Financial Process Automation Key Benefits
Capacity expansion up to 4x:
growing revenue without growing expense
Cycle time improvements:
process documents 85% faster
Increase Efficiency:
free up employee time for higher-value initiatives
Knowledge Capture:
Codify and streamline processes
Compete Effectively:
With stalwarts and fin-tech startups alike
Customer Satisfaction:
Improve turnaround times
Problems with Early Attempts at Automation in Financial Services & Banking
Banks and Financial services firms for years have been trying to automate document-intensive workflows such as the mortgage origination process, but with limited success. The reason: because most processes deal with documents that contain unstructured content.
Early financial services 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 financial institution 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 so, as soon as a new document type came along, or an existing document format changed, the automation would break down. Money wasted.
Banks and Financial services firms for years have been trying to automate document-intensive workflows such as the mortgage origination process, but with limited success. The reason: because most processes deal with documents that contain unstructured content.
Early financial services 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 financial institution 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 so, as soon as a new document type came along, or an existing document format changed, the automation would break down. Money wasted.
What About OCR and RPA?
Other early attempts at automation in financial services document 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 useful information, and all the issues that presents.
Robotic process automation in the banking and financial services 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, 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 content, which is most of them. With unstructured content, a human has to read the document and make judgment calls about it, including what data to extract. (RPA can, however, complement IPA in a financial services automation solution, as we discuss briefly below and in detail here.)
Other early attempts at automation in financial services document 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 useful information, and all the issues that presents.
Robotic process automation in the banking and financial services 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, 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 content, which is most of them. With unstructured content, a human has to read the document and make judgment calls about it, including what data to extract. (RPA can, however, complement IPA in a financial services automation solution, as we discuss briefly below and in detail here.)
Intelligent Document Processing for Financial Services and Banking
Intelligent document processing technology, as implemented by Indico, 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 Intelligent Process Automation (IPA) 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 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 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 and other common financial services and 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 Process Automation page.)
Intelligent document processing technology, as implemented by Indico, 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 Intelligent Process Automation (IPA) 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 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 own models.
Intelligent Automation in Financial Services: Solving Real Business Problems
Intelligent document processing in financial services and banking can address a number of common processes, including the following:
Meeting the LIBOR challenge
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 detail, read our blog post : “Don’t Labor over LIBOR: Meet the Looming Deadline with Intelligent Automation.”
Streamlining Financial Document Analysis
Investment firms rely on data in SEC earnings reports to inform their analysts’ investment advice. Analysts must 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 the job for them, freeing up more time to actually analyze the results. (See the blog post :“Bringing Intelligent Process Automation to Financial Document Analysis.”)
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 Commercial Mortgage Processing
Assessing the creditworthiness of an applicant 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 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.)
Anti-money Laundering
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 Indico’s IPA 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.”)
Processing Trade Order Confirmations
Financial firms involved in any kind of 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 kind of 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 because the confirmation spells out the terms under which the trade was executed, so both sides can see whether the trade matched their expectations in terms of price, quantity and timing.
How IPA Complements RPA
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’s useful for automating repetitive, labor-intensive tasks, often with greater accuracy than humans – because software robots don’t get tired. IPA, meanwhile, is able to 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 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.
Financial and Banking Process Automation: Key Benefits
In this age of digital transformation, it’s imperative that financial institutions and banks take steps to achieve efficiencies wherever they can. Indico’s intelligent document processing solution is one solution that can help in that effort while delivering significant benefits, including:
Capacity Expansion:
Automation increases employee productivity up to 4x, enabling the company to increase revenue with existing headcount.
Cycle time improvements:
Automating manual processes enables companies to get work done faster, even while increasing accuracy.
Increase efficiency:
Taking mundane tasks off an employee’s plate frees up time for more rewarding work that’s also more valuable for the institution.
Knowledge capture:
Automation requires companies to codify processes that may have been performed for years with no formal agreement on how it should be done. In the process, companies are often able to streamline the process and make it more efficient.
Compete more effectively:
Automation makes organizations more competitive, taking a page out of the book of the most nimble fintech startups.
Automation Augments but Doesn’t Replace Humans
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 financial and banking world, that means your employees will have more time to dedicate to strategic efforts that can give you a competitive edge.
What’s in a Name?
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,” 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.)”
Featured Resources
Everest Group Whitepaper: Unstructured Data Process Automation
A Deep Dive into the Role of AI in Automating Content-Centric Processes
Demo: See how Indico solves unstructured content
Learn how to drive 85% faster process cycle times, a 4X increase in process capacity and an 80% reduction in process resources
Ebook: Intelligent Process Automation in Financial Services
Learn how to automate processes such as mortgage processing, customer onboarding and more
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