With increasing competition from the fintech industry and digital-first banks, the traditional players in the banking and financial services sector need to continually evolve, remain competitive, and provide a holistic customer experience. Banks and NBFCs are looking to reduce operational costs, boost profitability, and improve productivity in addition to dealing with a lack of skilled resources, inefficient procedures, technology usage gaps, and an increase in human resource overheads. These challenges have paved the way for the adoption of robotic process automation (RPA) in financial services and banking industry to simplify and speed up the processes.
RPA can be defined as the use of applications powered by robotics to supplement (or replace) human efforts in various activities and processes. It assists banks and accounting divisions in automating the manual and repetitive tasks. This frees up the staff to work on more important activities that require a deeper level of expertise. To enhance RPA functionality, banks and other financial services companies also make use of artificial intelligence, machine learning, and natural language processing technologies. This makes it possible for RPA to manage complicated processes, comprehend spoken language, detect emotions, and quickly adapt to rapidly evolving data.
The adoption of RPA in financial services and banking sector is accelerating globally. According to a report by Fortune Business Insights, the global RPA market is expected to grow from $13.86 Bn in 2023 to $50.50 Bn in 2030, a CAGR of 20.3% from 2023 to 2030. These statistics show that RPA is an effective tool for most businesses, particularly banks and NBFCs. It is a key enabler in reducing costs, driving revenue growth, and enhancing business agility through holistic digital transformation.
Benefits of RPA in banking
In order to meet the growing needs of the banking sector, RPA is used as a tool in the SaaS model to help banks maximize their operational efficiency. To take full advantage of this opportunity, banks and financial institutions must adopt a strategic approach. Some of the benefits of financial process automation are:
- Bring down the time for activities: When a robotic application is set up, it can reduce the time needed to perform a task by up to 90%.
- Enables seamless scaling of operations: Robots can work longer hours and do not need breaks, unlike humans. They can be used for handling large volumes of requests during peak hours.
- Reduces the cost of operations: Since repetitive tasks are automated and completion takes lesser time, the deployment of RPA reduces infrastructural costs since no significant changes need to be made to the infrastructure.
- Increases job satisfaction and employee well being: Since the speed of a robot is much higher than that of a human, agents working on routine tasks can instead focus on tasks that require the knowledge and expertise of human resources.
- Reduced chances of error: Since processes are automated, mistakes such as lack of attention and memory lapse do not arise.
RPA use cases in banking and finance
While the goal is to automate end-to-end processes, using the right strategy for different use cases can have a big impact on the productivity of banking operations. Let us explore some RPA use cases that have been the most rewarding in the banking and finance industry.
- RPA for customer onboarding: The customer onboarding process is the first step in forming a new customer relationship for a bank. Due to the manual verification of numerous identity documents and the need to identify any discrepancies with the customer profile, it is difficult, time-consuming, and tedious. KYC solutions are structured in a manner that combines the capabilities of RPA with optical character recognition to validate the information provided by the customer. For the team managing onboarding, this helps eliminate manual errors and saves time and effort.
- RPA for automated report generation: In its day-to-day functioning, a bank relies heavily on system-generated MIS reports in order to modify strategies and have an insight into current performance. With RPA, banks can replace manual intervention in activities such as data extraction, standardization of the process of data aggregation, and development of templates for reporting and reconciliation. Deployment of RPA can remove the possibility of error in such a tedious process. Additionally, RPA can help compliance officers identify and process suspicious transaction reports (STR), with the help of NLP capabilities.
- RPA for Anti-money Laundering: Anti-money laundering analysts spend a lot of time on data collection, segmentation and classification and little on data analysis. AML, a critical investigative process, calls for the automation of repetitive and rule-based tasks so that turnaround time can be reduced and inconsistency in reporting can be avoided.
- Account closure processing: Account closure activity in a bank is a range of manual activities, such as checking the adherence to minimum balance requirements, collecting charges, if any, validating signatures as per the mode of operation, checking the authenticity of the request with the account-holder in case the application is a third-party submission, and updating the bank records. RPA can automate all these manual tasks so that knowledge workers can focus on operational tasks that impact productivity.
- RPA for mortgage processing: This is one of banking and finance’s most prominent use cases. Mortgage lending is extremely process-driven, time-consuming, and can take up to 60 days. Banking executives supervising loan closures need to verify employment details, credit checks, and other inspections to determine each case’s future course of action. Robotic Process Automation in banking accelerates processing and reduces turnaround time, thereby impacting subsequent procedures and productivity.
- RPA in loan application processing: The loan application process has huge potential for deploying RPA in banking industry since data extraction from applications and its verification against multiple checks are done manually. Bots with AI capabilities can be leveraged for this purpose, further expediting the determination of the customer’s creditworthiness.
Implementing RPA in financial services and banking operations is easy to deploy compared to large-scale digital transformations. Automating time-consuming tasks is a massive shift for the banking sector, enhancing productivity while powering other technologies such as AI, ML, data analytics and NLP. While the full deployment of RPA across all processes cannot be accomplished all at once and is a continuous process, banks would benefit greatly from picking a starting point. Starting with specific tasks and matching them to the company’s goals will set banking organizations on the path to transforming their core functionality. Additionally, the benefits of RPA can be reinforced by supplementing it with other AI, ML and automation capabilities to achieve a comprehensive changeover in the procedural functionality of banks and financial institutions.
- What are the challenges in RPA in banking industry?
Lack of qualified personnel with sufficient knowledge of how to use RPA effectively is one of the main problems with RPA projects. Adoption of these technologies is also hampered by business owners’ lack of support and steadfast adherence to outdated procedures. Another obstacle that must be overcome in order to reap the rewards of RPA is employee resistance to change.
- Where is RPA not applicable?
RPA cannot be implemented in processes that involve unstructured data. A huge percentage of businesses rely on working through unstructured formats. Sorting and categorizing of data cannot be done by a bot, therefore, human intervention will be indispensable for such tasks.
- How does RPA in financial services reduce risks?
Automation removes the possibility of error since the bot is automated and instructed to perform the tasks consistently. In addition, RPA helps maintain adherence to compliance protocols, thereby reducing the risk associated with non-compliance and subsequent penalties. The enhancement of data security is another reason for the reduction of risks on account of the deployment of RPA.