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On this page, learn about commercial & retail bank automation topics including:
• Automation in banking: key benefits
• Automating unstructured documents in banking
• The shortcomings of OCR and RPA in banking automation
• Role of intelligent document processing in commercial & retail banking
• Use cases for intelligent automation in commercial & retail banking
• How intelligent document processing complements RPA
• Key benefits of banking process automation
• Why automation augments humans, but doesn’t replace them
Commercial and retail banks are awash in all sorts of documents, from customer onboarding forms to commercial and retail loan applications, mortgage origination and refinance applications, and more. As part of their digital transformation efforts, banks are looking to automate the processing of these documents – potentially millions of them.
But banks are running into roadblocks because most of the documents contain unstructured content, which automation tools that rely on keywords, rules-based methods and templates simply can’t handle. Intelligent document processing systems, however, use artificial intelligence technology that enables them to “read” unstructured documents just like your employees do – except far faster. Employing AI in commercial and retail banking changes the automation equation and brings immediate value.
Commercial and retail banks for years have been trying to automate document-intensive processes, but with limited success. The reason: because most processes deal with documents that contain unstructured content.
Early bank process automation attempts relied on systems based on keywords, rules, or templates, all of which fall short when confronted with unstructured content. Such approaches would have to account for every possible document type a bank may come across – an unlikely scenario given the range of documents and content involved in many processes.
Consider customer onboarding. It requires all sorts of documents to get a new customer set up correctly, including the bank’s own application forms, perhaps tax returns, statements from other institutions, credit reports and the like. You would have to spend millions paying consultants to come up with templates or writing rules for every possible document type you may encounter. Even if you succeeded for a time, as soon as a new document type came along, or an existing document format changed, the automation would break down. Money wasted.
Other early attempts at commercial and retail banking automation included optical character recognition (OCR). OCR is machine learning technology that can be used to convert documents such as PDFs into a machine-readable format. That’s useful, but it still leaves you dealing with templates to actually extract pertinent information, and all the issues that presents.
Robotic process automation in banking is another possibility but it, too, has severe limitations. RPA is great at automating processes that involve the exact same steps each time. Say, for instance, a bank data entry clerk entered the exact same keystrokes in the same order time after time into a mortgage processing system. That would be a process that’s ripe for automation using RPA.
But that’s not at all how bank processes tend to work, especially those dealing with unstructured content, which is most of them. With unstructured content, an employee has to read the document and make judgment calls about it, including what data to extract. (RPA can, however, complement IDP in some retail and commercial banking automation solutions, as we discuss briefly below and in detail here.)
As implemented by Indico Data, intelligent document processing technology is fundamentally different from OCR, RPA and templated approaches because it can understand document context much like a human does. That’s because the Indico Unstructured Data Platform is based on a model that incorporates some 500 million labeled data points, enough to enable it to understand human language and the context of a document.
Building an effective automation model requires having a large set of data to “train” on. It’s that trove of data that brings bring intelligence to any artificial intelligence solution. But even the commercial or retail bank would be hard-pressed to come up with that much data and effectively train its own models.
Indico uses an AI technology known as transfer learning to create custom models that can tackle virtually any downstream task – including customer onboarding, loan processing and other common banking processes. The end result is it takes a relatively small number of documents to train the model – usually just a few dozen. What’s more, you don’t need data scientists to make it all work. Rather, business professionals on the front lines train the automation model – those who know the processes best. (For a deeper dive on this point, check out our Intelligent Document Processing page.)
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.
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 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.”)
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.”)
While intelligent document processing often works well on its own, some commercial and retail banking processes can benefit from the combination of IDP and robotic process automation.
RPA is effective at automating deterministic business processes that involve highly structured data. So, it’s useful for automating repetitive, labor-intensive tasks, often with greater accuracy than humans – because software robots don’t get tired. IDP, meanwhile, is able to automate processes that involve unstructured data, making it complementary to RPA.
One example of how RPA and AI can work together in banking is automating lockbox processing, which involves matching payments, in the form of checks, to invoices. An IDP tool is used to “read” invoices and checks, extract relevant data and translate it to a structured format, then hand it off to an RPA tool for input to a downstream processing system.
In this age of digital transformation, it’s imperative that banks take steps to achieve efficiencies wherever they can. Indico’s intelligent document processing platform is one solution that can help in that effort while delivering significant benefits, including:
A common misconception about automation is that it will displace lots of human from their jobs. In our experience, that is not the case. Rather, automation is used to augment employees, relieving them of the most repetitive, boring aspects of their jobs so they can focus on more important matters.
A recent report by the McKinsey Global Institute supports this notion. It said only about 5 percent of occupations “could be fully automated by currently demonstrated technologies.” The more likely scenario is that portions of jobs will be automated – about 30 percent of the activities in 60 percent of all occupations, according to McKinsey.
In the commercial and retail banking world, that means your employees will have more time to dedicate to strategic efforts that can give you a competitive edge.
Intelligent document processing (IDP) can go by various other names. Analyst firms including the Everest Group use the IDP term, but others may go with just “intelligent automation.” (For its part, Indico long ago coined the term “intelligent process automation,” but these days we’re happy to go with the industry flow and use IDP.)
Gartner lumps various automation technologies, including IDP, under the term “hyperautomation,” which it defines like so: “Hyperautomation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyperautomation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.)”
