By automating these tasks instead, RPA can help alleviate issues with inconsistency as well as any delays in manually collecting financial data. Also, how often do banks change key information from their CRM systems, like a borrower’s personal details, affecting an existing credit application? This is a common problem also solved by RPA systems.

– The next important phase is assessing the risk of the customer based on the financial data gathered. Usually a very manual process with limited or no automation.

– Then, most bankers run a ratio analysis to have a good idea of the client’s lending appetite, performing some projected scenarios and undertaking risk rating so to be ready when it comes the decision by the Risk Department. Automation in the commercial loan approval process is about mining for data and information, then presenting it clearly, to make a credit decision.

– Once the loan is  allocated, it has to be monitored annually, quarterly or even monthly so that risk can be managed well. Banks today have to deal with an overwhelming amount of financial data. The problem is that this information can be hard or impossible to manage because it is not standardised. This leads many banks into inefficient and risky practices when trying to track their own collections efforts manually, using pre-defined processes which rely on automated tools like software programs that are only designed specifically for that purpose.

– When the data is not structured correctly, lenders have a hard time seeing what is their exposure and how it changes over time. Lending institutions still use manual underwriting methods for these loans despite digital technologies being available to help streamline the process.

RPA can help mitigate that risk and facilitate the decision making process for a new loan by using a robot (i.e. a software tool) to automate simple, well defined and repetitive tasks that neither customers nor existing employees want to do anymore.