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Intelligent Automation for Financial Services and Banking

How to automate banking & financial services processes involving unstructured documents — to increase capacity and reduce costs


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On this page, learn about financial services and banking process automation topics including:

  • Financial Services and Banking Intelligent Document Processing Use Cases
  • How to Successfully Implement Intelligent Automation in Financial Services and Banking
  • How IDP Complements RPA in Financial Services and Banking
  • Financial Services and Banking Automation: Key Benefits

Banks and other financial services companies have always dealt with an inordinate number of documents, from 10-Q and annual 10-K forms to customer onboarding documents and statements. Now that we’re firmly in the digital transformation age, the race is on to achieve intelligent document automation for financial services processing.

While they’ve had some success with robotic process automation (RPA) and templated approaches to automation, those solutions will only take them so far for a simple reason: they can only handle highly structured documents. But 85% or so of the documents financial services firms must deal with are full of unstructured content.

Dealing with unstructured documents requires an intelligent document processing platform that can “read” these documents just like seasoned employees do. A good IDP platform can quickly find and extract the relevant terms and data in a document and input them into another downstream system for processing. It all adds up to a tremendous increase in efficiency in dealing with numerous financial services use cases.

Financial Services Intelligent Document Processing Use Cases

With an intelligent automation tool such as the Indico Unstructured Data Platform, your process experts – the folks who perform the processes day-to-day – can build models that automate large portions of their jobs, leaving them free for more rewarding, strategic work. Following are just a few of the financial services use cases that intelligent document processing addresses.

Automate commercial debt document review

Financial services companies both hold and issue commercial debt. That means they must review documents such as promissory notes, to examine the economics of the debt, interest rate index, debt maturity dates, legal reporting responsibilities and more. That’s the kind of detailed data that no RPA robot or templated approach to automation will be able to deal with. But with an intelligent document processing platform built on sound AI technologies, you can build models trained to identify and extract relevant data from such commercial debt documents.

Know Your Customer requirements

Know your Customer (or Know Your Client) is a regulation that requires banks and other financial services companies to understand some key information about their clients, including their risk tolerance, financial profile, and anyone who has authority to act on a client’s behalf. If the client is a corporation, the financial institution will need to identify who its officers are, how much equity the officers hold, where the company’s operations are located, and more. An intelligent document processing platform can be used to help automate the process of collecting KYC data, saving innumerable hours of employee time.

Anti-money laundering compliance

KYC can also factor into regulations that U.S. financial institutions must comply with to detect money laundering. In practice, compliance means collecting documents from clients when they sign on with the firm to prove they’re legitimate. It also includes ongoing monitoring for negative news that may indicate legal problems. Traditionally these were manual processes, but today intelligent automation solutions enable financial services firms to automate large portions of anti-money laundering programs.

Automate LIBOR loan updates

The end of 2021 saw the retirement of the LIBOR interest rate benchmark, yet many financial services institutions are still trying to find all their loans that reference it. It’s a time-consuming endeavor because, traditionally, it required a team of employees to read reams of unstructured documents in search of LIBOR-related terms. IDP presents a better option. An IDP model can search thousands of documents, find and extract LIBOR-related terms, and enter them into a downstream tool. Such an automated process greatly simplifies the job of dealing with LIBOR, as this blog post details: “Don’t Labor over LIBOR: Meet the Looming Deadline with Intelligent Automation.“

Streamline financial document analysis

Financial services 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. (For more detail, read the blog post: “Bringing Intelligent Process Automation to Financial Document Analysis.“)

ISDA master agreement process automation

ISDA Master Agreements define the terms between parties involved in an over-the-counter derivatives transaction. Financial institutions that process such transactions must examine the ISDA documents related to each trade. It’s an onerous task, given that the ISDA document is 28 pages long and contains numerous variables for each transaction. Processing a single one can take 2 hours or more and large financial services institutions may process thousands of them every year. Given that, ISDA agreements are a good candidate for intelligent process automation in financial services. (For more detail, see our blog post, ​​“Process Automation Comes to ISDA Master Agreements.“)

Trade order confirmation automation

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.

Adding Intelligence to Banking and Financial Services Automation

The Indico approach to intelligent process automation for financial services is fundamentally different from earlier technologies such as RPA and templated approaches that use optical character recognition (OCR). Such approaches only work with documents that are highly structured, where the same data is in the same place each and every time.

But most financial services documents aren’t like that. Rather, they are unstructured, meaning relevant information and data may appear anywhere in the document. Automating processes involving unstructured documents requires a tool that incorporates artificial intelligence, along with a massive database for the AI technology to draw on.

Indico’s Unstructured Data Platform is based on a model that incorporates 500 million labeled data points, enough to enable it to understand human language and the context behind any document or image. That massive database is crucial to the ability to effectively train models that can automate financial services processes.

Using AI technology known as transfer learning, Indico Data enables financial services firms to build off that database and create their own custom models that can tackle virtually any document-intensive financial services process. 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.)

How IDP complements RPA in Financial Services and Banking

Adopting intelligent process automation doesn’t mean abandoning all your investments in ​​robotic process automation, however. Some financial services processes can benefit from the combination of using RPA to automate deterministic processes and IDP for those that require intelligence to handle unstructured documents.

One example is in the financial document analysis use case, which involves analyzing unstructured documents including quarterly 10-Q and annual 10-K forms. An IDP tool could be used to “read” such documents, identify and extract required data, and convert it into a structured format. At that point, the RPA tool could be employed to take the now-structured data and enter it into a downstream processing or analytics tool.

Financial Services and Banking Automation: Key benefits

In this age of digital transformation, financial institutions must take steps to achieve efficiencies wherever they can. The Indico Data intelligent document processing solution helps in that effort while delivering significant benefits, including:

Capacity expansion:
Automation increases employee productivity up to 4x, enabling your company to increase revenue with existing headcount.

Cycle time improvements:

Automating manual processes enables financial services companies to get work done faster, while increasing accuracy.

Increase efficiency:

Take mundane tasks off your employee plates, freeing up time for more rewarding work that’s also more valuable for the institution.

Knowledge capture:

Automation requires companies to codify processes that were performed for years without formal agreement on how the process should happen. It’s an exercise that often uncovers ways to make processes more efficient.

Compete more effectively:
Automation makes organizations more competitive, taking a page out of the book of the nimblest fintech startups.


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