3 Use Cases for Intelligent Document Processing in Commercial Banking
September 10, 2020 / Commercial Banking, Intelligent Process Automation, Use Case
In our never-ending search for use cases for intelligent process automation software, among the most complex we’ve found is commercial banking. From issuing commercial mortgages to meeting stringent anti-money laundering and “know your customer” requirements, commercial banking processes involve untold numbers of documents, making them perfect candidates for automation.
For years commercial banks have been seeking ways to automate processes, such as by using robotic process automation (RPA) in customer onboarding processes. RPA is indeed useful in automating processes that involve the exact same steps over and over, typically with highly structured documents.
But once commercial banks tackle that low-hanging fruit, they often find themselves wanting a more intelligent banking automation solution, one that can deal with the many unstructured documents involved in various processes. In this post, we’ll look at three such use cases for intelligent commercial banking automation solutions.
Automating the commercial mortgage process
Let’s first consider the commercial mortgage process. All commercial banking is about assessing the creditworthiness of an applicant to ensure the lender can make money while managing risk. At the commercial level, this is a lengthy process that involves reams of documentation, from W-2s and bank statements to tax returns and business plans.
Traditionally, all of these documents would be reviewed by humans, who extract key data points and enter them into spreadsheets or other downstream systems for analysis. It’s a cumbersome, time-consuming process.
A poll conducted by Moody’s Analytics asked bankers what their biggest challenge was in initiating the commercial loan process. More than half (56%) said it was the manual collection of data and subsequent back and forth with the client.
An intelligent commercial banking automation tool would help the bank in a number of ways. Most banks now have an online application process. As a first step, the IPA tool could classify all of the documents to make sure all required documents have been filed. It could then “read” the documents and extract the relevant data points required for downstream processors to make an informed decision on the application.
Here’s where the big difference lies between RPA, as well as templated approaches to automation, and an intelligent automation tool. While the former can deal only with highly structured documents that are all the same, IPA uses natural language processing (along with, in Indico’s case, a massive database of known terms) to discern context in a document. That enables the tool to read a document much like a human would and find whatever data it’s been trained to find, no matter where it may be located.
Related Article: How Automation Eases the Pain of Commercial Bank Customer Onboarding
Implementing automated anti-money laundering
Another common use case for commercial banking automation is meeting regulatory requirements around anti-money laundering (AML). In the U.S., that means complying with the Bank Secrecy Act and related regulations meant to deter money laundering by terrorist financing networks and drug cartels.
The law requires banks to take numerous steps to deter and detect money laundering, including:
- Establish effective customer due diligence systems and monitoring programs.
- Screen against Office of Foreign Assets Control (OFAC) and other government lists.
- Establish an effective suspicious activity monitoring and reporting process.
- Develop risk-based anti-money laundering programs.
In practice, here again this requires collecting numerous documents from clients intended to prove they are legitimate businesses, as well as monitoring various websites for negative news about a client. Traditionally, these were all manual processes requiring ever-larger legions of employees to perform.
The ability of IPA tools to examine and understand documents as a human would changes that dynamic, making it possible to automate large portions of an AML program, leaving only the thorniest cases to human experts.
Know your customer automation
Closely related to AML requirements is “know your customer” regulations, and they present similar challenges. As part of the commercial banking client onboarding process, these laws require banks to make an effort to verify the identity of customers as well as the risks involved in any business relationship with them.
Here again, compliance requires examination of numerous documents proving the customer is who they say they are, that their source of income is legitimate and more. Adding to the challenge is the fact that KYC laws vary by country and state, as this guide from PWC makes abundantly clear.
Noncompliance can be costly, as AI Business reports:
“Since the global financial crisis that started in 2008, Aurexia Institute reports there has been over USD $450 billion worth of compliance penalties worldwide. Regulations are also becoming more complex to comply with; since 2016 alone, compliance teams have faced over 51,000 regulatory changes. The typical “bulge bracket” institution is spending over USD $1 billion annually on compliance.”
Applying intelligent automation to the process will not only help commercial banks ensure they are in compliance, but free up employees for more strategic and rewarding work, such as risk analysis.
To learn more about how Indico IPA tools can help free up resources in your organization, download this white paper from experts at the Everest Group, “Unstructured Data Process Automation,” or contact us to arrange a demo.