Robotic process automation (RPA) in the banking industry: the expert guide
According to a 2020 PwC report, 81% of banking executives are overwhelmed by the current technological change that requires constant restructuring of business processes. One of the innovations that are impacting the banking industry is enterprise automation forming the core of many digital transformation strategies today.
Robot Process Automation is a type of enterprise automation extensively used by banks and financial services organizations today. It holds a lot of potential in the banking industry for many reasons.
In this article, we delve into the topic to show you the potential of Robotic Process Automation in commercial banking and other financial institutions, discuss its most important benefits and use cases in banking, and outline the key challenges banks face when implementing RPA.
What is Robotic Process Automation?
Robotic Process Automation (also called RPA) is a type of business process automation technology based on software robots, also known as bots or digital workers. The bots are configured to integrate with human activities and execute business processes directly in the user interface.
Many people have already interacted with RPA without being aware of it. For example, when visiting a website, we often get a message from the company in a pop-up chat window. These messages are preprogrammed and sent by special robots that are designed to answer the most common inquiries and questions. No human employee has to intervene at all.
This allows brands to answer customer questions quickly and let their human employees focus on mission-critical tasks and serve their customers more efficiently. This is the essence of the potential of Robotic Process Automation in retail banking.
But how can banks and financial institutions benefit from implementing RPA? Here's the answer.
Benefits of RPA for the finance industry
Cost savings
Robotic Process Automation provides much more value to the overall organization's efficiency. Learning from different implementation stories, we can also see that robotics in banking reduce costs.
For example, Bank of America deployed 22 robots across the front, middle, and back offices to improve customer service, secure the bank, and increase the efficiency and speed-to-market of its products. The bots generate impressive savings: $100,000 per code request and $350,000 per code change.
Banks are always on the lookout to cut their costs in such a hyper-competitive industry where they face traditional banks and fintech startups. Research shows that by implementing RPA, banks stand to achieve from 25% to 60% cost savings and improve the output metrics of many applied functions.
Operational efficiency
Banks play a critical role in every economy. If they become more efficient, this will have a ripple effect on many other industries that use their services. Robotic process automation is an excellent solution here. By implementing RPA, banks make their processes faster and more efficient.
While it might incur some upfront investment, including employee training, technology, and governance, in the long run, it brings greater operational efficiency and cost reductions.
Faster process execution and greater operational efficiency resulting from dramatic reductions in the process execution time explains the popularity of RPA in trade finance.
Increased agility
The growing technology penetration in every industry and globalization forces banks to become more agile and flexible in responding to market changes and customer demands. Thanks to RPA, banks can respond to them faster and more accurately.
That's because RPA also frees up the human resources from the everyday mundane tasks. Employees have more time and energy to focus on developing innovative strategies for growing business.
Today, banks use different technologies to digitize data from paper entries to make it available for analytics. RPA bridges the gap between a legacy system and brand-new data - by keeping all the data in a single system, you can quickly create reports that will inform more accurate business strategies.
Unlocking the value of the existing infrastructure
Implementing robotics for banking doesn't require a brand-new infrastructure. In fact, banks can deploy robots successfully using their legacy systems. One of the unique values of RPA is that it can be integrated with any system.
It doesn't matter what kind of development technology is used at your bank. RPA is applicable across the entire enterprise. Banks are using RPA across a broad range of departments, starting from operations and sales to finance and human resources.
Use cases of RPA in banking
1. Customer service and experience
Financial institutions deal with a massive number of customer inquiries every day. They range from simple account inquiries to loan inquiries and bank fraud. Answering all of these questions can become a huge burden on the customer service team - especially if it wants to keep a short turnaround time.
By implementing RPA, banks can ensure that the bot answers low-priority inquiries, and human teams focus only on the high-priority inquiries that require human assistance.
Moreover, RPA reduces the time required for customer verification, mapping customers with details from different sources, and customer onboarding. This reduces the waiting time and increases the efficiency of redressal to help banks improve their customer relationships.
2. Risk and compliance reporting
RPA has a huge potential for compliance. Today, banking sector needs to comply with several different rules at once, and Robotic Process Automation can help to do that. In order to achieve compliance, banks need to access several applications to get the required data for reporting.
RPA can do seamlessly, and its risk management capabilities increase when it goes through multiple email systems, broker statements, and external websites. For example, a broker statement can be easily automated to generate a detailed report that allows identifying anomalies. With RPA, banks can automate up to 90% of compliance-related tasks, saving a lot of time and money for their teams.
3. Credit card processing
In the past, credit card validation and approval used to take weeks. The long waiting periods resulted in customer dissatisfaction, often leading to canceling the request.
Thanks to RPA, banks can now significantly speed up the validation, approval, and dispatching of credit cards. With a bot in place, the process takes just a few hours to complete - from gathering documents from the customer through performing background checks to making a decision based on the parameters to either disprove or approve the credit card requests.
4. Know Your Customer (KYC) processes
KYC is a data-intensive and rule-based process in every bank. That's what makes it such a great use case for RPA. Due to the huge costs associated with the process, banks are looking to test the technology and give RPA a spin.
RPA can aggregate customer data, evaluate it, and validate it to accelerate the process and eliminate errors. The end-to-end digitization of Know Your Customer processes is the goal of many banks today.
5. Mortgage processing
Another use case for RPA in the banking sector is mortgage processing. This banking process used to take a lot of time and requires the applicants to pass several scrutiny checks before getting approved. In the United States, it takes around 52-53 days to process a mortgage loan. A small error made by the customer or the bank could slow this process down even more and add a lot of complications. By implementing RPA, banks can streamline and accelerate the process, addressing any bottlenecks if it gets interrupted and deliver a fantastic customer experience.
6. Anti-money laundering (AML) and fraud detection
Cybersecurity is another area that can benefit a lot from automation - and RPA is definitely up to the job. Many banks across the world are now automating manual processes for inspecting suspicious transactions flagged by AML systems.
Fraudulent transactions are one of the biggest headaches for banks, and monitoring every single transaction manually to identify fraud patterns is increasingly challenging.
RPA is used to identify potential fraud and raise the flag. For example, if a customer makes multiple transactions in a short period of time, a robot can identify a potential threat and highlight the case for further investigation by a human agent. Implementing RPA saves a lot of time for human agents, allowing them to focus on more important and complex tasks.
Steps to implementing RPA in banking
Step 1: Carry out a detailed assessment
Your analysis needs to be carried out to identify banking processes that might be suitable for RPA. What helps here is a list of operational issues that are good candidates for automation because they are repetitive or rule-based. Naturally, you also need to consider the costs of change and the potential benefits. Pick the cases that offer the greatest potential and don't end up incurring too many costs.
Step 2: Find a strong use case
Document the cost and efficiency gains delivered by RPA. This document will become your proof that demonstrates the potential of RPA to other stakeholders. Otherwise, how can you know whether scaling out RPA across the organization makes sense?
Step 3: Develop an execution strategy
Formulate an execution strategy that is well-aligned with your goals. Identify key stakeholders who will be responsible for managing this strategy and oversee the implementation of RPA in a team or department of your organization.
Step 4: Choose the right partner
The company that helps you implement RPA can make or break your project. Make sure that your partner can provide you professional implementation services starting from idea definition and requirements gathering, through planning and execution, to support and maintenance.
Top 4 challenges in implementing RPA
Resistance to change
According to a recent study, 45% of the leading financial organizations consider resistance to adoption as one of the top challenges blocking them from introducing RPA. This shows that despite their technological maturity, the adoption of RPA is a significant business challenge.
Change management and mindset shift are two critical goals that need to be addressed before embracing any technology - and RPA is no exception. It's the core of enterprise-wide automation strategies. But in order for this strategy to be properly executed, change management is critical. This is a part of a holistic approach to building acceptance of this technology.
Process standardization
Organizational misalignment and process standardization were included by Deloitte in the Robotics and Cognitive Automation report among the top challenges to implementing RPA in banking.
The root of the problem is the traditional separation of IT and business departments handling different operations. To integrate RPA solutions successfully, it's essential to come up with a new distribution of those responsibilities and create an alignment between the teams.
This comes with another challenge related to unstructured data and non-standardized processes that require human input. When deciding which banking processes can be automated, it might turn out that the same process can be understood and executed differently depending on who you ask.
Legacy infrastructure
Another common obstacle to RPA adoption is the generally slow pace of technological development among enterprises in the banking industry. While it's one of the most data-driven sectors, it's still lagging behind in digital transformation.
The majority of banking platforms and core systems running today were developed a long time ago. For example, 43% of banks in the United States use COBOL, a programming language invented in the 1950s. This isn’t compatible with modern technologies.
However, replacing a legacy system is a massive and expensive undertaking. It also poses a number of risks that banks aren't ready to tackle. By postponing the inevitable, banks are only making it riskier and more expensive.
That's why the best digital transformation strategies are holistic and overarching processes that take into account the limits in the value of legacy infrastructure.
Lack of regulation
Finally, the lack of legal regulations to govern automation is a significant problem in RPA adoption. The industry involves many different legal requirements and constraints for process automation.
Even though it's been around for a few years now, RPA is still relatively young in terms of regulation and remains to be addressed by central banks, governments, and other parties.
For example, Article 22 of the GDPR states that data subjects have the right not to be subjected to a decision that was based on automated processing. However, the criteria for this are ambiguous. It's hard to tell whether assessing a person's credit rating using algorithmic card scoring really fits the standard laid out in the article.
However, there is still hope. As RPA technology matures and becomes a must-have for more and more banks, the regulation complexity is bound to become easier via investments made in digital transformation.
Conclusion
Robotic Process Automation is one of the strongest trends in the digital transformation of the banking industry. In just a few years, we're going to see more and more robots performing the most common back-office tasks and interacting with customers. Implementing RPA is in the interest of all banks that want to streamline their processes, become faster in responding to customer queries, and reduce their operating costs.
At Maxima Consulting, we have been supporting the financial services industry for many years and have helped multiple banks launch their automation projects.
If you're looking for an experienced vendor that knows how to build a successful digital transformation initiative with automation at its core, get in touch with us.
Although this article focuses on robotics in banking, it is also worth mentioning the popularity growth Robotic Process Automation in pharmaceutical industry currently undergoes. Browse our tailored IT solutions for Pharma and Healthcare companies to learn more.
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