Robotic Process Automation (RPA) in banking: Improve and accelerate your operational management
During the COVID-19 pandemic, the adoption of regulatory technology solutions accelerated, particularly the use of robotics in banking. According to the AML Banking Survey conducted by RiskScreen, 70 percent of respondents claim that the pandemic has pushed digital transformation in the banking sector. However, the user-friendly interfaces of modern mobile banking apps often mask banking processes that are still full of manual operations and reliance on paper.
Although a team of experienced developers can code an RPA banking solution to automate manual processes, some bank employees still use Excel, Access, SQL-based query tools, and Google for manually searching for adverse media. Operations that are often performed manually by bank staff include onboarding clients to collect important customer data and conducting regulatory, legal, and credit checks with identity verification. Data merging, transformation, enrichment, and reconciliation are also frequently done manually.
Relying on manual processes instead of automating financial processes hinders banks’ ability to generate profit while exposing them to unnecessary risks caused by human error. This issue can be resolved if banks implement user-controlled data governance and automation. Robotic process automation (RPA) in the financial industry is the way forward.
Robotic Process Automation (RPA) for Banks: Solutions for Optimizing Operational Management
Robotic Process Automation (RPA), often referred to as intelligent or cognitive automation, refers to advanced software programmed to perform a sequence of operations typically carried out by humans. Key tasks and activities based on rules that specialists consider repetitive and mundane are ideal for RPA.
More advanced financial services automation systems are equipped with machine learning (ML), artificial intelligence (AI), and cognitive computing capabilities. For example, consider the combination of RPA with AI. RPA handles structured data, while AI is used to gather information from semi-structured and unstructured data in text, scanned documents, web pages, and PDF files. By doing this, AI adds value by processing the data and converting it into a structured format that RPA tools can understand.
Nevertheless, RPA technology is still valuable on its own and brings many benefits to financial companies.
Advantages of RPA technology in banking
According to Gartner, an RPA bot typically costs only one-third of the salary of an offshore specialist or one-fifth of the salary of an offshore expert. On top of that, the bot works accurately and without failure — if properly programmed. With such capabilities, RPA has attracted the attention of organizations across many industries, including banking. Along with the obvious benefits of robotic process automation in retail banking, such as reducing human error and fewer repetitive tasks, RPA provides the following advantages:
- Quick implementation: Since many RPA solutions come with drag-and-drop capabilities for automating banking processes, it is easy to implement and maintain automation workflows with virtually no programming required.
- Zero infrastructure cost: RPA does not require significant changes to the infrastructure due to its user interface automation capabilities. Hardware and maintenance costs are further reduced in the case of cloud-based RPA.
- Compatibility with legacy data: By implementing RPA, financial institutions can leverage both legacy and new data to bridge gaps between processes. Integrating critical data into a single system helps organizations create better reports faster, supporting business development.
- Limited integration budget: Applying RPA in the financial sector allows your organization to invest in a single platform instead of ensuring compatibility across all software solutions.
- Enhanced security: A large volume of data is both a blessing and a curse. RPA reduces the curse by completely eliminating manual processing by humans, thus avoiding tasks that were previously prone to errors or even the exposure of sensitive payment and customer data.
- Having reviewed the benefits of robotic process automation (RPA) in banking processes, let’s take a look at how some financial institutions are already benefiting from RPA technology.
RPA for Banks: Real Examples of Successful Implementation
There are many successful examples of robotic process automation (RPA) in banking. Check how the implementation of RPA in financial services has allowed businesses to transform their operations in a short period.
Payroll Protection Program (PPP) Loan Processing
One of the notable examples of successful RPA implementation in recent years was the processing of PPP loans. Public banks were required to upload a huge volume of documents and numerous PPP loan applications to the Small Business Association (SBA) loan system. Many participating financial institutions used RPA technology to replicate the data entry process typically performed by humans, eliminating manual data entry.
For example, Queensborough National Bank & Trust Company, located in Louisville, Georgia, achieved impressive results using RPA technology to process PPP loans in 2020. As a result, the company was able to issue 1,780 loans totaling $150 million. The following year, the bank processed approximately 1,000 additional loans. These remarkable results placed the bank in fifth place among its competitors headquartered in Georgia for the number of PPP loans issued.
Commercial loan agreement processing
JP Morgan Chase is another example of successful RPA implementation. Like many companies that entrust tasks to RPA, Chase had to find a solution to perform higher-quality work in less time.
In 2017, the bank launched a program called COIN (Contract Intelligence). This program involves the use of digital workers to automate administrative tasks. COIN uses machine learning to interpret, analyze, and extract information from commercial loan agreements. As a result of its implementation, a process that previously took credit specialists and lawyers 360,000 hours per year is now completed in just a few seconds.
Volume of banking operations
ICICI Bank adopted RPA for 10 operations, with the number increasing to 200 within the first year to support numerous sectors such as retail, corporate law, treasury, agribusiness, trade, and Forex. To date, ICICI Bank has automated 1,350 processes with 750 bots performing over two million transactions daily.
As seen from the examples above, RPA technology can automate various manual processes. Let’s define the key use cases for this technology.
Some banking processes that can be automated with RPA.
RPA boasts a wide range of use cases in the Banking, Financial Services, and Insurance (BFSI) sector, allowing employees to focus on more complex tasks. Below are some examples of robotic process automation in banking.
Use cases of RPA in banking.
Report generation
To comply with regulations, laws, and guidelines, financial institutions must prepare reports on their activities to inform the board of directors. These reports often contain human errors and take a lot of time to create, as they are based on vast amounts of data.
RPA is excellent at gathering information from multiple sources, presenting it in a consistent format, and generating reliable reports. RPA helps automate a wide range of reports, such as reconciliations, closings, and management reports.
Accounts Payable
Accounts payable is a simple yet repetitive task that requires obtaining supplier information and approval for payment processing. RPA, enhanced with Optical Character Recognition (OCR) technology, helps automate the accounts payable process.
OCR extracts supplier information from a digital copy of a physical form and feeds this data into the RPA system. The RPA system then verifies the data against existing information in the system and processes the payment. In case of an error, the RPA system notifies the designated person for resolution.
Customer Onboarding
The client registration process conducted by financial institutions is complex, primarily due to the manual authentication of multiple identity documents. Know Your Customer (KYC), a key part of onboarding, requires significant operational effort to verify documents.
According to a Thomson Reuters report, the cost of KYC compliance and proper customer verification can range from $52 million to nearly $384 million per year. An AML study by RiskScreen shows that eight out of ten compliance professionals find their work too labor-intensive.
To reduce costs and automate the customer onboarding process, you can create a software system with RPA, computer vision, and OCR technologies to extract crucial data and verify the customer’s identity.
Credit Card Processing
Without proper automation, banks took weeks to approve customer credit card applications. Long waiting times led to customer frustration. Now, thanks to RPA technology, banks can quickly approve and issue credit cards.
Within hours, RPA allows gathering customer documents, performing credit checks, and determining whether the customer qualifies for a credit card based on established criteria.
Mortgage Processing
According to Mortgage Reports, mortgage settlement in a financial institution can take from one to two months. Mortgage specialists must take several steps to verify the applicant’s employment, check their credit score, and perform other types of checks. During this process, even a minor mistake by a bank employee or applicant can significantly slow down the process. RPA can reduce processing time by 80%, as automation of the mortgage process can speed up tasks from creation to post-closing.
These are just a few examples of where RPA can be applied in banking and finance. Banks should consider implementing RPA across all their functional areas to improve the customer experience and gain a competitive edge. While implementing RPA may seem like a costly investment, considering the technology’s value to the business, it can provide a good return on investment within a few months after implementation.
When choosing an RPA implementation approach, you will face a decision between off-the-shelf solutions and developing custom RPA software.
Overview of Off-the-Shelf RPA Solutions
There are many off-the-shelf RPA solutions. Let’s review a few popular ones among BFSI industry players.
Aiwozo: This intelligent process automation platform combines RPA capabilities with artificial intelligence and machine learning for high-level automation. While the platform can be applied to large-scale automation projects, its flexibility allows for rapid deployment. Organizations of any size can benefit from its broad set of functionalities. Using Aiwozo’s tools, BFSI institutions can automate processes related to consumer lending, retail banking, and asset management.
Kofax. This business process automation platform helps clients register, display, and analyze applications, including desktop, internal, and external. For the BFSI sector, Kofax’s RPA and accounts payable features enable organizations to automate invoice data collection, verify invoices, process payments, and integrate with ERP systems. Kofax is ideal for small and medium-sized businesses.
UiPath. This platform allows any designated employee in your organization to create and use robots. Experienced programmers can use the rich development environment, while business users with little or no coding experience can automate daily tasks with StudioX. BFSI organizations can use the platform’s RPA and artificial intelligence technologies to enhance productivity and processes with various tools for intelligent analysis and process automation. UiPath is an excellent solution for small and medium-sized projects.
How to Choose the Right Software for Banking Automation
To select the most suitable RPA tool, you need to consider your organization’s goals and requirements. Additionally, we provide some recommendations for choosing the right RPA solution.
Ease of Use: The RPA software solution should offer easy-to-understand features, clear and informative graphics, smooth navigation between separate modules, intuitive text, and sufficient flexibility for easy automation of core processes. For example, users of Aiwozo praise the platform for its user-friendliness and claim that the tool requires almost no coding knowledge. Therefore, non-technical users can quickly learn and adapt to the technology.
Bot Setup Simplicity: There should be several approaches to setting up bots for different types of users. Non-technical users should be able to configure bots via a graphical user interface, while those with some programming knowledge should be able to do so using a low-code environment. For example, Aiwozo Studio includes pre-built actions and allows for quick bot development through drag-and-drop functionality.
Security: RPA bots handle sensitive data by transferring it between systems. If not properly secured, this data can be exposed, potentially costing your organization thousands or millions of dollars. Therefore, it is essential to check the security features of your solution. For example, UiPath encrypts data during both transmission and at rest. All information stored in UiPath’s cloud products and services is encrypted during transmission over public networks, protecting the data from unauthorized disclosure or alteration.
Key Criteria for Choosing RPA Tools
When selecting an RPA tool, consider factors such as ease of implementation, compatibility with existing legacy systems, and machine learning capabilities for easy extraction of information from unstructured data.
Build or Buy: How to Get the Most Out of RPA
Before deciding whether to opt for custom RPA software development or purchase an off-the-shelf product, answer the following questions:
Which processes need automation?
The primary goal here is to determine whether the ready-made RPA software can meet your automation needs. Assess all the processes you want to automate, considering your business requirements and the needs of all departments. A practical approach is to create a list of operational issues your company faces. Processes that are repetitive or rule-based are excellent candidates for automation.
As a result, you may conclude that your business processes are too specific for off-the-shelf solutions. In such a case, creating a custom solution will help you save money and implement RPA efficiently.
Are there RPA solutions tailored to the tasks I want to automate?
Off-the-shelf RPA solutions are created with specific workflows in mind. For example, you can find many bots designed to collect emails and input information into systems. However, to determine the ideal solution, you need to consider all the variables and systems involved in the process requiring automation. The more complex the workflow, the greater the need for custom RPA software.
What will be the total cost of ownership and development?
While developing custom RPA software may require higher initial costs, it leads to lower ongoing expenses and eliminates further licensing fees. On the other hand, off-the-shelf intelligent automation solutions require lower initial investment. However, in the long run, you will have to pay more if you need additional functionality, and you will be required to pay an annual licensing fee. Additionally, if you need to scale, the cost of licenses will also scale.
The main advantage of developing custom RPA software is that you only pay for the features you need. While off-the-shelf RPA products are designed to solve common problems for mid-sized organizations, a custom solution is designed to meet your unique needs. Many off-the-shelf solutions provide general functionality, and most do not allow you to significantly customize their features.
As a result of this preliminary analysis, you may conclude that developing custom RPA software is the best option for your organization. If so, the next challenge will be to find the right software provider.
