, including with legacy mainframe systems. Using APIs and other integration methods, banks can create automated workflows that includes both legacy and modern systems.

However, while banks can implement automations alongside legacy systems, it’s important to have a long-term strategy for modernization. Relying solely on legacy systems may limit a bank’s ability to adopt technologies in the future and constrain its ability to adapt quickly to changing markets and customer expectations. A long-term modernization plan will help banks accelerate operations and remain competitive in the evolving financial landscape.

While building in-house may seem appealing for its control and customization, buying from a proven automation vendor generally delivers far greater advantages. Here’s why.

Cost and resource efficiency. Designing an in-house platform demands a substantial investment—not just in technology but also in skilled talent, ongoing maintenance, and infrastructure. For most banks, this would strain resources and shift focus away from core operations like customer experience and growth. Vendor platforms, on the other hand, come with predictable, manageable costs, so you can allocate resources more strategically while getting all the benefits of a ready-made solution.

Keeping up with technology. The pace of innovation in financial tech is relentless. Vendors specializing in agentic automation continually push their platforms forward, introducing the latest advancements in AI, machine learning, and data processing. By purchasing from a vendor, you gain immediate access to these updates without the internal burden of staying ahead of the curve. Building your own platform risks falling behind quickly unless your team has the capacity and budget for constant innovation.

Meeting compliance with confidence. Banking regulations are complicated, and they change frequently. Vendor platforms are designed to stay aligned with the latest legal and regulatory standards. If you build your own system, ensuring full compliance is an ongoing, expensive challenge that your team may struggle to keep up with amid other priorities.

Privacy and security you can trust. Data security is non-negotiable in banking. Vendor solutions typically include robust, built-in security frameworks, leveraging years of expertise to safeguard sensitive information. Creating a secure in-house solution demands continuous investment in advanced security measures—a tough and costly responsibility for any institution to take on alone. With a vendor’s proven solution, you can feel more confident in protecting customer data.

Enterprise scale and support. Banks have diverse automation needs, from streamlining back-office processes to bettering customer interactions. A key advantage of vendor solutions is the ability to deliver an enterprise-wide platform that works across all these functions from day one. These solutions are designed to handle complex workflows and serve different departments seamlessly, delivering cohesive automation across banking operations. Leading vendors also provide dedicated support staff to quickly address any issues, keeping disruptions to a minimum and your bank running efficiently.

When does building in-house make sense? If your institution has unique processes that demand ultra-specific customization, or if you possess the resources to fund and sustain constant development, building might align with your goals. However, for most banks, the challenges—rising costs, obsolescence risks, and complex scalability—outweigh the potential benefits.

Choosing a trusted agentic automation vendor will keep you on the cutting edge of innovation, handle compliance and security, and allow you to focus on what really matters—serving customers and growing the business. Building in-house may sound appealing, but buying from a vendor is often the smarter, more efficient path to success.

Banking automation offers a powerful competitive edge by enhancing efficiency, cutting costs, and improving customer satisfaction—all while boosting regulatory compliance and enabling strategic growth and innovation.

A key element here is agentic automation, which allows systems to not only execute tasks but also make informed decisions based on pre-set criteria or real-time data. This capability is particularly valuable for functions like approving loans, detecting fraud, and managing investment portfolios. By enabling quicker and smarter decision-making, agentic automation reduces turnaround times and boosts overall operational efficiency.

Automation in general transforms routine processes like transaction handling, data entry, and reporting. By streamlining these tasks, banks lower operational costs, minimize human error, and enable employees to focus on delivering personalized service and driving strategic growth.

Customer experience also sees remarkable improvements through automation. Tools like AI chatbots provide 24/7 support, while streamlined workflows speed up services like account openings and transaction processing. Agentic automation can go even further and provide tailored solutions for customers, like personalized financial advice or customized product offerings.

On the compliance side, automation excels at simplifying complex regulatory requirements. AI-driven systems monitor transactions in real time, flagging irregularities and ensuring compliance with laws and standards. This proactive approach reduces the risk of fines, legal complications, and reputational damage.

Through automation, banks can elevate operational performance, adapt swiftly to market demands, and secure their position as innovative industry leaders.