Banks were among the pioneers of robotic process automation. Since the early 2010s, they have been deploying RPA bots that mimic human behavior to automate clerical, rule-based processes, such as data entry or invoice processing, helping reduce excessive workloads, minimize human errors, and lower operating costs.
Today, the banking sector remains at the forefront of digital transformation, with AI technology driving the next wave of change. One of the key trends is complementing traditional RPA bots with AI capabilities, which helps shift from simple automation toward intelligent systems.
82% of respondents to the KPMG’s 2025 Intelligent Banking report state they are currently using RPA together with AI to optimize workflows. And this shift towards more intelligent automation is not surprising. After all, AI-enabled bots can process both structured and unstructured data, make human-like decisions, and perform data-intensive tasks, which enables banks to automate a broader range of processes and leverage more impactful efficiency gains. Customer onboarding, transactional accounting, and fintech software development are examples of RPA use cases in banking where AI can be applied.
In this article, experts from Itransition, a company with extensive experience in RPA development, provide three real-world use cases to highlight the transformative effect of intelligent automation on banking.
Onboarding new banking customers is a highly time-consuming and labor-intensive process, primarily because employees have to manually perform tasks such as customer identity verification and sanction screening. The adoption of AI-enabled bots, which can automate the collection of customer data from multiple sources and formats and verify it automatically, enables banks to eliminate bottlenecks in this process and provide customers with fast and frictionless onboarding experiences. Habib Bank Limited (HBL), the largest Pakistani commercial bank, provides an illustrative example.
To streamline onboarding for its rapidly growing customer base, the bank aimed to accelerate manual customer screening processes. The combination of RPA and AI proved highly effective: bots in the bank’s compliance department now automatically extract data from new customers’ documents and verify it against global and national sanctions lists, as well as databases from credit rating agencies and electronic credit bureaus. Currently, bots perform 80,000 screening checks per month with 95% accuracy, helping enhance process execution speed and improving employee morale who can now focus on more high-value tasks.
Accurate identification and tracking of financial transactions enable banks to detect system and procedural errors, prevent fraud, and ensure regulatory compliance, all of which are critical for seamless banking operations. AI bots can automatically monitor transactions across multiple banking systems, capture structured and unstructured financial data from transaction documents, and compare it with internal accounting records to identify discrepancies and fraud. A good example of the impact of intelligent automation comes from Siam Commercial Bank (SCB), a Thai bank operating for over a century.
SCB operates more than 10,000 ATMs across Thailand, and correcting transaction errors across such an extensive ATM network was challenging. Whenever an error occurred, employees had to manually reconcile ATM records with internal financial data, a process that took a week and left customers dissatisfied while they waited for refunds. The bank solved this problem by implementing AI bots that automatically compare records from ATM errors with internal financial records, and once an error is confirmed, initiate a refund. Now, bots, which handle 24,000 ATM errors per year, help customers receive refunds in less than 10 minutes.
Although the banking sector has adopted RPA technology long ago, many in-house IT teams still largely rely on manual software engineering, testing, and deployment processes. This leads to inefficiencies and bottlenecks in the SDLC and hinders banks’ ability to deliver new features and services to customers quickly. The adoption of AI-driven bots enables banks to automate virtually any stage of the SDLC, enhancing software delivery speed without compromising its quality. The example of State Street, an American financial services and banking company, illustrates the tangible benefits of intelligent automation.
Previously, State Street’s in-house IT team spent a large portion of their work time on manual app testing, which prevented them from focusing on revenue-generation tasks. To address the issue, the company decided to automate inefficient processes with the help of AI-enabled bots that can generate new test cases, execute them, and even analyze testing results against the bank’s requirements without human involvement. During the PoC stage, State Street accelerated test execution time by 67%. Now, the bank plans to scale up the use of bots to thousands of tests annually, further improving software time-to-market and reducing the IT team’s workload.
Banks are increasingly integrating RPA technology with AI capabilities to optimize high-volume processes, data-intensive tasks, and other activities that traditional RPA bots were not able to automate due to their technical limitations. Customer onboarding, software development, and transactional accounting are just a few areas in banking undergoing active digital transformation.
To streamline specific business functions with RPA and AI, it is highly recommended to engage experts proficient in both technologies to ensure smoother intelligent automation. Third-party experts can help you layer AI on top of the existing RPA workflows, either by integrating RPA bots with suitable AI services or by embedding AI functionalities directly into bots. If needed, they can also help you build a new intelligent automation solution, train employees to use it, and provide post-launch software support.
Roman Davydov is a Technology Observer at Itransition with more than five years of experience in the IT industry. Davydov monitors and analyzes the latest technology trends, helping businesses make informed software decisions that align with their strategic goals.
