The biggest technological trend in the financial sector is undoubtedly Robotic Process Automation. Quite simply, RPA software employs "robots" to carry out rules-based processes in a more efficient, secure and effective way than performed by their human counterparts.
The benefits of RPA are clear: improved performance and accuracy, reduced cycle times, and it is cost-effective. But the question facing many corporations is when to use RPA, or more specifically, for which processes can it be applied to?
Areas of RPA development
The most common areas where RPA can be used to enhance productivity are within finance, supply chain, people management, and IT. The ideal processes that RPA can help fix are ones with tasks that are repetitive, rules-based, and require a template with data being entered into specific fields.
Within the financial services industry, there are several processes where RPA is particularly useful. Some examples include opening and closing of an account, account audit requests, and claims processing among others.
RPA Use Cases within Finance
Robotic Process Automation focuses on freeing up employees from having to perform time-consuming rules-based activities. More than 50% of users of RPA technology belong to the finance sector, given that there are many processes ideal for RPA software. Some of these processes include:
- Loan Application Processing: rules are applied to train the robot to recognize actionable fields from the title documents, then extract key data and finally accept and close the title service request.
- Trade Execution: rules are determined to monitor breaches in a threshold, after which the robot alerts the broker to take action. The bots can also take action or offload trade positions.
- Know Your Customer (KYC): it is possible to automate parts of the KYC process through RPA. When cases require human intervention, the bot sends the information to an employee.
- Logistics/Trade Finance: even when multiple parties and documents are involved, RPA can be used with trade finance applications. An extensive rule set is not always necessary, as the bot is trained through the work of employees.