What are some RPA tools that work well for banking? RPA use cases in banking While end-to-end automation is often the ultimate goal, targeted automation using RPA, when applied to the right use cases in bank operations, can deliver significant value quickly and at low cost. The following infographic shares some key examples of banks applying RPA for operational resilience, which has become essential during the COVID-19 crisis. There are many more RPA use cases in banking beyond those mentioned in the infographic. The list below highlights some of the most valuable RPA use cases in the banking industry. 1. Contact centre optimization In this COVID-19 crisis, banks are facing a huge volume of inbound calls in their contact centres as most physical interactions with customers are either non-functional or working at limited capacity. A large amount of this traffic can be handled by chatbots app development and RPA systems.For example, bots can handle simple queries related to account statements and transactions, while queries that require human decision-making are assigned to the appropriate knowledge workers. 2. Commercial financial activities Banks can use RPA solutions to scale their trade finance operations and strengthen their presence in the financial supply chain. Navidus, a leading Indian bank, uses RPA to automate the process of issuing, managing and closing letters of credit, the most preferred trade finance vehicle. Automation enabled by Nividous RPA bots can improve overall processing time by 70%, increase process visibility by 80%, and reduce operational costs by 50%. 3. Customer onboarding The customer onboarding process for banks can be difficult, primarily because it requires manually verifying multiple identity verification documents. Know Your Customer (KYC), a key part of the onboarding process, requires significant operational effort to verify these documents. According to a recent study conducted by Thomson Reuters, the cost of managing KYC compliance and customer due diligence ranges from US$52 million (for banks) to approximately US$384 million per year. Nividous has built a KYC solution that combines RPA with computer vision (CV) and intelligent optical character recognition (OCR) to capture relevant information and verify user identities presented in applications. Automation not only helps eliminate manual errors, it also significantly reduces the time and effort of your back-office operations team. 4. Anti-Money Laundering (AML) Anti-money laundering analysts typically spend only 10% of their time on analysis, according to a recent report from Booz Allen Hamilton. Most of the effort (approximately 75%) is spent on data collection and 15% on data entry and organisation. The automation of the whole AML investigation process is among the best instances of RPA in the banking industry .This process is very manual and can take anywhere from 30 to 40 minutes to investigate a single case, depending on the complexity and availability of information in the various systems. These repetitive, rules-based tasks can be easily automated with RPA, reducing process time by more than 60%. 5. Termination of bank guarantee This is a very specific and relevant RPA use case for many banks. The bank guarantee closure process requires a team of knowledge workers to manually log data between multiple disconnected legacy systems and identify bank guarantees due to closure/removal/release. Creating and distributing notifications and executing cancellation/revocation are also handled manually. Additionally, each step requires a lot of manual verification, which reduces overall productivity. With RPA, the entire process can be successfully automated. A leading bank in India successfully implemented Nividous RPA bot to automate the entire bank guarantee closing process, speed up customer communication, and reduce process turnaround time by 45%. 6. Bank reconciliation process The bank reconciliation process is time-consuming, requiring knowledge workers to manually find large amounts of transaction data involving multiple banks and balance the final numbers. RPA bots can be programmed to replace manual tasks with a variety of rule-based automation, such as validating each payment item against banking data and other records. If the items match, the record is modified. However, if there are discrepancies, the bot can send the records for further verification. 7. Loan application processing The loan application process is a great candidate for automation for banks and financial institutions. Typically, loan and appraisal request documents are received via email as a bundled PDF. Extracting data from applications, verifying against multiple identity documents, and assessing creditworthiness are some of the major manual tasks. RPA bots with artificial intelligence (AI) capabilities are used for intelligent data extraction and automation of various manual tasks. Check out the demo video below of the Nividous RPA platform being used to automate the loan origination process. Nividus smart bots, equipped with native AI and machine learning (ML) capabilities, are used to automate many of the manual activities involved in the loan application process. ● Text classification and object recognition – Navidus bots read emails, intelligently classify them, and assign them to relevant agents. ● It essentially extracts data from loan/appraisal documents using built-in computer vision and human-bot task coordination. ● Detect fraud tendencies using predictive ML models. 8. Automatic report generation Many banks and financial services providers are using RPA to automate manual tasks associated with report generation and realise immediate return on investment (RoI). Automating the report generation process involves a variety of activities, such as optimising data extraction from internal and external systems, standardising data aggregation processes, developing templates for reporting, performing reviews, and coordinating reports. 9. Account closure processing End-to-end account closure activities involve various manual tasks such as checking document availability in bank records, sending emails to customers and branch managers, and updating system data. RPA bots can automate all these manual tasks so that knowledge workers can focus more on productive activities. 10. Process credit card applications RPA-enabled automation for credit card application processing is another use case where banks have seen impressive results. Using RPA, users can issue credit cards within hours. RPA bots can easily navigate multiple systems, authenticate data, perform multiple rule-based background checks, and decide whether to approve or reject an application.