This blog post was featured on Forbes.com
When one thinks of digitization and automation in financial services, dynamic areas such as payments and digital wallets (think Venmo, Apple Pay) come to mind.
Commercial lending, on the other hand, is a document- and process-intensive business with origination teams finding the deal, underwriting teams crunching the numbers, credit teams opining on probability of default, legal teams creating loan documents and, finally, operations teams undertaking the monthly servicing of the loans for a period of anywhere from one to seven (or more) years.
There are some areas within commercial lending, such as negotiating fees and terms for a borrower, where automation will not make sense, but there are others where it is starting to make a difference.
RPA Use Cases In Commercial Loan Operations
From our work benchmarking various commercial lenders, we are seeing robotic process automation (RPA) being applied in the following areas:
1. Manual Data Entry
During the initial loan booking, lenders use the credit agreement to manually create booking sheets for manual servicing system input. On syndicated General Merchant, participants receive notices from agent banks and manually key in data points from the notice into the servicing system. RPA tools are utilized to scrape key loan terms from lender group notices and legal documents and auto-populate the booking systems.
2. Email Overload
Operations teams receive hundreds of emails daily from internal groups, as well as from agents, participants, trustees and borrowers. Distribution lists are useful but do not solve the email overload issues. Also, it is difficult to show an audit trail when requests and tasks have been driven through a multi-thread email conversation.
Through workflow routing, incoming emails are routed and actioned based on automatic reading of keywords and/or attachments. Amendments and other loan modifications kick off a workflow process, ensuring important items are not lost in email chains.
3. Limited Borrower Self-Service Capability
Operations responds to basic customer and internal inquiries regarding the loan. Asset-based lenders receive daily borrowing bases via email, and operations teams must extract and analyze the data.
Borrower portals allow customers to check key loan terms, and chatbots can answer basic questions without operational intervention. Asset-based borrowers can upload their borrowing bases daily, with key data extracted automatically.
4. Static Reporting
Operations teams have built static reports showing basic portfolio stratifications by geography, industry and risk rating, but other value-added analytics is performed offline. Using data analytics tools such as Qlik or Tableau, you can uncover business insights such as customer profitability and cross-sell opportunities.
How To Get Started
So, how do you make sure your implementation of automation is effective? With a bit of planning, the following steps will help you see swift and tangible results:
1. Identify Candidate Processes
Since not all processes lend themselves to RPA, it’s important to identify the processes that do. Those that are repetitive and based on concrete rules with very limited exceptions work best. Begin by identifying a group of these processes, and make them your “automation wish list.”
2. Ensure Organizational Ownership
Processes that are automated must still be documented both for internal use and for regulators. Remember that the bot will need to be maintained and/or modified as systems change or new products are brought online.
3. Build And Evaluate Lessons Learned
Build your initial bots, and put them into production. Then stop. Conduct a lessons-learned session to see if ROI projections were met and what the organizational impact has been.
4. Create An Automation Road Map
Armed with the experience of the first bots and the lessons learned, go back to your wish list, and determine if everything on it still makes sense. Then create a road map for implementation.
Automation can make a real impact on commercial loan operations. While not a complete cure-all, it is helping operations teams elevate their contributions from the realm of data entry and simple query response to the more valuable world of data analysis and front office partner.