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For years, banks and financial services firms have been trying to automate document-intensive workflows such as the mortgage origination process, but with limited success.
Early financial services process automation attempts relied on systems based on keywords, rules, or templates, all of which fall short when confronted with unstructured data. Such approaches would have to account for every possible document type a financial institution may come across – an unlikely scenario given the range of documents and data 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 application forms, perhaps tax returns, statements from other institutions, credit reports, and the like. You would have to spend millions paying consultants to develop templates or writing rules for every possible document type you may encounter. Even so, as soon as a new document type came along or an existing document format changed, the automation would break down. Money wasted.
OCR is machine learning technology that can convert documents such as PDFs into a machine-readable format. That’s useful, but it still leaves you with templates to extract useful information and issues when there’s a document change.
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, say, a mortgage processing system. That would be a process that’s ripe for automation using RPA.
But that’s not at all how processes tend to work, especially those dealing with unstructured data, which is most of them. With unstructured data, a human has to read the document and decide what data to extract. (RPA can, however, complement IPA in a financial services automation solution, as we discuss briefly below and in detail here.)
As implemented by Indico, intelligent automation in banking and for other financial services is fundamentally different from OCR, RPA, and templated approaches because it can understand document context much like a human does. That’s because Indico’s Unstructured Data Platform is based on a model that incorporates 500 million labeled data points, enough to understand human language and the context of a document.
Building an effective digital process automation model requires having a large set of data to “train” on. It’s that trove of data that brings intelligence to any artificial intelligence solution. But even the largest financial services company or commercial bank would be hard-pressed to come up with that much data and effectively train its models.
Indico uses an AI technology known as transfer learning to create custom models that can tackle virtually any downstream task – including customer onboarding and other standard financial services and banking processes. The result is that 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 Process Automation page.)
The LIBOR interest rate benchmark is due to be phased out at the end of 2021, meaning commercial banks and other financial institutions need to find any loans that reference it. They could have a team of humans pore over paper documents looking for relevant terms – or train an IPA model to do it for them. An IPA 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 details, read our blog post : “Don’t Labor over LIBOR: Meet the Looming Deadline with Intelligent Automation”.
Investment firms rely on data in SEC earnings reports to inform their analysts’ advice. Analysts study quarterly 10-Q and annual 10-K forms looking for actionable data, pull it from the reports, and enter it into spreadsheets. An effective intelligent document processing tool could take on this task, providing more time to analyze the results. See the blog post :“Bringing Intelligent Process Automation to Financial Document Analysis.”
Processing over-the-counter derivatives transactions require examining the ISDA Master Agreement that defines the terms between the two parties involved in the trade. It’s a herculean task, given that the ISDA document is 28 pages and 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 ISDA agreements a prime candidate for intelligent automation in banking. For more, see the post: “Process Automation Comes to ISDA Master Agreements.”
Assessing an applicant’s creditworthiness for a commercial mortgage means examining reams of documentation, including W-2s and bank statements to tax returns and business plans. It’s a labor-intensive process that involves a human extracting key data points and entering 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 too far, but an intelligent document processing tool will 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 to prove they’re legitimate and ongoing monitoring for any negative news. Traditionally these were manual processes, but today solutions like Indico’s IPA platform 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.”
Financial firms involved in trading know that the confirmation process can get complex and time-consuming, which is why many of them are now looking at intelligent document processing as a way to streamline the process. Any trade – including over-the-counter stocks, stocks traded on an exchange, and derivatives – requires a settlement process and, ultimately, a trade confirmation. It’s an important step as the confirmation spells out the terms the trade was executed, so both sides can see whether the trade matched their price, quantity, and timing expectations.
While intelligent process automation often works well on its own, some financial and banking industry processes can benefit from the combination of IPA and robotic process automation.
RPA is effective at automating deterministic business processes that involve highly-structured data. So, it helps automate repetitive, labor-intensive tasks, often with greater accuracy than humans – because software robots don’t get tired. Meanwhile, IPA can automate processes that involve unstructured data, making it complementary to RPA.
One example of how this can play out in practice is in automating lockbox processing, which involves matching payments, in the form of checks, to invoices. An IPA solution “reads” invoices and checks, extracts relevant data, translates it to a structured format, and then hands it off to an RPA tool for input to a downstream processing system.
In this age of digital transformation, financial institutions and banks must take steps to achieve efficiencies wherever they can. Our intelligent document processing solution is the solution that helps in that effort while delivering significant benefits, including:
Automation increases employee productivity up to 4x, allows the company to increase revenue with existing headcount.
Automating manual processes provides companies to get work done faster, while increasing accuracy.
Taking mundane tasks off an employee’s plate frees up time for more rewarding work that’s also more valuable for the institution.
Automation requires companies to codify processes that were performed for years without formal agreement on how the process should happen. In the process, companies are often able to streamline the process and make it more efficient.
Automation makes organizations more competitive, taking a page out of the book of the most nimble fintech startups.
A common misconception about automation, it will displace lots of humans from their jobs. In our experience, that is not the case. Instead, automation augments employee’s work, relieving them of the most repetitive, uninteresting 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.” According to McKinsey, the more likely scenario is that portions of jobs will be automated – about 30 percent of the activities in 60 percent of all occupations,
In the financial and banking world, this means your employees will have more time to dedicate to strategic efforts that give you a competitive edge.
What Indico calls intelligent process automation can go by various other names. Analyst firms, including the Everest Group, use the term “intelligent document processing” while others go with just “intelligent automation.”
Gartner lumps various automation technologies under the term “hyperautomation,” defined as: “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.”