The banking and financial industry are required to continually evolve to compete in a sector with high-security needs and digital-first customers. As fintech companies becoming mainstream players, banks must consistently improve their services and provide a good customer experience to meet the competition. They are also required to cut down costs and increase productivity even if skilled resources are scarce. According to this PwC paper, 81% of banking CEOs are concerned about the speed of technological change, more than any other industry sector. Robotic Process Automation (RPA) can help banks meet these challenges, streamline processes, and help offset increasing personnel costs.
Use Cases of RPA in Banking and Finance Businesses
Banks occupy are subjected to a lot of rules and regulations. Many of the compliance processes are rule-based where employees perform routine and largely mundane tasks that tend to be time-consuming, error-prone, and detrimental to employee’s work satisfaction. RPA can be used to automate these rules-based processes to save time and cost, ensure accuracy and allow employees to engage in more intelligent work such as, in the compliance stage, human validation.
Compliance Tasks Automated with RPA:
- Aggregate data from multiple sources for reports
- Store and understand regulations for accurate and compliant processes
- Automates compliance testing procedures
- Saves and logs all actions for secure and simple auditing
Opening a new account requires a lot of paperwork which is then uploaded to banking systems. This process not only takes a lot of time but is prone to errors. It is not uncommon for human employees performing repetitive and mundane tasks to slip up and make mistakes. This leads to further delay in the process. RPA helps speed up the process and eliminate mistakes as bots are programmed to follow specified rules and cannot be fatigued or distracted. RPA bots can extract information submitted by applicants, process them, and upload them in different applications or systems.
Know Your Customer (KYC) or Customer Due Diligence (CDD) is a critical and mandatory process for banks onboarding new customers. This process involves verification and validation of information and documents provided by the customer, and as such consumes a lot of time and effort when carried out manually. According to Thomson Reuters, financial firms spend around $500 million annually ensuring KYC compliance. RPA can extract and collate data even from non-electronic documents using Optical Character Recognition (OCR) technology and verify them by matching against the government or public databases that store the original record. RPA in KYC can greatly increase the speed and accuracy of the process with minimal human involvement. It also improves the customer experience by making the entire process seamless.
Anti-Money Laundering (AML) and Fraud Detection
Frauds have not only increased in today's digital world, but they have also become more sophisticated and harder to detect through traditional security mechanisms. Money laundering has become a huge problem and banks must comply with Anti-Money Laundering (AML) regulations. With digitization, the volume of transactions has increased so much that detecting and flagging suspicious transactions manually has become difficult.
RPA can detect fraud patterns, flag the account, and report it to the concerned authority or department. If suspicious transactions are reported by AML systems, RPA can help in the investigation as it is well suited for working with a large amount of data following specified rules. AML systems may also raise alerts for false positives and put a strain on available resources, but with RPA that no longer remains a concern as RPA is meant for high volume and repeatable tasks. Furthermore, if specified in the rules, RPA can even stop suspicious transactions and block accounts temporarily.
Mortgage loan processing usually takes a long time as the application must undergo verifications and inspections before approval. A small mistake can set back the process leading to complications and delays. Since this process is largely rules-based, RPA can be used to automate the mortgage lending process. RPA can work with documents in different formats including paper documents and extract data using optical character recognition (OCR). Faster processing and approval of mortgage loans leads to enhanced customer experience.
Banks generate reports on various processes and present them to stakeholders. This helps stakeholders evaluate their performance and has a bearing on the reputation of the institution. Creating these reports by pulling data from various sources is a time-consuming and data-intensive process. RPA can be of great advantage in this process as it can automate the extraction of data from different systems and use templates to generate a report without any errors. After the information in the report has been validated, the report can then be scheduled to send to different recipients.
Banks deal with a huge volume of requests and queries from customers which could vary from requesting account information, loan requests to reporting frauds. Customer support teams are typically overburdened with routine tasks and may not always be able to respond promptly. With RPA bots, a lot of low priority and rule-based repeatable tasks can be automated so that customer queries are responded to in real-time. This allows the customer support team to focus on delivering more valuable service to the customers creating an enhanced customer experience.
Banks and other financial institutions operate with a high volume of repeatable tasks that must be processed quickly and accurately. There are many benefits to implementing RPA. RPA is much easier to adopt and can transform the efficiency of an organization by increasing productivity, cutting down costs and bringing about customer satisfaction.