Robotic Process Automation (RPA) has become an essential instrument for most businesses to drive cost optimization, process efficiency, improved data accuracy, and turnaround time. Banks and financial service providers are no exception.
Banking and financial service organizations have started evaluating the value of RPA tools with pilots in the areas of operations and finance due to their repetitive, time-consuming, and logic-driven tasks. The application of RPA in banking and finance industry can rapidly reduce operational costs and compliance risks. Recently, many organizations have shifted their focus decisively from exploration to enterprise-wide execution of RPA technology. Risk and regulatory compliance are popular target areas because manual compliance management in an industry that is heavily regulated and volatile results in higher operational costs, human errors, and delayed timelines.
Some processes in this domain that are good candidates for RPA include:
- Anti-Money Laundering alert notification
- Know-your-customer (KYC) profiling and validation
- Regulatory monitoring, data collection, and report generation
- Compiling customer information, screening data, and customer servicing
- Account closure processing
Let us take a closer look at the use of RPA in banking and finance operations.
Anti-Money Laundering
According to a recent report, anti-money laundering analysts usually spend only 10% of their time on analysis. These highly skilled resources invest close to 75% of their efforts into data collection and another 15% into data entry and management. Such time-intensive processes are a great fit for RPA automation.
The anti-money laundering investigation process can be seamlessly automated using RPA. This process is highly manual and takes anywhere between 30 to 40 minutes for a single case investigation. In the case of high complexity and lack of information across various systems, this process can consume even more time and effort. RPA-enabled automation can eliminate most of the rules-based tasks driving more than a 60% reduction in process turnaround time.
Know-your-customer (KYC)
Know-Your-Customer (KYC) is another important use case of RPA for banking and finance industry. KYC, an essential part of customer onboarding, is a highly daunting process involving manual verifications of several identity documents. As per a Thomson Reuters survey, the KYC compliance and customer due diligence operations cost $52 to $384 million a year for a bank.
RPA and advanced applications of computer vision (CV) can be used in conjunction to automate a range of manual operations. For example, RPA and intelligent optical character recognition (OCR) together can completely automate the manual data extraction process. This automation frees up back-office staff from dealing with several documents and hundreds of application forms daily, saving a significant amount of time and effort.
Regulatory Report Generation
Regulatory report generation is again a highly time-intensive process involving a range of manual tasks such as data extraction from discrete systems, data aggregation, creation of different templates for customized reports, reconciliation of reports, etc. As the process can be managed through certain pre-defined steps/rules, RPA can be leveraged successfully to achieve an immediate return on investment (RoI).
Customer Servicing
Today, every organization is using some form of automation in servicing its customers on different digital platforms. The chatbot is one of the highly used automation tools to deliver automated customer service. Banks and financial institutions are using customer service bots to offer quick and 24×7 responses to basic queries related to account opening, balance checks, transaction history, etc. On the other hand, RPA is used as an attended automation tool by customer service representatives to deliver accurate data more quickly. An RPA Bot can be called on-demand to access or download customer data, check external or non-integrated systems or to prevent duplicate data entry. The Bot seamlessly interacts with all of these systems and third-party websites to perform these tasks within a fraction of seconds.
Account Closure Processing
Checking to ensure up-to-date and accurate account information and transaction history, sending emails to managers and end-customers, and updating data in internal/external systems are some of the manual tasks involved in the account closure activity. RPA can be used to automate these operations and free up your knowledge workers to focus on more productive tasks.
Benefits of RPA in Banking and Finance:
The benefits of RPA in the banking and finance industry are not limited to just cost reduction and time-saving. As the scope of automation expands with the introduction of cognitive technologies such as artificial intelligence, machine learning, and computer vision, the impact of automation will be manyfold.
-
Scalability: RPA bots can be easily scaled up as the volume of data and processes increases
-
Operational efficiency: Processes are better streamlined and optimized with effective digitization driving more efficiency
-
Improved compliance: Quick adherence to changing rules and regulations saves a lot of time and effort. Moreover, RPA tools offer unmatched auditability.
-
Speed and accuracy: RPA Bots are many times faster than humans and deliver error-free results.
-
Productivity: Automation allows banks and financial institutions to free up their knowledge workers for more value-added tasks.
Continue reading to discover some more use cases of RPA in the banking and finance industry:
Loan Application Processing
The loan application process is again a good fit for RPA-enabled automation. There are several manual tasks involved in this process such as data extraction from application forms, its verification against several identity documents, assessment of creditworthiness, etc. But many times, the documents and application forms are received via email in varying formats and data structure. This is where various applications of Artificial Intelligence such as machine learning and natural language processing can be used along with RPA.
-
RPA Bots with cognitive capabilities can read emails and intelligently classify and assign them to respective agents.
-
Computer Vision-enabled intelligent optical character recognition (OCR) can be used to extract data from loan/appraisal documents.
-
Machine learning models can be used to detect fraud propensity.
Check the following demo video on the Nividous Platform being used for loan origination process automation.
Click Here to View Demo Video
Credit Card Processing
Applying RPA to automate credit card processing is another focus area where banks have seen phenomenal results. Banks can issue credit cards to customers within hours, eliminating unnecessary delays caused by manual operations. As RPA Bots navigate through multiple systems with ease, it can seamlessly extract and validate data, conduct rule-based background checks, and present accurate results that help in final approval or rejection of the application.
Conclusion
Early adopters and risk-takers have already invested heavily in RPA and AI technologies for their ability to drive unprecedented benefits. The adoption will continue to increase rapidly as automation technologies continue to improve. If you are interested to learn more about the application of RPA in the banking and finance industry, reach out to us at [email protected].
