AI EDUCATION: What Is Robotic Process Automation?

Each week we find a new topic for our readers to learn about in our AI Education column.

"Talk amongst yourselves, I'll give you a topic: Rhode Island is neither a road nor an island." - Linda Richman, Coffee Talk, Saturday Night Live. 

We love our robotic humanoids here at AI & Finance, so we looked forward to the day when we would write an AI Education column about robotic process automation (RPA) and actually spend some time talking about cool robots of the kind that our AI image generator illustrates for us. 

Well, folks, we hate to break it to everyone, but as it turns out, robotic process automation is in a long history of terms like Greenland and French fries. It doesn't really involve physical robots. The "robot" in RPA is more of a metaphor. It's not like a Linda Richman line, though—RPA does at least indeed involve automating processes, and it happens to be directly related to artificial intelligence. It's often referred to as software robotics, "bots," because the "R" in RPA actually refers to robot-like computer programs that work as digital machines without the need for a physical platform. 

RPA uses bots to imitate human interactions with computers and execute repetitive, rules-based business activities. Rather than manufacturing products, transporting materials or performing physical labor, RPA bots operate within digital environments, entering information into databases, navigating applications, processing transactions, generating reports and transferring data between systems. 

What Is RPA? 

Long before generative AI and autonomous agents became central to conversations about the future of financial services, businesses were deploying invisible digital workers to perform repetitive administrative tasks that previously consumed substantial amounts of human labor. This is RPA. 

RPA is especially useful when employees repeatedly perform the same sequence of actions across multiple software applications. Consider a financial services employee who receives a spreadsheet containing customer information, opens a customer relationship management (CRM) platform, searches for individual accounts, updates records and then sends confirmation emails. An RPA bot can perform those steps automatically, potentially completing hours of administrative work in minutes. 

According to IBM, RPA combines application programming interfaces (APIs) and user-interface interactions to execute activities across enterprise applications. Unlike conventional software integrations, RPA can operate through the same interfaces employees already use, making it particularly useful for organizations with aging technology infrastructure or applications that cannot easily communicate with one another. 

The technology has become important in banking, insurance, accounting, asset management and wealth management, where employees regularly navigate complicated combinations of legacy systems, proprietary platforms, spreadsheets, regulatory databases and customer-facing applications. 

RPA is not necessarily a replacement for those systems. Instead, it often serves as a technological bridge between them, allowing institutions to automate existing workflows without immediately undertaking expensive infrastructure modernization projects. 

However, that convenience introduces an important distinction: automating an inefficient process does not necessarily make the underlying process efficient. A bot can execute a cumbersome workflow faster than an employee, but it cannot automatically determine whether the workflow should exist in the first place. 

How Does RPA Work? 

Traditional RPA works by converting a sequence of human actions into executable software instructions. Those instructions specify which applications to open, which information to retrieve, which fields to populate, which conditions to evaluate and which actions to take when particular events occur. 

For example, a bot responsible for processing expense reimbursements might monitor an email inbox for submissions, download attachments, extract information from standardized forms, compare the information against company policies, enter approved expenses into accounting software and notify employees when processing is complete. 

The bot follows predetermined instructions. If an expense exceeds an established threshold, the workflow might require managerial approval. If information is missing, the bot might flag the submission for human review. If all conditions are satisfied, the software proceeds automatically. 

RPA tools commonly interact with enterprise systems through graphical user interfaces, using simulated mouse clicks, keyboard entries and screen navigation. More sophisticated implementations combine these interactions with APIs, databases and other integration technologies. 

Developers can construct automations using conventional programming, but many RPA platforms emphasize low-code or no-code development. Employees can sometimes record their actions, assemble visual workflow diagrams or configure automation steps through drag-and-drop interfaces. 

An enterprise implementation generally includes a development environment, software bots, scheduling capabilities, centralized orchestration, access controls, monitoring tools and exception-handling mechanisms. Bots may execute on employee workstations, virtual machines or centrally managed computing infrastructure. 

Platforms offered by UiPath, Automation Anywhere, SS&C Blue Prism, IBM and Microsoft provide variations of these capabilities. Microsoft's Power Automate also connects desktop automation with broader cloud-based workflows. 

The distinction between a bot and its orchestration platform is important. A single bot might perform one narrowly defined activity, while an orchestration system coordinates numerous bots, assigns workloads, manages credentials, monitors failures and ensures that automated activities occur in the correct sequence. 

Three Types of RPA 

RPA generally falls into three categories: attended, unattended and hybrid automation. 

Attended RPA operates alongside human employees. A customer service representative, for example, might activate a bot during a telephone conversation to retrieve account information from several databases, populate a service request and generate a summary. The employee remains responsible for the interaction while the bot performs supporting administrative work. 

Unattended RPA operates independently, generally according to a schedule or predefined system trigger. A financial institution might deploy unattended bots overnight to reconcile transactions, update customer records, produce regulatory reports or transfer information between accounting systems. 

Hybrid RPA combines these approaches. A bot might independently collect and validate information, pause when human approval is necessary and resume processing after authorization is received. 

IBM identifies attended and unattended automation as the two primary operating models, with their distinction resting on how bots are triggered and whether human participation is required. Hybrid implementations combine their characteristics within larger workflows. 

RPA and AI 

Despite the terminology sometimes used to market automation products, traditional RPA is not intelligent in the same sense as contemporary artificial intelligence. A conventional RPA bot does not understand why it performs a task. It does not independently discover better business practices, develop novel strategies or exercise humanlike judgment. Its behavior is determined by instructions supplied during development. If a bot is programmed to transfer customer information from one application to another, it can perform that operation repeatedly and accurately. However, if the source application's interface changes unexpectedly, the bot may fail because the buttons, fields or navigation paths it expects are no longer available. 

The relationship between RPA and AI has become increasingly important as businesses experiment with generative AI and autonomous agents. Traditional RPA executes predefined actions. Conventional AI can classify information, identify anomalies, recognize documents or make predictions. Generative AI can produce language, summarize documents, write software and respond to natural language instructions. Agentic AI extends these capabilities by enabling systems to pursue objectives through sequences of decisions and actions, potentially using external tools, retrieving information and adjusting plans as circumstances change. 

Consider a financial institution processing a customer's request to transfer an investment account. A traditional RPA bot might retrieve account information, populate transfer forms and submit transactions according to established instructions. An AI-enhanced automation system could interpret an unstructured customer email, identify the requested transfer, extract account details and determine which documents are missing. An agentic AI system might coordinate a broader workflow, selecting appropriate tools, requesting missing information, checking requirements and deciding which steps to initiate next. The distinction concerns the degree of autonomy and flexibility. 

The arrival of increasingly capable AI agents has raised questions about whether conventional RPA will eventually become obsolete. There are reasons to believe AI will replace some traditional RPA applications. AI-enabled systems may become better at navigating changing interfaces, interpreting unfamiliar documents and completing workflows without requiring developers to specify every individual action. 

Some newer AI tools can already interact with computer interfaces through visual recognition and natural language instructions, potentially reducing the need to maintain brittle, screen-specific automation scripts. However, replacing every deterministic automation with an AI agent would not necessarily be economically or operationally sensible. A company that needs to transfer 50,000 standardized records between two applications has little reason to introduce an expensive, probabilistic reasoning system if a conventional bot or API integration can accomplish the task accurately and reliably. 

RPA and AI Complement Each Other 

The strongest argument for RPA's continued relevance is that AI and traditional automation solve different problems. AI can interpret information that would otherwise require human attention, while RPA can translate those interpretations into standardized actions. 

Consider a bank processing mortgage applications. An AI document-processing system might extract information from tax returns, pay stubs and bank statements. A machine learning model might identify inconsistencies or potential fraud. An RPA bot could then enter verified information into the bank's loan origination system, update the application status and generate notifications. Human underwriters would retain responsibility for decisions requiring professional judgment or regulatory accountability. 

In this arrangement, AI provides interpretation, RPA provides execution and humans provide oversight. The same architecture can support insurance claims, investment operations, customer onboarding, regulatory compliance and financial reporting. 

The combination is commonly described as intelligent automation. When organizations coordinate AI, RPA, process mining, workflow management and other technologies across extensive business activities, the broader approach is sometimes called hyperautomation. 

Benefits of RPA 

RPA offers several important advantages, beginning with productivity. Software bots can perform repetitive tasks continuously, often completing work much faster than employees who must divide their attention among competing responsibilities. Unlike human workers, bots do not require breaks, vacations or sleep. Properly configured unattended automations can operate around the clock, allowing organizations to process transactions and update records outside normal business hours. 

Accuracy is another benefit. Humans can make mistakes when repeatedly copying information, entering numbers or transferring records between applications. Bots can reduce these errors by executing standardized instructions consistently, although they can also reproduce programming mistakes at considerable scale. 

RPA can improve regulatory compliance by ensuring that prescribed procedures are followed and creating records of automated activities. Properly configured systems can generate timestamps, execution logs and exception reports, supporting internal audits and regulatory examinations. 

The technology can also improve customer service by reducing processing delays. Faster account opening, payment processing and document verification can translate into shorter waiting periods and fewer administrative obstacles. For employers, RPA may reduce operating expenses and increase processing capacity without requiring proportional increases in staffing. 

RPA Applied 

Financial services is particularly well suited to RPA because its institutions process enormous quantities of information while operating under strict regulatory, security and documentation requirements. Banks, insurers, broker-dealers, asset managers and financial advisory firms depend on numerous interconnected systems, many of which were developed at different times and were never designed to communicate seamlessly. RPA can help bridge those technological gaps. 

In retail banking, bots can automate portions of account opening, customer information updates, transaction reconciliation, payment processing and loan administration. They can retrieve information from multiple applications and populate standardized forms, reducing the amount of manual data entry required from employees. 

Know-your-customer (KYC) and anti-money-laundering (AML) processes represent additional opportunities. RPA can collect customer identification documents, query approved databases, compile information for compliance reviews and generate alerts when predefined conditions are met. 

However, automating information collection is different from automating compliance judgment. A bot may gather evidence, but trained professionals or appropriately governed analytical systems must still determine how to interpret suspicious activity and whether additional investigation is warranted. 

Mortgage lenders can use RPA to organize application documents, transfer information between origination and servicing platforms, monitor application status and generate routine communications. 

Insurance companies can automate policy administration, premium reconciliation, claims documentation and payment processing. AI-enhanced RPA can also assist with extracting information from medical records, accident reports and other unstructured materials, subject to privacy and regulatory safeguards. 

Capital markets operations provide another extensive set of applications. Investment banks and broker-dealers can use bots to reconcile trade records, verify settlement information, process corporate actions, generate confirmations and prepare regulatory submissions. 

In these environments, automation can be especially valuable because operational mistakes may create financial losses, settlement failures or compliance problems. 

RPA Implemented 

Successful RPA implementation begins with process selection rather than software purchasing. Organizations should identify repetitive activities that consume substantial employee time, involve predictable inputs and outputs, and follow reasonably stable business rules. Process discovery and process mining tools can help identify opportunities by analyzing how work actually moves through an organization. 

Before automation begins, management should examine whether the process is necessary, whether it can be simplified and whether existing systems already offer native automation or API-based integration. Automating an unnecessarily complicated workflow can preserve inefficiencies that should have been eliminated. 

Once an appropriate process has been identified, developers document its steps, business rules, inputs, outputs, exceptions and security requirements. They then construct the automation using an RPA development platform, test it against realistic scenarios and establish procedures for handling unexpected conditions. 

A pilot implementation should measure processing speed, error rates, employee time savings, exception frequency and operational reliability. Successful pilots can subsequently expand into larger deployments, but scaling requires centralized management, monitoring and governance. Many enterprises establish an automation center of excellence responsible for technical standards, reusable components, risk management, employee training and the prioritization of automation opportunities. 

The U.S. government's RPA Program Playbook describes this type of operating model, emphasizing organizational standards, governance, risk management and controls as important foundations for enterprise automation. 

Financial institutions should also establish ownership for each automated process. Someone must remain accountable when a bot fails, produces inaccurate information or performs an unauthorized action. Credentials should be managed securely, bot permissions should follow least-privilege principles, and sensitive information should be protected throughout execution and logging. Automations should be tested after application updates, changes in business rules or modifications to underlying infrastructure. Finally, organizations should establish procedures for retiring bots when their functions are no longer necessary.