The Role of Robotic Process Automation in Shaping Financial Services

Introduction to RPA in Finance and Banking

Before we start with Robotic Process Automation for financial services, it’s essential to understand the current landscape. The financial industry is constantly evolving, facing immense pressure to reduce costs while delivering superior services to customers and maintaining its competitive advantage.

Today’s customers expect easy access to services, personalized experiences, and value for money. Financial institutions must meet these demands while managing costs, and this is where robotic process automation plays a pivotal role. RPA involves deploying bots to mimic day-to-day routine tasks based on predefined business rules, making it easy to automate various processes. As a result, robotic process automation services have become increasingly popular among financial institutions looking to streamline operations and enhance customer experiences.

Artificial intelligence is helping banks become more efficient in the process of detecting fraud and Robotic Process Automation. Source, Applications of AI in Banking and its Benefits

RPA aims to automate basic tasks like

• Filling out forms

• Extracting and merging data

• Formatting data

• Copying and pasting data

• Reading and writing database

Robotic Process Automation (RPA) is helping financial institutions provide 24/7 support for critical activities and processes. By automating routine tasks, institutions can ensure real-time support for operations while utilizing staff more strategically for higher-value activities. 

What is Robotic Process Automation in Finance and Banking?

Robotic Process Automation (RPA) in banking, often referred to as accounting process automation, involves using RPA tools such as UiPath, Blue Prism, and Automation Anywhere to reduce the human effort required for processing financial transactions and accounting tasks. These RPA tools help automate the movement of data between accounting systems and external applications, minimizing manual intervention and improving efficiency.

Instead of thinking of accounting robots as replacements for human workers, RPA is more akin to a "bionic arm" that enhances the work of finance and accounting professionals. By automating routine data transfer tasks, RPA bots allow workers to focus on higher-value activities, speeding up processes and reducing errors in financial operations. This results in faster, more accurate financial reporting and transaction processing.

Forecasting plans and developing data-driven decision is a must to improve the business, and reports are the best way to showcase the stats. Source, Management Reporting vs Financial Reporting

Overview of Robotic Process Automation in Financial Services

Robotic Process Automation (RPA) has become a cornerstone for banks, financial institutions, and insurance companies, which are some of the most prominent adopters of automation in their operations. As these organizations face increasing pressure to provide better customer experiences and stay competitive with virtual banking solutions, they have turned to RPA services to streamline their processes.

Financial institutions are under immense pressure to boost efficiency and optimize resources, and RPA tools have proven to be an essential solution. The two main activities in financial services where RPA is implemented are:

• Deploying end-user device software bots to automate routine tasks.

• Building an artificial intelligence (AI) workforce to handle more complex, data-driven processes.

In the finance industry, it serves as an essential tool to address the demands of the sector and increase their efficiency by reducing their costs with the services-through-software model. To seize the opportunity to rise in their industry, they should follow a strategic approach.

RPA in financial services focuses on routine administrative work, such as copying data from email to the system. Financial Services functions at the presentation layer, such as scraping data associated with multiple, simple tasks that occupy a large part of the day.

Many US banks and insurance companies have already implemented its processes, automating about 800 operations. The exponential growth of it can be predicted by the fact that the finance industry will be worth $2.9 billion by 2023, which is a massive increase from $250 million in 2016, as per a recent report.

Challenges

The industries have to face many challenges. Some of them are:

1. Intervention of Automation and AI: The integration of artificial intelligence (AI) and automation in accounting processes is transforming the industry. Key tasks like receipt collection and converting bills into financial statements have been automated, saving significant time. However, this also presents challenges for accounting professionals, as automation may reduce the need for manual labor in these areas, putting jobs at risk.

2. Need for Online Accounting Services: The accounting industry has been thrown ahead of the task of offering virtual accounting services due to existing social distancing and lockdown norms. Accounting firms must now meet with customers virtually and delegate work to staff members who work from home. Traditional accounting firms that haven't kept up with the times and digitized their operations feel the brunt of online accounting services' wrath.

3. Competition: Inside the financial services sector, there is still a lot of competition. Consumers, as previously said, want more personalized service and easier-to-use digital systems. Institutions that offer any of these programs would have a significant market share. Consumers are less concerned with brand loyalty and identity these days. They care for themselves. Customers will stay with institutions that offer such services.

4. Organizing Big Data: Big data is both a requirement and an impediment for financial services companies. Since various sources generate a large amount of data, big data is growing. These legacy data structures can't accommodate the amount of data coming in because they are both structured and unstructured.

Use Cases of RPA in Finance and Banking Industries

Here is a list of various tasks that are being automated in the financial services sector:

Fig 1: Use Cases of RPA in Finance and Banking Industries

In Finance and Banking

• Automatic Report Generation: A regular requirement of a bank is to generate compliance reports of fraudulent activities in the form of suspicious activity reports or SAR, which have to be checked by the compliance officers and read manually and in the details in the SAR, which is an extremely cumbersome task and takes a lot of time. But, if we implement it with natural language generation capability, this entire process can be quickly completed in record time, where it can read through the process and extract the required information for filling in SAR, which leads to a reduction in operational cost and also saves time. 

• Customer Onboarding: Customer onboarding is a long and tedious process primarily because many documents are required for manual verification. This whole process can easily be automated by using its tools to extract the data from KYC using OCR, which can then be matched with the data provided by the customer. If no discrepancies are encountered, then the data can be automatically entered into the customer management portal. This not only removes the chances of error but also saves time and effort put in by the employees.  

• KYC and Anti-money Laundering: Both these processes are very data-intensive, which makes them suitable for RPA. They can be used to catch suspicious banking transactions or automate manual processes. We can quickly implement them, which saves both time and cost compared to the traditional solutions provided. 

• Account Opening: By implementing it, the process of account opening has become much more straightforward, quick, and accurate. Automation directly eliminates errors that may exist between the core banking system and new account opening requests. Thus enhancing the data quality of the system.

• Mortgage Lending: Mortgage lending is extremely time-consuming, making it a perfect choice for automation. Automation allows for the automation of various tasks that are crucial in the mortgage lending process, including loan initiation, document processing, quality control, etc. This helps in faster completion of the process, leading to enhanced customer satisfaction. Another benefit of this is that it unburdens the employees from doing manual tasks, thus helping them to focus on essential tasks.

• Loan Processing: Loan processing has always been considered a very tedious process, even though banks have automated it to some extent. However, further automation will reduce the processing time to a record 10-15 minutes. This will lead to increased customer satisfaction and reduced workload on employees.

• Customer Service: A large volume of common customer queries makes it difficult for the staff to respond with a low turnaround time. Its tools allow them to automate mundane rule-based tasks to effectively respond to queries in real-time, thereby reducing the turnaround time.

• Credit Card Processing: Credit card processing is one of the most cumbersome and tedious processes due to its extensive validation checks. However, by implementing an RPA strategy, we can quickly decide whether to approve or disapprove an application with a rule-based approach.

• Account Closure Process: There is an enormous number of monthly account closing requests, which the bank has to deal with primarily due to customers' non-compliance. It can help solve this by easily tracking all such customers and sending them automatically generated notifications and reminders to submit the required documents.

Mode of payments involving Credit Card, Debit Card and Net Banking is prone to frauds. Taken From Article, Credit Card Fraud Detection

In Finance and Accounting

Here are the use cases for Robotic Process Automation (RPA) in finance and accounting, each summarized in two lines:

• Daily Sales Reconciliation (DSR): RPA can automate the extraction of transaction data from sales and match it with bank statements, reducing manual reconciliation efforts. The bot alerts when discrepancies are found, speeding up the process and minimizing errors.

• Bank Reconciliation (BRS): RPA automates the comparison of bank statements with financial reports, identifying discrepancies and ensuring accurate cash records. The bot clears common checks and reconciles cash and credit card transactions with ease, improving accuracy and efficiency.

• Accounts Payable (AP) Automation: RPA can automate invoice processing by extracting data, matching invoices with purchase orders, and ensuring timely payments. It reduces errors and speeds up invoice approval, enhancing cash flow management.

• Accounts Receivable (AR) Automation: By 2021, according to an EMC report, there will be 44 zettabytes of digital data. This equates to 44 quadrillion gigabytes. Financial service providers face sorting through their data to decide what is useful and what isn't.RPA automates the collection of payment data and generates reminders for overdue payments. It ensures accurate tracking of receivables and reduces delays in cash inflows.

• Tax Compliance Automation: RPA automates the extraction of financial data for tax calculations, ensuring timely and accurate tax filings. It reduces the risk of human errors in tax reporting and helps stay compliant with tax regulations.

• Fraud Detection and Prevention: RPA continuously monitors financial transactions and flags suspicious activities based on predefined criteria. It enhances security by enabling real-time fraud detection and reducing the risk of fraudulent transactions.

• Financial Reporting Automation: RPA streamlines the collection and consolidation of financial data, ensuring faster and more accurate financial reporting. It automates the generation of reports and improves compliance with regulatory requirements.

By 2021, according to an EMC report, there will be 44 zettabytes of digital data. This equates to 44 quadrillion gigabytes. Financial service providers face sorting through their data to decide what is useful and what isn't.

List of Accounting and Financial Services Companies Using RPA

Zurich Insurance

Global insurer Zurich has implemented it and freed up to 40% of its commercial underwriter process. This has allowed them to focus on high-value-added tasks and devote more time to complex policies. Zurich related that its pilot program realized a 50% cost reduction, motivating it to expand its implementation further. It has also won an award for the same.

Global Insurer

A large global insurer with operations across the world and businesses in all lines benefitted from its implementation. Earlier, it had to go through 26 different sites and repeatedly search to make sure payment against claims was being made and had to do this four times on different dates of the month. After the implementation, this task of 4 days was reduced to only 2 hours, saving those thousands of hours of FTE in a year and also reducing errors.

OCBC Bank (Singapore)

OCBC Bank, a prominent Singaporean bank, reduced the time required to re-price home loans from 45 minutes to just 1 minute by deploying RPA. The bots not only re-price the loans but also check customers' eligibility, recommend options, and draft the necessary recommendation emails, significantly improving efficiency and reducing human intervention.

Sumitomo Mitsui Banking Corporation (Japan)

Sumitomo Mitsui, a major Japanese financial institution, leveraged RPA to cut out 400,000 hours of manual labor annually. By automating its processes, the institution significantly boosted its operational efficiency, freeing up time for employees to focus on more strategic tasks.

 Benefits of RPA in Finance and Banking 

1. Accelerates Customer Onboarding: RPA speeds up the customer onboarding process by automating data entry and verification, reducing wait times, and improving efficiency.

2. Increases Revenue and Cash Flow: By automating routine tasks, RPA frees up time for employees to focus on more revenue-generating activities, leading to improved financial performance.

3. Reduces Operational Costs: RPA minimizes the need for manual labor, cutting down operational expenses while maintaining high levels of accuracy and efficiency.

4. Automates Manual Processes: RPA automates repetitive tasks, reducing the reliance on human labor and improving operational speed.

5. Reduces Human Intervention: By replacing manual tasks with automated workflows, RPA ensures higher process efficiency and fewer chances for errors.

6. Decreases Customer Loss: Automation streamlines services, reducing delays and ensuring customers receive timely responses, which in turn lowers the likelihood of losing clients.

7. Enhances Customer Experience: By offering faster and more accurate services, RPA improves the overall customer experience, helping banks meet customer expectations.

8. Reduces Churn: RPA helps reduce churn by improving service delivery and ensuring more personalized, timely engagement with customers.

9. Improves Customer Satisfaction Quotient: With quicker processing times and fewer errors, RPA enhances the overall customer satisfaction quotient, fostering better customer relationships.

10. Develops Customer Loyalty and Trust: The efficiency and reliability offered by RPA help build customer trust, which in turn nurtures long-term loyalty.

11. Enhances Data Quality: RPA ensures higher data accuracy and consistency, eliminating the errors typically associated with manual data handling.

12. Stabilizes Industry Best Practices: By automating tasks according to standardized processes, RPA helps banks adhere to industry best practices, ensuring compliance and reliability. 

Risks Associated with RPA in Banking & Finance

All process changes, and the adoption of new technology come with their share of risks, which can affect an organization’s functionality. However, compared to long-term core technology implementation, the risks associated with RPA are generally lower, as robots can be turned off without major disruptions. Below are some key risks associated with RPA in banking and finance:

1. Operational Risk: Employees might fear job displacement due to increased automation, but in reality, RPA acts as a tool to support staff by automating repetitive tasks. This allows them to focus on more critical business functions, thereby improving overall productivity.

2. Compliance Risk: Although RPA is user-friendly, managing its scope and inventory is complex, and it requires robust audit trails to ensure compliance and avoid discrepancies in operations.

3. Data-quality Risk: The quality of data might be compromised if there are inconsistencies in how information is displayed or structured, requiring a coordinated approach to ensure that data standards are maintained across systems.

4. Ethical Risk: There is a need to balance investments between people and technology; overly relying on RPA may negatively affect team member morale. However, an optimal combination of human expertise and RPA can yield the best results, preserving jobs while enhancing efficiency.