The financial industry is one of the most process-heavy sectors out there. From managing invoices and payments to compliance and risk management, there are a lot of repetitive, rule-based tasks that need to be carried out on a daily basis. This is where robotic process automation (RPA) can be a game-changer.

According to a recent Gartner research, around 80% of finance executives have already implemented RPA in their organizations or are planning to do so. In fact, RPA is being increasingly seen as a key technology for finance transformation.

So, what are the different ways in which RPA can be used in finance? read on

The importance of RPA in finance

Robotic process automation (RPA) can help take on the repetitive, time-consuming tasks of human workers in a variety of industries, including finance. RPA is part of the greater trend of hyperautomation, which optimizes end-to-end finance processes by using data to automate actions.

The robots used in RPA are well-suited for handling large volumes of recurring tasks without human intervention. This allows employees to focus on more impactful work, such as developing relationships with customers, analyzing data for insights, or creating new financial products. Ultimately, RPA can help improve efficiency and decision-making across the finance sector.

8 Real-life use cases of RPA in finance

Here are 8 ways that RPA is being used in finance today:

1. Improve investor relations

Finance professionals can use RPA to automate the preparation of various reports. For example, Investor relations can use RPA to generate external, periodic, and regulative financial reports automatically. This would make the process of report creation more efficient and accurate.

 In addition, RPA can be used to manage documentation pertaining to securities issuance. This would enable finance professionals to focus on other areas while still ensuring compliance with regulations.

2. Strategic finance management

RPA can also be used in strategic financial management. For example, it can help with the forecasting of cash flow and the creation of various financial models. Additionally, RPA can automate the budgeting process and help with financial planning.

 In capital management, RPA can be used to optimize cash flow and ensure that all financial obligations are met. This can free up time for treasury managers so that they can focus on developing long-term financial strategies.

DBS bank, a leading financial group in Asia, has successfully incorporated robots into their business processes in order to increase productivity and efficiency. The bank has been able to free up employees to spend more time on strategic tasks, resulting in improved customer engagement and innovation. 

IBM, which is the partner company providing the robots, has been helping DBS with not only the RPA program but also incorporating embedded analytics, machine learning, and a whole ecosystem of capabilities such as Optical Character Recognition (OCR) technologies to simplify, streamline and automate end-to-end business and operational processes.

3. Enhance operational effectiveness

Banks and other financial institutions are under pressure to drive down costs and improve efficiency. One way to do this is by automating manual, time-consuming tasks. RPA can be used to automate a range of processes, including data entry, reconciliations, report generation, and compliance checks. 

According to Gartner, one bot can displace up to X30 the work of a human Full-Time Equivalent (FTE).

This not only saves time and money but also reduces the risk of errors. Digital workers can extract data from multiple sources, including emails, PDFs, and spreadsheets, and then input it into the required systems. For instance, using NLP, they can read and understand emails to extract relevant information.

4. Fight financial crime

The world of finance is no stranger to crime, whether it’s money laundering, fraud, or terrorist financing. Numerous regulations are in place to prevent these activities, but they require close monitoring and analysis of large volumes of data. This is where RPA can help.

RPA can be used to automate the collection and analysis of data from multiple sources, including social media, newswires, and government databases. This information can then be fed into financial crime detection models to identify high-risk activity. 

AML rules can also be automatically applied to transactions, with any suspicious activity flagged for further investigation. For example, if a customer’s behavior patterns suddenly change, that could be an indication that they are involved in criminal activity.

5. Improve customer experience

In the world of finance, customer experience is everything. If your customers are unhappy, they will take their business elsewhere. To keep them happy, you need to provide a seamless, digital experience that meets their needs.

RPA can be used to automate tasks such as customer onboarding and KYC/AML checks. This not only makes the process faster and smoother for the customer but also frees up staff to provide a more personal level of service. In addition, RPA can be used to automatically generate personalized communications, such as account statements and marketing materials.

6. Prepare for new regulations

The financial services industry is subject to constant change, whether it’s the introduction of new regulations or the revisiting of existing ones. This can present a challenge for compliance teams, who need to be able to quickly adapt to the new requirements.

RPA can be used to streamline the process of updating compliance policies and procedures. For example, if a new regulation requires changes to customer identification or anti-money laundering checks, these can be built into the RPA solution and automatically applied to all relevant transactions.

7. Manage risk

In order to manage risk effectively, financial institutions need to have a clear understanding of their exposure to different types of risk. This includes credit risk, market risk, operational risk, and liquidity risk.

RPA can be used to collect data from multiple sources and consolidate it into a single view. This data can then be fed into risk models to help identify potential problems and take action to mitigate them. For example, if a bank is exposed to too much credit risk, it might need to set aside more money to cover bad debts.

8. Attain a sustainable development

The financial services industry is under pressure to become more sustainable. This includes reducing greenhouse gas emissions, financing green infrastructure projects, and promoting responsible investment.

RPA can help financial institutions to meet their sustainability goals in a number of ways. For example, RPA can be used to automatically collect data on carbon emissions from multiple sources and then input it into emissions reporting software. 

This data can then be used to develop strategies for reducing emissions. In addition, RPA can be used to screen investments for environmental, social, and governance (ESG) criteria and identify those that are most aligned with the institution’s sustainability goals.