Robotic Process Automation (RPA) is a powerful technology that is used by banking firms to maximize organizational efficiency while keeping operational costs low. From streamlining back office operations, customer workflows, and document processing, RPA solutions make it convenient for bank employees to process enormous volumes of customer data without sacrificing accuracy or precision. RPA has also introduced recent innovations which make it possible for firms to process transactions seamlessly.
In this post, we’re going to talk about the latest RPA automation trends and applications in the finance and accounting industry.
RPA automation is the process of automating labor intensive, high-volume tasks done manually by users and enabling them to be performed automatically without human intervention. In the past few years, large organizations have been investing in RPA automation solutions and integrating them with other digital technologies. RPA bots are low maintenance and are configured using rule-based mechanisms, which means there are lower rates of human errors, increased efficiency, and faster processing times.
Below is a list of the latest RPA automation trends in the financial service sector:
According to Gartner, the Robotic Process Automation (RPA) industry is estimated to be valued at USD 1.89 billion in 2021 and this figure is projected to rise, with intelligent automation being at the forefront of RPA in the BSFI sector. The intelligent automation sector is forecasted to grow at a CAGR of 12.9% annually from 2018 to 2023 and trends show that these solutions will assist operation teams in automating various business functions. From loan application tracking, asset management, reducing operational costs, and speeding up banking workflows, intelligent automation will enable financial organizations to read data from multiple sources and use a rules-based approach for processing applications faster.
Complete process reengineering is one of the many focus points of using RPA solutions in finance and accounting services. With this, banks can identify all stages of the customer journey and create workflows that streamline flows among them. RPA enables customers to access and map out their financial data, giving them an intuitive user experience and letting them navigate through business complexities with ease. Studies show that banks are investing $60 million USD annually on average into automating KYC Compliance and corporate treasury processes using RPA solutions.
RPA automation is used for new product launches and introducing innovative services. Banks are developing lending apps for customers who want to take loans digitally. RPA workflows are being globally used in key processes such as processing financial transactions, reading KYC documents, human resources, managing customer relationships, and for providing quick resolutions to queries. Customer onboarding and offboarding processes are being transformed into seamless user experiences thanks to finance automation workflows.
RPA automation is enabling banks and startups to say goodbye to paperwork. Organizations are saving millions of hours in 2021 by storing their data electronically and removing the need for paper-based document storage. It is expected that in the coming years, employees will be freed of doing time consuming tasks and focus on more productive areas of finance activities.
Optical Character Recognition (OCR) is a new innovation under the umbrella of RPA that is used for scanning documents and transferring the digitized data into ERP systems. One of the major barriers in document processing is the storage and analysis of unstructured documents. OCR is integrated with complex business processes to seamlessly pull data from a variety of documents and structure it into easily readable formats. Banks are using OCR solutions to track financial transactions, generate fraud reports, and keep their general ledgers updated for better presenting reports to customers and stakeholders about their performance.
RPA and ML integrations can be paired with legacy systems and used for upgrading system workflows. It is estimated that finance departments save upwards of $878,000 per year and reduce up to 250,000 hours of rework by simply investing in these technologies.
A majority of financial institutions and banks are using RPA to automate their accounting workflows. RPA is becoming indispensable for businesses due to its following benefits:-
RPA workflows in finance and accounting help employees reduce data entry errors by ensuring precision. 32% of enterprises are automating every function and banks enjoy competitive advantages by speeding up transactions, data processing, and getting information that is both accurate and reliable.
RPA tools can be used to update legacy systems and make data models scalable for organizations. Bots are used for reducing significant labor costs, managing huge volumes of data, and migrating to other systems.
Organizations use RPA processes to get deeper insights into their data and analyze it. This leads to better reporting and predicting patterns in customer data submissions.
Organizations have to address legal compliance issues and ensure data follows state regulations. RPA tools ensure financial success by making sure data integrity is maintained, is of high quality, and complies with legal rules.
RPA tools don’t need breaks and can work 24x7 for processing data, unlike humans. The most popular use cases of RPA in finance and accounting are:
One of the challenges in finance and accounting is comparing account balances and ensuring transaction details are correct. RPA tools are used to pull data from multiple accounts, compare, and validate them
Banks and accounts payable departments in organizations use RPA to generate and automate invoice processing. Invoices come in a wide range of formats and RPA tools can process over millions of documents in different formats.
OCR and Machine Learning in RPA is used to automate purchase order processing and streamlining shipments. RPA ensures the completeness of data and helps automate data extraction from paper-based documents for transactions.
This is another area the technology is excelling at and RPA workflows ensure vendor compliance terms and conditions are met. RPA bots scan through invoices and extract key value pairs such as discounts, vendor details, rebates, etc.
When it comes to finance and accounting, it is clear that with more innovations, RPA tools will revolutionize the BFSI sector. If you’re on your path of digitizing document processing, sign up for a free demo with Docsumo and get started today.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
The traditional supply chain management approach relies heavily on manual work and is time-consuming, error-prone, and expensive. As documentation is an important part of the supply chain that consumes considerable efforts of enterprises in the supply chain workflow, it makes sense to automate the process with the help of intelligent document AI software.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.