Even if you don’t work in the financial industry, you have to be a genius to know that finances come with a lot of processes and paperwork.

Banks and other financial services have always dealt with a bunch of documents. These papers could be tax documents, account statements, 10-Q forms, annual 10-K forms, and more. Now that we’ve entered into such a digital age, the race to achieve intelligent document processing for financial documents has already begun.

Although intelligent process automation (IPA) has been used for templated approaches to finance processes, IPA can only take it so far because banks and financial services deal with so much unstructured content. Over 80% of financial documents are considered unstructured content.

In order to process unstructured documents, a bank would need an intelligent document processing platform. This kind of processing can “read” documents just like a regular employee would. In return, the worker would free up more of their time to focus on more important or tedious documents.

A good IDP platform can find and extract relevant terms in a document and input them into another downstream system for processing very fast. With this quicker process, efficiency will increase dramatically, and numerous use cases can be identified.

Read on to learn more!

Intelligent automation tools allow the people engaging in the day-to-day processes to build models that automate large portions of their jobs. This frees them up to work on more rewarding, strategic things. Here are some of the use cases that intelligent document processing can be used for.

Automate commercial debt document review: Financial service companies hold and issue commercial debt. This means they review documents to look over the interest rate index, economics of debt, debt maturity dates, and more. All of this detailed data can’t really be automated by IPA because there is so much intricacy involved.

However, intelligent document processing builds on existing AI technologies and builds models that are trained to identify and extract specific data from these kinds of debt documents.

Streamline financial document analysis: Financial firms rely on the data found in SEC earnings reports so that their analysts can offer more informed advice. They study quarterly 10-Q and annual 10-K to look for actionable data, pull it from the reports, and record it into various kinds of spreadsheets.

Effective intelligent document processing could take on this task instead, giving human employees more time to analyze the results that are found.

Know Your Customer requirements: Know Your Customer, also known as Know Your Client, is a regulation that requires banks and financial firms to understand key information about their client. This information includes their financial profile, risk tolerance, and anyone who may have the authority to do something for them on their behalf.

If the client is a corporation, the bank will need to know who the officers are, how much equity they hold, where the company’s operations are located, and more. Intelligent document processing can be used to help automate the collections process for all of this data, ultimately saving large amounts of time for an employee.

ISDA master agreement process automation: ISDA Master Agreements define the terms between parties involved in a transaction. To process these transactions, financial firms must review the ISDA documents that are a part of each trade.

The process is very long, and a standard ISDA document is 28 pages long and contains a bunch of variables from the transaction. It can take two hours or more just to process one document, and thousands of them are processed every year. Because of this, ISDA agreements could benefit from IPA and document processing.

Anti-money laundering compliance: KYC also factors into US financial regulations because banks and financial firms must comply when it comes to detecting money laundering. Compliance means that banks collect documents from clients so that they can prove they’re legit.

It also means that there is consistent monitoring for negative things that could lead to legal issues. Back in the day, these processes were manual, but today’s intelligent automation solutions allow financial services to automate large portions of their anti-money laundering programs.

Trade order confirmation automation: Financial services that are involved in trading understand how much time it takes for the confirmation process.

Now, many of them are looking into intelligent document processing so they can streamline their processes. Any trade with over-the-counter stocks, stocks traded by exchange, and others all require a settlement process and trade confirmation. These steps are important and show how the terms of the trade were executed so both parties can see whether the trade matched their price, quantity, and timing expectations.

Automate LIBOR loan updates: At the end of 2021, the LIBOR interest rate benchmark was retired, and many financial institutions are still trying to find all of their loans that reference it.

This is an incredibly time-consuming task because usually, it required a whole team of employees to read a bunch of unstructured documents in search of LIBOR-related terms. IDP could be used to boost efficiency in this area. An IDP model can search thousands of documents, extract LIBOR-related terms, and enter them into a downstream tool. Overall, automating this process would simplify the job of dealing with LIBOR.

Intelligent process automation has been changing in the finance industry over the past few years. Older approaches only worked with documents that were highly structured, where the same data is in the same place each and every time.

However, most financial institutions are incredibly unstructured, meaning that important data can be found anywhere in a document. Automating processes that need unstructured data requires a tool that uses artificial intelligence and a large database for AI technology to use.

Some unstructured data platforms are based on a model that uses over 500 million data points so that the AI can understand the human language and context behind the data or image. The database is important for training models to automate financial processes.

Using transfer learning AI technology, data platforms can allow financial firms to create their own custom models that tackle virtually any document-heavy process. The result is that only a few documents are needed to train a model.

You also won’t need a data scientist to make it all work. Instead, business professionals working in a position can train the model so it learns from someone who knows the process best.

Some financial firm processes can benefit from the combination of using RPA to automate deterministic processes and intelligent document processing for unstructured documents.

One example that is found in the use cases section is the analysis of unstructured documents like 10-Q and 10-K forms. Intelligent document processing could be used to identify and extract specific data from these documents so that the info could be changed into a structured form. Then an IPA too could be used to take the currently structured data and put it into a downstream processing tool.

In this day and age, the finance industry needs to do everything that it can to keep up with technology. IDP platforms help with this in ways like:

Capacity expansion: Automation increases employee productivity up to 4x, enabling your company to increase revenue with the people you already have hired.

Cycle time improvements: Automating manual processes enables financial firms to get work done faster with increased accuracy.

Increase efficiency: Take mundane tasks off your employees’ hands, freeing up their time for more rewarding work that’s also more valuable for the firm.

Knowledge: Automation requires companies to tweak processes that were performed for years without formal agreement on how the process should happen. It’s a way to make processes more efficient.

Compete more effectively: Automation makes organizations more competitive because they’ll be on the cutting edge of all automated processes and improve customer engagement.