Intelligent document processing in commercial and retail banking can automate a number of common processes, including the following.
Automating customer onboarding
Whenever a commercial or retail bank gets a new customer, it requires reams of documentation to get the customer set up correctly in its systems. In addition to the bank’s own account or loan application forms, it may also require tax returns, identification, proof of address, statements from other institutions, and the like. Banks may also have to reach out to other institutions to pull credit reports or transfer funds, for example.
While these forms may be in a standardized format for whatever institution they come from, the fact that a given bank will be dealing with dozens or hundreds of third party companies effectively makes all of this documentation unstructured.
Consider just one data point: the customer’s nine-digit Social Security number. This number may appear in different places on different forms and be rendered differently, some with dashes, others without. A templated approach to process automation would require a separate template for each possible permutation of the social security number. IDP, on the other hand, can recognize a Social Security number no matter what form it may take or where on a document it’s located. The same applies to all sorts of other information: customer name, address, account numbers—you name it.
With that kind of capability, financial services firms can largely automate the customer onboarding process, perhaps requiring only a supervisor to check for accuracy as a final step.
Meeting the LIBOR challenge
The LIBOR interest rate benchmark has been phased out as of the end of 2021, meaning commercial and retail banks need to find any loans that reference it. They could have a team of employees pore over paper documents looking for relevant terms – or train an IDP model to do it for them. An intelligent automation model could search thousands of documents looking for LIBOR-related terms, extract relevant data from any documents it finds, and enter the data into a downstream tool to gather all the LIBOR loans in one place. (For more detail, read our blog post: “Don’t Labor over LIBOR: Meet the Looming Deadline with Intelligent Automation.”)
Processing ISDA Master Agreements
Processing over-the-counter derivatives transactions requires examining the ISDA Master Agreement that defines the terms between the two parties involved in the trade. It’s a herculean task, given the ISDA document weighs in at 28 pages and is really just a template; different variables will apply to each transaction. It can easily take a human 2 hours to process a single one and large banks may process hundreds of thousands per year – making the processing of ISDA agreements a prime candidate for intelligent automation in banking. (For more, see the post: “Process Automation Comes to ISDA Master Agreements.”)
Automating Mortgage Processing
Assessing the creditworthiness of an applicant for a commercial or retail mortgage means examining reams of documentation, from W-2s and bank statements to tax returns and purchase and sale agreements. It’s a labor-intensive process that involves having employees extract key data points and enter them into spreadsheets or another downstream system for processing and analysis. Given the varied nature of the documents, templated tools or RPA won’t get you far, but an intelligent document processing tool will be able to dramatically reduce processing time. (For more about this and other use cases, see: “3 Use Cases for Document Processing Automation in Commercial Banking.)
Commercial banks in the U.S. must comply with various regulations intended to detect money laundering. In practice, that means collecting numerous documents from clients intended to prove they’re legitimate and ongoing monitoring for any negative news about clients. Traditionally these were manual processes, but today tools like the Indico Unstructured Data Platform can automate large portions of anti-money laundering programs. (This topic is also covered in the post “3 Use Cases for Document Processing Automation in Commercial Banking.”)