Table of content:
- How Robotic Process Automation in Finance Works
- Top Reasons Why RPA & Automation and Banking/Finance Work Together Well
- Where Can RPA Be Applied?
- 10 Real Use-Cases of RPA in Banking, Fintech, and Finance
- 3 Financial Companies That Successfully Use RPA
The innovative technology of Robotic Process Automation (RPA) is one of the most powerful tools for optimizing business processes today. The course of all exhausting, routine processes in any business area can be significantly improved thanks to it, and the costs of their maintenance are significantly reduced.
However, is the banking sector one of those where it is possible to use robotization technology? After all, the banking sector is perhaps one of the most vulnerable in terms of risks, given banking specifics. In addition, the rather restrained attitude of banking IT departments to all kinds of technological innovations that could interfere with the smooth operation of banking systems and already integrated corporate applications is well known.
Also, what about security issues? Indeed, in the banking sector, they are relevant, like in no other: it is information security, and the security of saving personal data, the security of operations, etc. The slightest mistake can be very expensive here – both in monetary terms and in matters of reputation bank and clients’ confidence in it.
Due to such a high degree of financial and non-financial risks, doubts may even creep in whether it is advisable to “let robots into the bank” at all. But, despite the quite fair questions and warnings, the technology of robotization of business processes is not only suitable for implementation in the banking sector – it already has successfully established itself in this field and showed the first results that are encouraging for all skeptics.
Such world-famous banks as HSBC, Credit Suisse, Citi, Deutsche Bank, or international groups Raiffeisen Bank International and BNP Paribas are already leveraging RPA and intelligent automation.
Many other banking institutions around the world are already on the verge of robotization and see it as a powerful and truly revolutionary tool for positive changes in the banking business.
What is RPA?
The banking and finance industry has experienced exponential growth over the past few years, thanks to the implementation of technological advances that lead to faster, safer, and more reliable services. To stay competitive in an increasingly crowded market – especially with the increasing adoption of virtual banking – banking firms had to find a way to provide their customers with the best possible user experience.
Almost 81% of banking executives are more concerned about the speed of technological change than any other sector in the industry, according to PwC. Within the company, the challenge has also grown to maximize efficiency and minimize costs while maintaining the highest level of security. To meet these needs, Robotic Process Automation (RPA) has become a powerful and effective tool.
RPA has been widely adopted in this sector to make labor-intensive banking transactions more organized and automated. According to reports, the largest share of revenue in 2019 came from the BFSI segment in terms of RPA application.
Robotic Process Automation has also greatly simplified a wide range of back-office processes that once bored bank workers. By shifting much of these tedious manual tasks from person to machine, banks have been able to significantly reduce the need for human participation, which has had a direct impact on everything from productivity and efficiency to personnel issues and costs.
Recently, Japan’s largest banks announced the introduction of Robotic Process Automation to reduce labor costs and improve operational efficiency. Large banks such as Axis Bank and Deutsche bank also announced the implementation of RPA to automate business processes.
A bank employee deals with voluminous data from customers, and manual processes are prone to error. Banks around the world are considering using RPA to minimize manual processing of this huge data to avoid errors. Handling data manually is also a laborious task. Simple checking of client information from two systems can take seconds instead of minutes with bots. Implementing bots for these manual processes can reduce processing costs by 30% to 70%. Some processes in banks can be automated to free up staff to work on more important tasks.
Over the years, companies have tried to find ways to improve these departments through corporate systems, reporting tools, and interim measures to eliminate repetitive manual actions.
According to Jeremy Dean, the head of RPA at the accounting company Mazars in the USA, this phenomena has a bright future:
“Unfortunately, these methods solved many other problems but did not help in any way to eliminate two main ones that exist in finance and accounting. The first is how to load data into the systems, and the second is how to carry out the “closing of the month.” Many people now realize that automation solves these two problems.”
How Does Robotic Process Automation in Finance Work?
To reduce errors and perform repetitive workloads, RPA also leverages the power of artificial intelligence. The advantage of RPA is that you do not need to automate the entire process, but only certain parts of a multi-step process.
Dennis Gannon, vice president of consulting at Gartner, says rule-based processes are best suited for automation. This is where RPA delivers fast results and acts like digital adhesive tape.
“RPA is good at handling bottlenecks that arise in time-consuming processes that require many hours of manual labor,” Gannon revealed.
He says RPA is likely to gain widespread acceptance as a means of automating the buy-to-pay process. By starting with these transactions, finance teams can capture early results and get feedback on what is working well, and then move on to other tasks that are also easy to automate.
When it comes to fears of being unemployed due to automation, it’s important to involve finance staff on RPA projects to reduce their fears and allow them to discover new perspectives.
To begin with, project managers will involve employees in automation processes for a couple of days a month so that they can practice introducing new robots into the production environment. For the remainder of the time, they will observe how the robot is working and identify any limitations and problems in using it. This will open up other areas for them to automate their work.
RPA consists of software robots, which are patterns of repetitive actions. The robot mimics normal human functions: reading text in an application, copying information, and transferring it to another application. IT departments use RPA platforms to create, monitor, manage, reuse, and save robots and the actions they perform.
The simplest robots capture the mechanical actions of a person and copy them. This is a kind of sample. Based on it, the developer will create a stronger robot that will not break if the application screen is slightly changed. Other artificial intelligence technologies often complement robots. For example, optical character recognition (OCR) is used to transfer text from a paper document, and machine learning is used to match fields in an invoice with fields in an application. This combination is sometimes referred to as intelligent process automation.
Robotic Process Automation in banking also complements a number of other technologies. Sometimes IT departments use low-code / no-code frameworks to create lightweight automation that is implemented as code, API management frameworks, or platform-as-a-service, which enables direct communication that is much faster than RPA. However, RPA has access to any application available to humans, and this is its advantage over other technologies.
Studying the market of implemented RPA solutions in the international IT segments, we can conclude that the use of RPA does not actually depend on the industry, and this technology works in any market segment.
However, this article is devoted primarily to Robotic Process Automation in banking and finance.
Top Reasons Why RPA and Banking & Finance Work Together Well
Here are the most important points. Look through all of them to see the main benefits of using Robotic Process Automation in finance.
Automate Manual and Repetitive Actions and Processes
Experience has shown that robotization works best in those business areas where there are many manual and repetitive actions and processes. From this point of view, the banking sector, in which such processes – carrying out transactions, entering/reconciling data, paying bills, etc., are commonplace – is one of the most suitable for RPA.
And suppose we imagine the scale of these processes in the context of an extensive banking network of regional branches. In that case, the need for their robotization over time will become simply imperative.
Speed and Efficiency
Correctly implemented and correctly controlled robotization will significantly improve the speed of processing banking operations, the efficiency of back-office and front-office processes in the bank, reduce the duration of processing client requests, and, consequently, free up front-office employees the time needed to serve customers better.
Contrary to concerns about reliability, robotization technology allows not only to increase the productivity of business processes significantly but also to minimize the number of purely mechanical errors that a person can make due to the influence of the so-called “human factor” (fatigue, health, etc.).
In addition, the working time of software robots is not limited. They are capable of performing specified operations 24/7, which is very important for a modern bank’s operational activities with the same unlimited 24/7 access to a wide range of banking services (in particular, through Internet banking, payment terminals).
In addition, RPA successfully integrates with corporate applications already available in the bank, without changing the IT landscape, which does not threaten banking systems’ smooth operation. On the contrary, it greatly improves their work.
Although RPA technology is still relatively new on the market, examples of positively implemented business cases on robotics in the banking sector are already making themselves felt.
In those banking institutions where robotization has already been successfully implemented, it has significantly increased business processes’ operational efficiency and productivity. Because reducing manual processes minimizes not only costs but also the likelihood of errors.
Thus, Robotic Process Automation in banking ensures the highest quality and the highest level of accuracy in business processes. Therefore, perhaps there is nothing surprising in the fact that automated solutions are increasingly becoming the subject of close attention from the banking sector since they allow them to bring it to a qualitatively new level of mastering new opportunities for developing the banking business.
Expand the Boundaries of Tasks
Robotization, enhanced by the capabilities of machine learning and artificial intelligence technologies, will also significantly expand the boundaries of tasks performed by software robots, making it possible for them to process unstructured and semi-structured data sets.
Changes in the Existing Banking Business Model
Undoubtedly, it is also worth remembering that Robotic Process Automation in finance will lead to significant changes in the existing banking business model. This does not literally mean that hundreds and thousands of employees will instantly lose their jobs with its implementation.
Although we do not completely exclude the possibility that the least qualified among them, capable of performing only mechanical work, can lose it – it will be difficult for them to compete with robots in speed and efficiency.
However, along with the implementation of RPA, one should be ready to redistribute the workload and powers between the employees who were previously engaged in routine processes within the bank itself, since robotization will free up their working time, which would be more expedient to be directed to solving more creative tasks and better work with clients.
Therefore, here we are also dealing with an indirect effect of robotization, which will consist of an unobtrusive inducement of employees to improve their qualifications. The human employee will also have to learn how to effectively interact with software robots to ensure banking business processes’ efficiency and continuity.
Where can RPA Be Applied?
How and what is automated in finance? According to a poll by The Economist Intelligence Unit, more than half of all financial transactions last year were carried out with robots’ participation. Analysts at McKinsey say that in the financial sector, automation can, on average, free up 43% of employee time. This figure may vary depending on the specific specialization. For example, a mortgage broker spends 90% of the time processing applications in manual mode, but if it uses more modern methods of determining the authenticity of documents or applications for a loan, then 60%. As a result, they can devote the saved time to communicate with clients – to consult more and delve deeper into their needs.
Companies trust robots to solve specific problems in large processes. They can:
- get access to applications;
- make decisions based on the if / then rule;
- navigate through applications;
- write and send emails;
- read structured data from documents;
- open files and attachments;
- copy and paste information.
As practice shows, a wide variety of banking functions and processes are successfully amenable to robotization:
- servicing accounts and payments,
- audit reporting,
- mortgage payments,
- reconciliation of account balances,
Here are examples of the most common tasks that robots take on in the financial sector.
- Collection and processing of data. Finance and insurance workers in the United States spend half of their time on these processes. According to McKinsey, RPA solutions are capable of handling 60% of these tasks. Robots successfully process completed customer forms according to the specified parameters, extract and check data from invoices, confirm payments, send error notifications, monitor duplicate payments, etc. The robot copes with data entry faster than a human. This saves companies the time and money needed to rework the task. The program works around the clock without a break for lunch and sleep, does not get tired and does not make mistakes, and provides reports simultaneously. Thus, customers of banks and insurance companies are less likely to face situations such as delays in payments.
- Confirmation of data accuracy. Employees of many financial institutions spend a lot of time on tasks such as determining a credit score. Robots with access to databases are able to verify the authenticity of the information provided by clients by order of magnitude faster. They easily track money transactions and identify suspicious activity. In the long term, this means that a person with a clean credit history will get access to necessary services faster.
- Prepare material for analysts. Organizing data for analysts takes a huge amount of time, and this task can also be safely given to a robot. The specialist will work with a ready-made table. Its task is reduced to familiarization with the document and making decisions.
- Customer support. Most executives across industries, including finance, believe that customer support automation is essential to a company’s competitiveness. This implies both a more convenient search and storage of user information and the implementation of RPA solutions integrated with chatbots. So, bots are happy to communicate with any client, regardless of the time of day. They are ready to answer basic questions, leaving complex counseling to the people.
With such a wide range of potentially robotic processes, one has only to remember about choosing the “correct” ones among them, the effect of which will be the best.
The decision on this should be made on an individualized basis – taking into account the specific banking infrastructure and current business processes’ efficiency.
Let alone RPA in the banking industry, the robot can do the following:
- opening/closing applications and systems Web clients, Email (including attachments);
- navigation within applications (ERP, CRM, etc.);
- creating and moving files and folders;
- following links and emulating button presses;
- automatic switching between applications;
- filling out forms, copying;
- loading data from external sources into the program interface and directly into the database;
- comparison and validation of data, mathematical calculations;
- work on complex logic with conditions and cycles;
- OCR (PDF / DOC / XLS);
- multilevel automated verification of data entered by other employees;
- messengers – communication using intelligent chatbots;
- using predictive analytics to drive statistics-based robot decisions and data mining.
10 Real Use-Cases of RPA in Banking, Fintech, and Finance
Below are ten examples of RPA implementation in banking, accounting and finance.
1. Automation Control
RPA implementation is difficult to scale. One of the challenges is to allow finance departments to create new robots easily, but the process needs to be limited.
“To address this issue, Hewlett Packard Enterprise (HPE) created a centralized bot infrastructure,” said Sandeep Singh, head of financial control and quality assurance and RPA CoE at a multinational IT company based in San Jose, California.
His team has built a bot platform based on WorkFusion, which simplifies the process of deploying automation, building robots, and tracking and reporting bugs. Employees created an internal governance framework so that all stakeholders can access audit, business compliance, IT, and finance data.
Singh says: “This is convenient for both the executives and external auditors as it allows you to keep a close eye on what is happening. We are constantly working to ensure that no information passes by us.”
2. Revising Accounts
Reconciliation of accounts between systems is a necessary but extremely boring exercise.
As per Singh, HPE has benefited most from using RPA, as it improved logging and subsequent invoice reconciliation. These processes are compliance-driven, time-consuming, and involve excellent operations.
For example, different departments within HPE have different process templates and review procedures. Some of these processes may include functions within the ERP system. Others must meet both the audit and compliance requirements for the authenticity of transactions and the business requirements for approval procedures and thresholds.
Singh’s team was challenged to find the right balance between specific business requirements and rigorous audit and compliance rules, resulting in an optimal solution that can be automated using RPA.
“While business requirements are not as strict and negotiable, and it is possible to improvise here, accounting and compensation must be handled very delicately,” Singh says.
3. Processing of Accounts
Invoice processing is another notable area for automation.
HPE accountants process significant volumes of paper invoices every month. They are also responsible for registering them for subsequent payment processing. Problems arise here due to the different formats of invoices, the quality of the scanned documents, and other languages’ use. The company has combined OCR technology and WorkFusion machine learning modules to smooth out format and image quality issues.
Optical Character Recognition (OCR) technology helps convert images to a sequential text format, and then machine learning uses historical rules to determine how information from an invoice is entered into a financial application. HPE also uses automation to feed extracted data into SAP Ariba’s procurement management system.
4. Processing of Orders
Depending on the source of the order, the document format itself is also different.
Jeremy Dean believes that automation speeds up order processing and eliminates human error.
The principle of operation here is similar to processing invoices: OCR technology reads data from paper, and machine learning transfers it to the system.
For example, Dean was working on a project for a brewery where they wanted to automate the creation of orders in SAP. Their robot received input data in two different formats, checked the entered data’s completeness, then set up a shopping cart in SAP, and sent a request for order confirmation.
5. Correction of Inaccuracies
Another great point of RPA in finance examples. As soon as erroneous information enters the enterprise data ecosystem, it immediately spreads across all systems and data warehouses. Subsequent cleaning and adjustments will require considerable effort.
Dean has already worked on several projects. The bottom line is that automation scans the data in order to quickly and efficiently make corrections and identify errors, then send them to employees for review. Having found the correct data, the robot programmatically changes all other information in the system.
For example, Dean was working on a logistics company project where RPA helped identify inaccuracies between the ERP system and reporting tools. To assess the degree of a discrepancy, the robot used different rules to determine whether the problems that have arisen are related to the original data, or they have already appeared in ready-made reports.
The final decision is flagged for employee review and approval.
The robot will make changes to the system as soon as the employee approves them. This approach improves the quality of data across all company systems.
6. Work with Cash
Cash depositing is an important function during the processing of receivables. For example, many errors arise when money goes to another account or is posted on the wrong invoices and invoices.
HPE Finance receives a huge stream of payments from customers in more than 50 countries. It all starts with bank statements, which are provided in the required format. Then they are posted to the appropriate accounts for a particular department or group. Robotic Process Automation in finance helps automate the reading of information from statements and entering data into specific fields in a receivables program.
Singh says the different formats, languages, and the lack of specific information on the statements became a problem for HPE, as it made it difficult for analysts on accounts receivable. The solution was implementing RPA in finance and its development, which uses fuzzy logic to improve data recognition, and machine learning to avoid repeating previous mistakes. As a result, the deposit process’s accuracy has increased, and the processing time has been significantly reduced.
7. Make Sure the Contractual Terms Are Compliant
In compliance, it is especially important that suppliers adhere to previously agreed terms. And this is another great argument for the bright future of RPA in finance. For example, it helps HPE contract employees automate many of the operations associated with fulfilling contractual obligations.
Singh defines this work as extremely cumbersome and boring, which is also done by hand.
Using natural language processing technology, the robot scans all contracts and purchase orders and extracts the necessary information on discounts, recalculations, and penalties. The robot then compares this data with data from the company’s ERP system to identify gaps and inaccuracies.
8. Report of Profits and Losses
Manish Chawla, associate director of business performance at international consulting firm Protiviti, says many companies automate the generation of profit and loss statements, especially those that need to submit such reports to management on a daily basis.
As with the previous examples, Robotic Process Automation in banking dramatically reduces the number of repetitive, time-consuming, and manual tasks so that finance staff can focus on generating profit and loss statements, which is their top priority. Many companies significantly reduce the time for processing reports and provide early access to them, but at the same time with much more accurate data.
9. Data Management in Multiple Systems
Oftentimes, finance staff members need to manage data that resides in different systems. Automation improves data management in these systems and allows you to take into account the rules for their movement.
Jeremy Dean says that IT departments can set up financial robots to respond to certain events in the systems, or so that they can be launched at a fixed time when it is necessary to complete the process.
Dean was involved in developing a solution for a banking and insurance company. The goal was to improve the process of managing master data and maintaining financial accounts.
For example, his team has automated three back-office activities related to the seizure of enforcement of clients’ assets. This simplified the launch of an operation, during which access to various databases is opened, and amounts are frozen on different accounts and two different banking systems with minimal involvement of employees. In the meantime, they can focus on more complex tasks such as analyzing and introducing new data.
10. Overall Efficiency
In 2017, the Polish Eurobank turned to the consulting company PricewaterhouseCoopers for their assistance in implementing RPA. At that time, the financial institution served 1.4 million private accounts and had a network of 500 branches. It took several months to check the viability of the project and develop several test robots.
The experience turned out to be successful, and the bank created its own RPA competence center, which began studying business processes and preparing them for robotization.
UiPath Orchestrator from one of the world leaders in RPA, UiPath, was chosen as the centralized robot control platform. The selection from the list provided by PwC was based on test results in which the UiPath Orchestrator demonstrated the best performance in terms of cost-effectiveness, simplicity, and ease of use.
The results of the project exceeded expectations. The robots were able to take over about 20% of the bank’s back-office tasks. This 20% includes almost all compliance-related tasks. The bank noted an improvement in customer service quality, and the resulting robotics savings in working time exceeded 20 FTE.
3 Financial Companies That Successfully Use RPA
Financial companies planning to automate their processes can learn from the following examples. These companies have pioneered Robotic Process Automation innovations beyond simple operational efficiencies.
Kryon Systems – Claim Payment Processing
Insurance is one of the main beneficiaries of Robotic Process Automation. Kryon Systems, a provider of RPA solutions, helped a global insurance company reduce payment processing times.
RPA has proven to be a game-changer in claims technology. Their client, a global insurance company, had to go through 26 different banking websites and use a smart search to check if payments were made. This happens on 4 different days each month and takes 4 business days each month.
Once RPA was implemented, it took only 2 hours to complete. In the past, employees made mistakes and searches were wrong, but robots provided a mechanism that increased accuracy. As a result, thousands of working hours are saved every year, and human error is eliminated.
Bancolombia – Investment Portfolio Management
Bancolombia, the largest bank in Colombia, has implemented RPA solutions to help people better manage their investment portfolio.
Their RPA tool, Invesbot, is available to anyone with investments over $7,000. It provides real-time market information and portfolio performance and offers advice on making changes in line with current market conditions.
Another innovation is a robot advisor who advises Colombian investors on stock marketing and helps them experiment with it. This allowed Bancolombia to teach how to invest in the stock market digitally.
“The idea is that the client does not have a higher cost for advisory services. We see this more as a highway for growth rather than something that allows us to cut back on financial advisors.”
– Juan Felipe Giraldo, President of Valores Bancolombia
The results are clear: Bancolombia has won the Digital Transformation Award at Colombia’s Best Biggest Company from PwC and CINTEL.
Keybank – Accounts Receivable System
Keybank is the leading commercial bank in the United States to partner with Billtrust and implement RPA technology to improve its efficiency.
Bank automated invoice delivery and receivables processing with RPA solution powered by Billtrust. It includes the creation of electronic invoices with Quantum Payment Cycle Management Billtrust. Keybank has optimized accounts receivable through an end-to-end implementation of RPA.
“Banks have traditionally focused on payment execution. We think that’s important. But in consulting with our clients, we hear a lot of pain upstream and downstream of payment processing. Partnering with innovators like Billtrust enables us to optimize clients’ processes from beginning to end.”
– Matt Miller, Head of KeyBank product and innovation for commercial enterprise payments
The bank has automated repetitive manual tasks. They hope that the entire implementation of machine learning will improve processes over time. It expands its RPA capabilities for better digital transformation.
For now, most industry leaders have no doubt that RPA will increase their companies’ competitiveness over the next two years. The reason for this will be, among other things, an increase in the level of service and greater customer satisfaction.
So, let’s say a positive decision on robotization in the bank has been made. It will be equally important to correctly build further steps for its implementation and determine what basic rules should be followed in order to make a banking RPA project successful.
Feel that the help of specialists would be useful? Don’t hesitate and contact Flobotics for RPA consultation and development.