Excerpt from the eBook
Unifying IT and Finance: Embracing Intelligent Apps for Corporate Finance
Most corporate back-office functions, such as marketing, sales, and even security, have a range of SaaS applications to help their teams be more efficient and productive. But the corporate finance and accounting departments have been underserved in that respect. Much of their time is still spent on routine, mundane tasks that limit them from endeavoring in more stimulating work.
While some technology, such as Robotic Process Automation (RPA), a tool programmed to execute high-volume, repeatable tasks, might be used in a few finance workflows, its current impact is limited. RPA initially provided significant gains in efficiency for enterprises, but one issue is that it requires structured data to execute tasks. Another is that an engineer must reconfigure the RPA code if the business rules or predefined activities change or the execution of repetitive processes, transactions, and tasks involve one or more software systems.
Since RPA is highly regimented and every activity must be explicitly scripted to perform correctly, RPA is less flexible and adaptable than other forms of automation.
Since RPA is highly regimented and every activity must be explicitly scripted to perform correctly, RPA is less flexible and adaptable than other forms of automation. Additionally, RPA programs require precise instructions to extract relevant or contextual information from an input. This lack of pliability often becomes frustrating as time goes on, making RPA more of a simple, quick-fix technique for automating and digitizing processes rather than a long-term solution.
It is also important to note that RPA software, while adding much-needed automation into workflows, is not intelligent in and of itself. Human expertise, labor, and time are required to train the software. RPA does not simply “learn” by itself and does not recognize when mistakes or omissions occur. RPA software is also not smart enough to adjust automatically if a minor item is overlooked or needs to be addressed, thereby lacking the direction and ability to detect apparent errors that a human would quickly spot.
Optical Character Recognition (OCR) is in the same legacy boat as RPA. While once it helped organizations extract data to solve complex finance and accounting challenges, OCR is not enough. OCR is used to convert an image, printed text, or other structure formats into machine-encoded text and is the baseline of data extraction.
OCR’s limitations include the inability to distinguish between certain characters, erroneously identifying letters or numbers. Input to the tool needs to be within a structured format - any deviations produce errors or omissions. OCR engines are only able to process documents with printed text and will fail to read handwritten notes or other contextual elements. The list of challenges goes on and on.
Most RPA and OCR tools must be custom created to fit your business. While at first glance, a unique system designed to fulfill the needs of your business seems beneficial, bespoke RPA and OCR come with a bevy of hidden costs and issues in terms of implementation, maintenance, and overall risk.
First, the onus is on the organization to ensure that the RPA and OCR solutions run on the latest, most updated software system and that any security holes are patched. This type of work places a substantial amount of responsibility on the organization and significant demands on IT and often requires dedicated personnel to maintain the code.
Ongoing RPA and OCR maintenance is tedious and time-consuming, especially when IT is already busy with a vast array of projects.
Additionally, when updates to a customized RPA or OCR instance are issued, it is essential to ensure connected applications are not impacted negatively and continue functioning as required. This ongoing RPA and OCR maintenance is tedious and time-consuming, especially when IT is already busy with a vast array of projects. Even worse? Many businesses lack the skilled employees to handle bespoke RPA or OCR maintenance, security, and operations consistently.
The bottom line? RPA and OCR alone are insufficient for handling complex business objectives, unstructured inputs, and the intricate processes typically found in today’s complex finance office. Instead, the key to taking RPA and OCR to the next level, and unlocking all of the benefits, lies in RPA enhanced by AI-powered technology and OCR plus Computer Vision, a next-gen data detection technology that utilizes OCR to retrieve the information but then adds artificial intelligence and machine learning algorithms to identify fields and additional elements from the image automatically.
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