Robotic Process Automation (RPA) is emerging as a game-changer in 2023, especially in the banking and financial sector. With an estimated global market size of $26 billion by 2027, RPA is not just a trend but a strategic necessity for financial institutions. Nearly 80% of finance leaders have already adopted or are planning to adopt RPA, recognizing its potential to revolutionize banking processes.
That’s why understanding what RPA entails for the banking industry and how to implement it effectively is essential to unlock its full benefits.
So, without further ado, let’s dive right into it!
What Is Robotic Process Automation (RPA) in Banking?
In the banking industry, robotic process automation refers to the use of sophisticated business process automation technologies to automate various monotonous, repetitive, and time-consuming processes. They can include data entry, account opening, mortgage loans, customer support duties, etc.
With basic rule-driven robotic process automation, its capabilities are restricted since it only executes mundane tasks based on the rules without any variation. It may, for instance, log into an account, transfer some files, and log out.
To level up robotic process automation, banks and financial institutions also use intelligent automation, which incorporates artificial intelligence technologies like machine learning and natural language processing. This makes it possible for RPA software to manage intricate operations, comprehend human language, detect emotions, and adjust to real-time data.
Benefits of Implementing RPA in Banking
Consider robotic process automation a digital workforce, working ceaselessly behind the scenes to execute critical tasks that might otherwise take hours. With RPA, your bank can reap multiple benefits, including:
- Save time
RPA bots are undeniably faster than humans in completing jobs, enabling banks to finish daily activities in less time. According to Gartner, Inc., the adoption of RPA in financial reporting may spare finance departments’ staff 25,000 hours of needless rework brought on by human errors.
- Reduce expenses
RPA deployment enables banks to reduce operational costs, which amounts to a 30% cost reduction by decreasing human participation in different procedures.
- Minimize involvement from the IT department
Bank employees can configure RPA bots independently after completing thorough training sessions. This significantly lessens the workload for the IT staff.
- No extra IT infrastructure
As a “minimally invasive” solution that effectively leverages existing infrastructure, RPA can utilize the natural user interfaces of existing legacy systems to carry out its automated operations. Thus, no need to worry about new infrastructure deployment.
- Facilitate compliance reporting
The activities of recorded RPA bots create an audit trail, which greatly facilitates compliance reporting. For example, through Natural Language Processing (NLP), bank employees may utilize RPA technologies to collect information and evaluate various transactions against defined validation standards.
If RPA bots detect questionable transactions, they may immediately flag them and alert compliance professionals. This proactive and automated surveillance can assist an organization of the financial services sector in avoiding financial losses and legal issues.
- Boost human employee efficiency
Thanks to RPA bots doing repetitive and manual processes, human employees have more time to work on value-added tasks.
- Minimize human error
In contrast to humans, RPA bots finish critical tasks, especially manual processes, with the same precision regardless of their difficulty level, leading to minimal errors.
Top 10 RPA Use Cases in Banking and Financial Sector
RPA has a wide range of applications in banking operations. Let’s take a look at a few notable RPA use cases.
1. Customer Service
The customer service team handles multiple queries from clients, from account openings to mortgage loan requests. Such rule-based processes can be automated with RPA to provide responses instantly, minimize turnaround time to seconds, and thus enhance customer satisfaction and experience.
Especially by adopting artificial intelligence, RPA can tackle questions requiring decision-making skills. Chatbot Automation also uses NLP to help bots comprehend the natural language used in customer chats and reply accordingly.
Working in one of the most strictly regulated sectors in the world, banks are required to generate multiple reports for the compliance process, from post-trade compliance checks to periodic expected credit loss (ECL) computations. Additionally, approximately 15% of compliance officers’ time is spent monitoring changes to regulatory requirements.
RPA bots can collect information automatically from numerous sources, such as governmental agencies, official websites, financial statements, news sources, etc., and enter it into a bank’s internal system while adhering to data structure requirements. This could significantly save operational costs and speed up the process.
3. Accounts Payable
Accounts payable management takes a lot of time. It requires workers to digitize vendor invoices, check the data in each field, and then process the invoice.
By automatically reading the invoices and crediting the payments after correcting errors and verifying data, intelligent automation, including optical character recognition (OCR), helps streamline these repetitive tasks.
4. Credit Card Processing
Banks can now swiftly process credit card applications and distribute cards thanks to RPA. RPA software can quickly evaluate whether a client is qualified for a card by scanning through credit card applications, customer documents, customer histories, etc.
5. Mortgage Processing
Both clients and banks must put in a lot of work when processing a mortgage loan. The Mortgage Reports claims it can take banks up to 60 days to close a mortgage deal. Loan officers must go through numerous procedures, including employment verification, credit checks, and other forms of inspections.
With robotic process automation, banks can significantly reduce the time it takes to process mortgage loans by 80%, which will be a huge relief for financial institutions and customers.
6. Fraud Detection
It is now hard for banks to manually verify every transaction and detect fraudulent patterns.
Rather than depending on human judgment and manual data manipulation, banks can employ RPA technologies to track client transactions consistently, identify irregularities based on an intricate rule-based system, flag them as possibly fraudulent, and issue warnings to staff members for additional assessment.
RPA enhances fraud detection by restricting accounts and halting transactions as well.
7. KYC Process
KYC may take up to 1,000 full-time equivalent (FTE) hours and $60 million per year to complete.
Due to the high expense of the traditional method to carry out the KYC process, banks have begun employing financial process automation for verifying customer data. Thanks to this, banks no longer have to be concerned about FTEs, and the procedure can be performed with minimum mistakes and manpower.
8. General Ledger
Account reconciliation is an excellent candidate for RPA-enabled change due to its repetitive nature. Overseeing and updating financial statements, assets, liabilities, and spending across multiple legacy systems is labor-intensive and prone to errors, especially for mid-sized and big institutions.
Banks may delegate most of these activities to RPA, allowing bots to autonomously gather data from numerous systems, confirm payments, validate loans, and balance general ledger accounts.
9. Report Automation
Compliance laws require banks and financial institutions to create and deliver reports documenting their performance and difficulties to the board of directors. These papers include a large quantity of data, making it a time-consuming and error-prone work for humans.
However, robotics in finance and banking can effectively gather data from many sources, format it, and provide error-free reports. To be specific, RPA in banking could automate a wide range of reporting processes, including monthly closes, reconciliations, and management reports.
10. Account Closure Process
Account closure requests require a series of sequential and frequently predictable steps to complete, such as sending emails about client papers, reviewing a bank’s records, and changing data in the internal system.
With RPA, your banks can automate these rule-based operations, freeing staff to focus on more profitable and intellectually demanding jobs.
How to Implement RPA in Banking
For successful RPA implementation, you should follow the 13 important steps below.
Step 1: Find repetitive and manual tasks that can be automated
First and foremost, select financial procedures that are appropriate for RPA in banking. Data entry, report automation, credit card processing, and mortgage processing are notable examples of such repetitive tasks.
Step 2: Establish specific goals and objectives for RPA implementation
Determine precisely what results you expect from RPA implementation, such as error reduction, efficiency increase, compliance boost, or faster turnaround time. These objectives should be in line with more significant strategic objectives in your financial organization.
To prevent disappointments brought on by mismatched expectations, be careful to set attainable and reasonable objectives for ROI (return on investment) and banks spend reductions.
Step 3: Choose a trustworthy RPA tool
Compare different RPA systems based on technical know-how, market experience, functionality, scalability, integration competencies, vendor support, and simplicity of use attributes.
Be mindful of differences between partners and vendors. For example, UiPath, the top RPA platform on the market right now, offers the ability to put this technology into practice. Meanwhile, Itransition, a UiPath silver partner, aids organizations in implementing it.
So, pick RPA tools that best satisfy the needs and budget of your company.
Step 4: Evaluate the process’ viability and set priorities
It’s time to check each identified process to see whether it can be automated sustainably. Take into consideration factors like transaction volumes, system compatibility, rule-based nature, and procedure stability.
Consider procedures with large volumes, repetitive tasks, and little exception handling processing a higher priority.
Step 5: Create thorough workflows and process maps
Use a process flow diagram to outline the whole process flow, including the input data sources, intended outputs, decision points, and system interactions.
Clearly specify all steps the RPA software robot needs to go through to perform the task, such as data extraction, manipulation, and verification.
Step 6: Create and test automation scripts using the selected RPA tool
Create automation scripts using the chosen RPA tool and the workflows specified in the previous step. To make sure the scripts function as planned and handle errors properly, extensively test them in a controlled environment.
Step 7: Get the required approvals from key stakeholders
To guarantee that you follow organizational policies and laws, obtain authorization from relevant stakeholders, from compliance to IT and management.
Resolve issues or risks related to operational effect, regulatory compliance, or data security.
Step 8: Put scripts into action and carefully examine how they perform
Launch the automation scripts in the real-world setting while keeping an eye on their efficiency.
You should continuously check on the automated procedures to ensure they function properly and deal with any problems or exceptions.
Step 9: Train employees how to use and collaborate with RPA
Employees who engage with automated procedures should receive training. You must assist them in comprehending the changes made possible via RPA and offer advice on how to work productively with software robots.
Step 10: Gradually expand automation to more processes
After using RPA effectively in a few procedures, progressively scale it to automate other banking and financial tasks.
After that, continue to evaluate and discover additional procedures that may incorporate automation.
Step 11: Constantly improve automated processes
Gather performance data and feedback from clients to find the areas that need improvement. It’s possible for you to improve the efficiency of the automated workflows by fixing any problems or limitations.
Step 12: Seek opportunities where you could employ cognitive technology
To further improve automation capabilities, consider intelligent automation, which integrates cognitive technologies such as machine learning or natural language processing. These technologies may facilitate sophisticated data analysis and intelligent decision-making inside automated operations.
Step 13: Align RPA projects with business objectives
Make sure that your organization’s RPA activities align with its overarching strategic objectives. For the most significant impact of RPA in banking, you need to assess it oftentimes and realign it.
It’s worth noticing that RPA implementation is a continuous process. Your banks should periodically examine how the RPA deployment has changed operations following established key performance indicators and success metrics.
RPA Case Studies in the Banking and Financial Industry
The following two notable case studies from the US banking sector show how those major banks have effectively applied RPA in practice.
#1 BNY Mellon
BNY Mellon has assets of $371.8 billion and more than 30 employees with RPA experience. It began utilizing robotic process automation in banking in 2016 and had more than 220 robots by 2017.
Bots carry out usual, automated tasks including settling every customer service banks deal, resolving disputes, and speeding trade settlements by researching orders. Because bots are also leveraged for data reconciliation, the team can thus focus on limitations and quality control.
BNY Mellon employs robotic process automation software based on the well-established and proven Microsoft.NET Framework. The system also supports every platform (mainframe, windows, WPF, Java, web, etc.) in a number of formats (terminal emulator, thick client, thin client, web browser, Citrix, and web services).
- Account-closure validations were completed with 100% accuracy across five systems.
- Processing time was reduced by 88%.
- Improvement of 66 percent in trade entry turnaround time.
- A robot can reconcile a bad deal in a quarter of a second, compared to 5-10 minutes by a person.
- The bank believes that its investment in bots alone is saving it $300,000 per year by minimizing the time its personnel must spend finding out and fixing data errors and speeding up payment processing.
#2 SunTrust Bank
SunTrust Bank has a total asset base of $205.96 billion and 21 professionals with RPA expertise.
According to its 2016 reports, the bank formed a team inside its IT department to assist with the implementation of banking RPA technology. SunTrust has utilized RPA in payment-operations sectors such as consumer bank cards, wires, and ACH.
The bank disclosed that robotics enhanced transaction speed by 3.8 times, cut training time by four times, and lowered the error rate by a stunning 65 percent.
SunTrust intends to use RPA technology in a variety of additional sectors, including wholesale lending, mortgages, treasury development, and imaging services. The bank has finished 19 projects and has seven more in the works.
Ready to Implement RPA for Your Banking System Now?
Implementing RPA in banking will remain indispensable for those committed to thriving in a fiercely competitive banking industry.
Our detailed guide above offers a comprehensive execution strategy for successful RPA implementation for banks and financial institutions. From identifying tasks for automation to evaluating RPA tools, training employees, and continuous optimization, each step is pivotal. Your banks must adopt a strategic approach, align RPA initiatives with business objectives, and monitor progress through key performance indicators.
So, are you ready to embrace RPA, adapt, and embark on a journey toward a more efficient and customer-centric future in the banking sector? Contact Neurond AI now to start transforming your banks!
In case you still want to dive deeper into RPA, check out our guide to RPA in procurement for more information.