Uncovering the Data Extraction Challenge of Annual Reports
When you’re in Financial Services, you’re different.
In fact, over time, it’s likely you’ve developed a superpower:
You see things others don’t see.
You uncover things others wouldn’t think to look for.
You learn things others won’t know...until you tell them.
This superpower comes in handy:
You’re called on to analyze stacks of documents.
Information from these documents affects many processes across the business.
Making errors is unacceptable; it can have a significant downstream impact.
But there’s a weakness that lurks…
And you might not even know you face it…
Even though it’s likely you work with it all the time.
So what is it? Annual Reports.
Not writing them.
Not reading them.
Extracting full value from the information trapped inside them.
Annual Reports are another example of complex documents that cost Financial Services companies time and money. Sometimes, they even open you up to unnecessary risk.
It’s all because of an inability to capture full information without time-consuming, manual, and inconsistent data extraction processes.
The best way to address a problem is to truly understand it.
And so, in this post, let’s dive deeper into what the underlying problem is and why it exists. That way, you’ll know precisely what to look for as you work to stamp out inefficiency and cut off any hidden risk.
Sound good? Read on!
The Data Extraction Challenge with Annual Reports
Annual Reports are produced for a reason: To provide information about a company’s mission, its history, and its most recent year’s performance.
Annual Reports are a staple to the investing community, and they also serve a strategic purpose for banks and other lenders looking to uncover potential risks before business loans are approved.
So what’s the problem? In a word, complexity.
Here’s a prime example: Our friends at Venngage offer a download of 55 annual report templates. Oh, and you can customize each one. The report will look good, but complexity lurks beneath that good-looking surface.
And here’s why.
Annual reports come in all sorts of formats with non-standard taxonomy being the norm more than the exception. Add in that important information is always presented in tables, charts, graphs, and other containers OCR has difficulty reading, and the picture is clear.
Following is more detail on challenges specific toAnnual Reports
Challenge 1: No fixed format
“If you’ve seen one Annual Report, you’ve seen ‘em all,” said no one ever.
Sure, the kinds of data in Annual Reports are consistent...we’re talking:
Product and service lines
Operating and financial review
Corporate governance policies and procedures
Profit and Loss statements
Cash flow statements
Notes to the financial statements
BONUS! Never forget footnotes and endnotes, which provide critical context
But HOW they’re presented can be remarkably different from company to company...even from year to year.
Annual Reports Don’t Follow Any Fixed Standard or a Designated Format.
When faced with the combination of a lack of standardized structure and a massive amount of information, most traditional data extraction approaches like OCR fall short. The result can be gaps in information captured, as well as inaccuracies, inefficiencies, and other unnecessary costs.
And in a world where even the smallest error can call a financial evaluation into question, that’s a big deal.
Challenge 2: Inconsistent graphics, charts, tables, and more...
Annual Reports are designed for humans to read them.
They are as focused on financial reporting as they are on marketing.
As a result, Annual Reports are typically rich with various graphics, charts, and tables, all in place not only to display information but display it in a compelling way.
This works wonderfully for marketing purposes.
But, for those firms that need the information in Annual Reports to make financial recommendations or lending decisions, how these graphics, charts, and tables look isn’t just immaterial. It’s a challenge.
OCR and Similar Technologies Cannot Accurately Capture the Rich Data Sets Trapped in These Graphical and Tabular Representations.
But, the manual process of going through each page of a single Annual Report is cumbersome and extremely time-consuming.
And, when you have thousands—or hundreds of thousands—of Annual Reports to review, each approaching 150-180 pages or more…...
Can you see how investment analysis can become a slow, costly, and high-risk expense rather than a strategic advantage?
Challenge 3: Non-English Languages
While many European companies create Annual Reports in multiple languages (French and English; Spanish and English; German and English), a significant number of businesses conduct and record business and financial reporting only in their own native language.
Do you have people on staff that understand and can accurately translate these languages?
(You get the idea)
If not, you’ll likely hire a third-party translator before your analysts can get to work...which delays reviews and adds to overall costs.
THE BIGGEST CHALLENGE: OPERATIONAL RISK
It all culminates here.
Data extraction technologies—like OCR—designed for standard documentation, fail to accurately extract full information— and especially context— from unstructured formats like Annual Reports.
Never underestimate the importance of context when it comes to data extraction. The point of extracting all this data is not just getting the numbers from the reports, but understanding the context.
When you understand context, you can validate entries and see how they may affect other data.
Context is another reason why—without awareness of viable alternatives—many turn to manual data extraction. It’s all because contextual understanding is a feature of the human brain, but seldom of a machine system (like OCR).
Tedious :- Can you think of anyone who would want to comb through text and data and graphics and charts and tables...day after day? Me either.
Time-consuming :- A single Annual Report can be up to 186 pages or more. When humans are left to manually extract critical data from each page, how long do you think a single review of a single document would take? Now, what if you had hundreds? Thousands to review?
Unproductive :- How do most humans react to tedious, time-consuming work? They get tired..some might even say fried. As a result, they take more breaks. They sprinkle in more interesting work. And...eventually...they look for a different job.
Inaccurate :- When the humans doing this tedious, time-consuming work become less productive because of fatigue, they make more mistakes. Inaccuracies are the bane of data-based decisions. Just one error can have a massive effect on a recommendation...and a reputation.
Costly :- There are so many factors that tie into the cost of the manual data extraction from Annual Reports.
The cost of unproductive time — Paying skilled workers to do unskilled work results in a lack of productivity due to weary, bored employees.
The cost of weary, bored employees —Weary, bored employees is more likely to make mistakes...AND more likely to leave or otherwise need replacements, resulting in higher retention or recruiting costs.
The cost of more mistakes — The mistakes of humans (or technologies not designed for extracting data from unstructured documents) result in inaccurate information.
The cost of inaccurate information — Decisions based on inaccurate data become inaccurate decisions; Recommendations based on inaccurate data become inaccurate recommendations.
The cost of inaccurate decisions — RISK...Loans offered that are higher in risk than accurate data would show.
The cost of inaccurate recommendations — RISK...Recommendations made that result in poor returns.
The cost of reputation...or worse — Can you see how this can spiral if left unchecked?
Don’t dismiss this.
Before you dismiss this as some story concocted to scare you or otherwise compel you to buy something, know one thing:
This is 100% Based on Actual Experiences.
Imagine if you could process massive quantities of annual reports...in minutes rather than months. Accurately.
How can I try Infrrd before I commit to a full deployment?
Sure. The first step is to schedule a guided demo where you get to jump into the thick of it. After you explore our solution you can try a proof of concept. When you're ready, you can deploy the system to one use case. Then more use cases. Then across your enterprise.
Glad you asked. Our data extraction process runs on servers. We have found performance and accuracy decline when running on a desktop or mobile device. (Remember Infrrd is running a powerful AI stack).
In a fast-paced world filled with never-ending rivers of documents and data, organizations continuously need smarter ways to work. Teams need flexible solutions that enable them to work faster while delivering higher levels of reliable accuracy than ever before. At Infrrd, we empower teams with Intelligent Document Processing Solutions for Intelligent Work™.