Robotic Process Automation in Banking and Finance is one of the most potent and compelling use cases of automation technology. Trading automation has been widespread since the 1970s and 1980s, but RPA is opening up a different type of mechanization with a greater focus on driving down costs and improving consumer experiences.
Banking RPA has also allowed businesses to respond to the ever-changing regulatory landscape by acting as a finance automation RegTech solution. However, there are several other excellent uses of RPA in finance, including transaction processing, loan approvals, and increased cybersecurity.
In this article, we’ll explore the benefits, case studies, use cases, trends, and challenges of Robotic Process Automation in Finance and Banking.
Robotic Process Automation in
Finance and Banking market size
The global Robotic Process Automation (RPA) in banking and finance (BFSI) market size was around $860.75 million in 2023. With a compound annual growth rate (CAGR) of 40%, analysts expect the sector to expand to almost $9 billion by 2030.
North America (45%) and Europe (30%) make up the bulk of the market. However, Asia Pacific is seen as the area with the highest potential for growth over the next decade.
Factors influencing banking and
finance process automation
The banking and finance markets were early adopters of software testing automation tools and RPA technology. In many ways, they were ideal candidates for the technology because these sectors process a high volume of repetitive and rule-based tasks, such as financial transactions. However, adoption has increased for a variety of other reasons. Here are a few of the most important.
1. Reducing costs
For a long time, banks and financial services companies existed in an era of low or even negative interest rates, which made cost savings a priority. Rampant inflation may have changed that in recent years, with many central banks raising interest to around 5%. However, there are other headwinds that financial businesses need to contend with.
The rise of neobanks and innovative FinTech businesses have added serious competition to the financial landscape. When coupled with clear shifts in consumer expectations, financial institutions need to reduce costs to stay competitive. RPA helps teams reduce the day-to-day costs of running services while still providing innovative products for consumers.
2. Increased regulatory and administrative burden
The increase in financial regulatory standards over the last few years posed a big issue for financial businesses. Know Your Customer (KYC) and Anti-Money Laundering (AML) obligations have placed a large administrative burden on financial services companies without adding to their bottom line. Manual compliance is costly, repetitive, and prone to human error.
RPA tools with Optical Character Recognition (OCR) and other AI-assisted tools can take some of this burden away from banks and reduce the costs of staying compliant, such as human capital.
3. Customer self-service
Customer expectations have changed markedly over the last decade. Now, consumers expect things to be done immediately, and they don’t have time for a business that can only help them between 9 and 5. Of course, it’s not just customer service expectations that have grown. Consumers also want quick decisions on loans and account applications.
RPA can help with all of these problems by automating applications against rule-based criteria with minimal need for human interaction and dealing with customer queries.
4. Less risk
Banks and financial companies inevitably deal with a lot of risk. However, mitigating that risk is an important part of a well-run business. Mistakes can lead to a loss of consumer confidence and reputational damage, while compliance errors result in stiff financial penalties.
RPA reduces human error, helps institutions stay compliant, improves data accuracy and processing, and can be used in fraud detection when augmented with Machine Learning (ML).
5. Business continuity
Financial institutions play a critical role in the economy, and any service disruptions can lead to reputational damage. Moreover, because these institutions hold sensitive data, they are bound by regulations that protect consumers and ensure the financial system’s stability.
RPA can form part of a solid business continuity plan (BCP) and ensure that any downtime caused by natural disasters, public health emergencies, cybersecurity attacks, or more is minimized.
Benefits of Robotic Process Automation
in Finance and Banking
Implementing RPA solutions in the financial services sector has many benefits. Here are some of the most important.
#1. Save money
The use of RPA is expected to continue to grow in the financial sector in the coming years. RPA can automate up to 80% of tasks in the financial sector, which represents incredible cost-saving possibilities for organizations.
#2. Increased job satisfaction
The financial sector is full of repetitive and mundane tasks that leave workers feeling uninspired, bored, and undervalued. RPA tools can take over these rule-based jobs and open the door to more engaging and creative tasks that help employees feel more connected to the overall mission of the organization.
Increased job satisfaction equals increased employee retention. RPA should be part of that strategy.
#3. Meet regulatory compliance
The financial services industry has some of the most exacting regulatory requirements for any sector. Failure to comply with these rules can lead to heavy fines, a loss of license, and reputation damage that is hard to bounce back from. RPA helps teams meet these ever-evolving standards.
#4. Scalability
Neobanks and FinTech businesses within the financial services startup space often grow rapidly thanks to alluring incentives. However, this growth can cause problems, like staff shortages. RPA helps overcome these limitations through a digital workforce that can handle increased workloads.
RPA banking use cases
There are many great RPA use cases in banking and finance. Some are directly related to core banking activities, while others help with more administrative or customer-facing tasks.
Here are nine of the best Robotic Process Automation use cases in banking and finance.
#1. Customer onboarding
Customer onboarding is one of the best RPA use cases for the modern banking era. The advent of neobanks and FinTech companies has ushered in a new era of digital banking. Walking into a branch to set up a new account is rapidly falling out of fashion. Instead, modern consumers want to do everything on their app.
Of course, shifting to a remote account opening comes with its own issues. Customers need to upload documents and paperwork and get credit checked. What’s more, their information needs to be uploaded to the bank’s systems.
RPA helps with all these processes, including customer communication, document processing, identity verification, credit checks, data entry, account updating, and more. It’s quick, scalable, cost-effective, and meets consumers’ demand for self-service.
#2. Processing loan applications
Processing loan applications is a great example of RPA in banking. These processes require intense scrutiny of paperwork and customer data to mitigate losses. However, this thoroughness must be offset against speedy decisions to stay competitive.
RPA helps by using Optical Character Recognition (OCR) and Intelligent Document Processing (IDP) to analyze documents, extract data, and compare information against internal documents to approve or reject loans. RPA provides the blend of speed and accuracy that consumers have come to expect from digital banking.
#3. Automated customer support
Continuing on from the trend of customer self-service, banks must find ways to deliver quick, always-on, multi-channel support to their customers. RPA can assist with this process in several different ways. For starters, customer service bots can provide sophisticated and contextual advice to customers. That can be something as simple as links to FAQs or knowledge bases or full-blown Generative AI-assisted conversations.
What’s more, RPA bots can help resolve customer issues by collecting data and documentation, pushing tickets to relevant departments, and providing automated contact to users during the issue. When paired with AI and data analysis, RPA tools can help provide a more personalized kind of service, which helps build trust.
#4. Report generation
RPA for banking helps satisfy financial services needs for report generation. By connecting with various databases and spreadsheets, employees can use RPA tools to extract information in real-time, leading to up-to-date reports that provide high visibility.
The entire report generation life cycle becomes quicker with RPA tools because they assist with automating data collection, aggregating information, generating reports, and distributing the final product to relevant pirates.
RPA-generated reports are quicker, error-free, and cost-effective. What’s more, RPA systems can be implemented with compliance in mind, and if paired with AI tools, they can also help with analysis and decision-making.
#5. Fraud detection
There are several ways that RPA can help financial businesses with fraud detection. RPA tools can collect and aggregate data to facilitate pattern recognition. It can also be used for real-time monitoring, sending alerts, and executing rules based on certain findings or conditions.
The real power of RPA for fraud detection lies in its integration with artificial intelligence and, in particular, machine learning algorithms that can analyze vast amounts of data to detect anomalies. From there, these RPA bots can highlight cases for human review, allowing banks and financial institutions to reduce the risks and losses associated with fraud.
#6. Compliance
Regulatory compliance is such a pressing issue in the banking and financial sectors that a whole arm of technology has sprung up in recent years to address the problem. Dedicated regulation technology (RegTech) tool spending is set to reach $200 billion by 2028. However, RPA can solve many of these issues.
RPA tools for financial regulatory compliance can help with data collection for reports, with audit trails perfect for showing transparency. What’s more, RPA is a great option for data management and anonymization, credentialing, and general cybersecurity.
Overall, meeting regulation requirements is costly and time-consuming. RPA tools allow teams to take the burden off their team by automating repetitive KYC and AML tasks. It’s a match made in heaven.
#7. Payment processing
Just like RPA in accounting, finance services organizations can automate a lot of the work-a-day payments and transfer transactions, ensuring they are completed quickly and error-free. RPA is adept at the automation of high-volume and repetitive tasks, and payment processing most certainly falls within those parameters.
RPA tools can initiate payments, instruct payment processing software, send reconciliation data and even resolve customer disputes. Again, it’s about accuracy, efficiency, and reducing human error. With the right setup, the payments can also help meet compliance standards while allowing expanding financial services business to scale easily.
#8. Automated account closure
No bank or financial institution likes to see a customer go, and a part of that is because of all the extra admin it creates. However, RPA tools can make the process more efficient, cost-effective, and compliant. Banks can use RPA to gather customer information from a variety of sources and schedule account verification by checking balances, documents, and account status.
Closing an account often requires transfers of funds to new destinations and notification of third parties. Again, RPA is well-positioned to automate these tasks. Finally, financial services businesses can also generate the relevant documentation and paperwork and update customer databases to reflect any changes.
#9. Employee management
From automating expense management to employee onboarding and performance reviews, financial services use RPA tools for a wide variety of HR-related tasks. With financial institutions under pressure to streamline services and reduce costs, RPA is an elegant solution for reducing the cost associated with employee management.
RPA helps teams automate payroll, benefits, and manage sick leave, all while meeting required standards and providing employees with a quick, self-service option. The benefits here are an increased employee experience that helps with job satisfaction and loyalty.
RPA in financial services case studies
Of course, hearing about RPA use cases in finance and banking is one thing, but understanding how the technology has been applied in the sector and what tangible benefits it has unlocked for organizations is the most compelling way to measure the impact of RPA.
Case study #1: Eliminating human error
A global financial services company with almost 240,000 employees in over 150 countries had a pressing need to streamline its workflows and reduce human error associated with manual tasks. One issue they had to contend with was the diverse mix of services they offered, including auditing, tax consultation, HR, cybersecurity, and deal management.
However, there were other parameters. The company did not want to overhaul its current IT system or cause too much disruption to business continuity.
The business gathered various stakeholders and IT workers within the organization and created a cross-functional team to gather requirements and identify workflows and business processes that they could automate. They identified repetitive tasks with a high rate of human error and set four KPIs for the project, including speed, data quality, autonomy, and product impact.
Implementation took around three months, and by the end, the team had built an RPA bot that exchanged data across myriad systems three times a day. The project saved 100,000 work hours per year and $800 million while reducing the problems caused by human error.
Case study #2: Accelerating loan processing
A prominent US bank received over 10,000 loan applications per month. Processing these loans took the work of 50 staff members, with the process including reviewing loan applications, gathering and verifying customer data, and ultimately accepting or refusing the loan. However, there was an extra layer of complexity to deal with due to the bank’s reliance on a legacy software system.
After some careful planning, the bank used RPA to automate its entire loan process. The RPA tools read and extracted data from the applications and validated the data against the bank’s loan policies and relevant regulatory framework. From there, the system could decide on the suitability of the loan.
By implementing an RPA solution, the bank greatly improved both the accuracy and speed of their loan processing. Application processing was reduced by 80%, with human error entirely reduced. The increased efficiency reduced human labor by 70% while ensuring the bank complied with regulations.
Case study #3: Meeting the regulatory burden
A multinational bank based in the UK faced regulatory pressure to replace one of its products. They had legacy credit cards, which earned their customers points and rewards. However, the need to switch to a new model, which required 1.4 million customers to select new products, was not something that could be handled manually.
The processes that needed to be automated included sending communication to customers about the changes, processing customer decisions, updating details across company systems, and recording changes to comply with audit requirements. However, there were time and budget restrictions, which added roadblocks to overcome.
The bank introduced a backend SQL database for the CRM system and built a database that could cover all the scenarios that could assist with decision-making. Additionally, they automated the product switching steps, including communication and feedback. Finally, they built an admin portal to handle report retrieval.
The end results included saving £1.2 million per year, saving on hiring 18 full-time members of staff, increasing accuracy to 100%, and meeting regulatory requirements.
Challenges facing Robotic Process
Automation in the Banking and Finance sectors
Implementing automation for banking and finance teams comes with some specific challenges due to the culture and workflows within both sectors.
#1. Legacy infrastructure
The financial sector has a well-earned reputation for sentimentality when it comes to IT technology. In fact, in the early 2020s, over 40% of large US financial institutions were still using software built on Common Business Oriented Language (COBOL), a programming language invented in 1959. What’s more, many businesses still use mainframe computers for data processing.
RPA is an effective tool to help integrate legacy systems with modern cloud-based applications and APIs. It can also be used to migrate data from these outdated systems and reduce the maintenance costs associated with legacy technology.
#2. Process standardization
Depending on the culture, employees, and the high concentration of legacy systems within company architecture, financial institutions will have their own workflows and processes, quite often across different departments. Attempts to implement RPA solutions will require cross-departmental collaboration and process standardization.
In many ways, process standardization is just part of increasing efficiency. If two departments or team members do the same thing in wildly different ways, one of them will be less efficient than the other in terms of time or resource use. Standardizing processes means organizations are positioned to take advantage of RPA solutions.
#3. The Silver Bullet Myth
Deloitte suggests that there is a danger that financial organizations believe that Cognitive RPA will be a “Silver Bullet” that can be “applied on a fundamentally broken process with the expectation that it will fix itself.”
In reality, the implementation of any RPA system needs careful requirement gathering and planning. Consultation with an RPA expert can smooth over many of the problems associated with implementing this technology in an already complex ecosystem.
#4. Regulatory compliance
Financial services are one of the most strictly regulated sectors, with rules relating to handling sensitive data and even risk. As such, any RPA solutions will need to fit inside these restrictions and ensure regulatory compliance.
RPA is a good candidate for these scenarios because there are records for each process, which is vital for financial audits. What’s more, while regulations are constantly changing and being updated, RPA offers the flexibility to adapt to new rules. Finally, automating can help ensure sensitive financial and personal data is not accessible to human eyes, providing an extra layer of security.
#5. Skills shortage
The IT skills shortage has affected the financial services industry over the last few years. As such, implementing RPA solutions is difficult without the experience and expertise of IT specialists.
Successful RPA adoption requires a deep understanding of the technology, including its potential and limitations. ZAPTEST Enterprise users can take advantage of a dedicated ZAP Expert who can work closely with them to understand requirements and help implement RPA solutions based on industry best practices. This addition can help teams overcome the relative shortage of RPA specialists.
RPA in banking industry trends
The financial services industry is moving fast in response to shifting consumer and regulatory demands. Let’s explore some of the trends of RPA in finance and banking.
#1. Intelligent Automation
Intelligent Automation (IA) involves using other types of Artificial Intelligence in conjunction with RPA tools. Some of the technologies involved here include Intelligent Document Processing (IDP) and Machine Learning.
The addition of these tools overcomes RPA’s inherent limitations in dealing with unstructured data and decision-making capabilities. The net result is that the scope of automatable tasks increases, allowing financial institutions to do more.
#2. Cloud-based RPA
While early RPA systems were typically on-prem, the last few years have seen a notable shift towards cloud-based tools. There are lots of benefits to this switch, including secure remote access for distributed teams.
#3. Generative AI
Generative AI is making an impact across a wide range of industries, with the banking and finance industries no different. There are lots of different use cases, including chatbot customer assistants, content creation, and report generation. Banks and financial services may also build their own in-house AIs to deal with regulations around financial and personal data.
#4. Assisted RPA
While Unassisted RPA is still the most popular flavor of automation in use in the business world, Assisted RPA is growing in relevance. These tools will stitch seamlessly within an employee’s workflow. For example, a customer service representative could automate data retrieval or processing tasks on the fly, leading to far greater productivity and, ultimately, happier consumers.
The future of automation in the banking industry
Robotic Process Automation in Finance and Banking is well established. However, it has plenty of room to grow in interesting and innovative ways.
#1. Hyperautomation
Data analytics, artificial intelligence, natural language processing (NLP), and RPA will converge to create banking and financial systems that automate everything possible, from back-end processes to front-end workflows. This futuristic destination is called Hyperautomation.
There are several ways that hyperautomation could go in the banking sector. Beyond robotic process automation in finance and accounting tasks, we could see human-computer collaboration on a higher level, with machine learning and analytics recommending decisions for human approval.
#2. Highly personalized no-code application design
Application design within the banking industry is complex. To a large extent, that has to do with strict laws governing financial and personal data. However, no-code applications will arrive in the space thanks to RPA tools with AI and APIs. Software testing automation will be a big part of ensuring both the integrity and security of this software, which can be tailored around the individual workflow or company culture.
#3. Predictive fraud detection
Fraud detection is a big concern for financial institutions. In the UK, fraud cost banks about £1.2 billion in 2022. Machine learning tools are already in use via RPA in finance and accounting, and they’re adept at detecting fraud. However, in the future, sufficiently well-trained ML algorithms could predict the likelihood of fraud at the time of application or based on a certain set of steps. The cost-saving implications are immense.
Final thoughts
Robotic Process Automation in Banking and Finance is a fast-moving and exciting space. The modernization and increasing technological sophistication in the financial services sector means that Banking RPA is not just a nice-to-have but critical for competing with your rivals.
Unleashing the power of Robotic Process Automation in Finance and Banking improves efficiency and adherence to compliance standards and saves money. As banks become more customer-focused operations, finance automation will help deliver better customer experiences and increased personalization, especially when combined with AI tools. Streamlined operations will pass down savings to users, while innovative new products will meet the demand for apps that help users save, budget, and achieve life goals.
