How to transform finance with Intelligent RPA?

First of all, does this sound familiar to you?

  • Organizations house many different applications for multiple financial processes;
  • all these applications serve as a source or endpoint for financial data;
  • all this data is essential of multiple process flows across the organization;
  • it's also used on a periodical base to generate reports or it serves as an input for other processes.

It is clear that financial data is used to take very important decisions on a daily basis and that financial processes of organizations rely on accurate and consistent data coming from varying sources and applications. Efficient and automated financial process flows are crucial to support the organizational digital transformation journey.

Although process automation and lean management in finance is not new (we all know the excel macro's and the ERP plug-ins 😉), we believe that there is still a lot of potential for increased efficiency, reducing overhead costs, and letting employees do more meaningful work instead of collecting and correcting data as a core part of their job.

Do we have your attention? Great, let's talk RPA then, shall we. 🤖

RPA & Intelligent Automation

First things first: RPA isn't new. Robotics Process Automation (RPA) was first introduced in 2012, and although the technology has evolved massively in these six years, it's only since 2017 that we've started seeing large scale implementations in organizations. What's new in 2019 however, is that we see RPA rapidly evolving into a connected platform that enables Intelligent Automation. The difference between these two lies in their scope and impact.

RPA was, and still can be, used to automate basic manual and repetitive processes. Compared to traditional automation strategies, RPA can work fully cross-platform and with any application. These software robots attain 100% accuracy in their operations, therefore consistently improving data quality across the financial processes. They can be built completely custom to each financial process, and are much easier to develop and implement than conventional process automation platforms.

Intelligent Automation (IA) on the contrast aims at a much bigger scope than RPA, but also can't exist without it. IA generally consists of three elements: RPA, various technologies housed under the Artificial Intelligence (AI) umbrella, and Smart Analytics. Try to look at it like looking at the different crucial pieces of a car: the engine and internal computer are the most essential building blocks, and they do most of the work to move the vehicle. Then there are also various sensors that feed external information to the engine. Third, you have the dashboard and all its lights that inform the user on essential information of the car like its speed and RPM but also indicate problems. In this example, RPA is the engine, the sensors are the AI, and the feedback lights are Smart Analytics.

But enough with this comparison (yes I like cars, can't help it): how does this map onto the financial organization?

  1. RPA is the driving force behind the automated processes. It performs rule-based repetitive tasks many times faster and more accurate than any manual worker could ever do, and on top of that robots never sleep: RPA can perform these tasks 24/7.
  2. Artificial Intelligence technologies such as Optical Character Recognition (OCR), Natural Language Processing (NLP) and Chatbots enables interpreting external unstructured data.
  3. Smart Analytics can report on the output from RPA-enhanced processes in an intelligent way. The purpose here is to help organizations make better decisions.

Now that it's clear what RPA & Intelligent Automation entail, let's zoom in on what it means for the finance department. 👀

How RPA and Finance come together

As mentioned, each and every process in an organization encounters these problems at some point:

  • Many different software applications
  • Structured and unstructured data from many different sources
  • Repetitive manual tasks

On top of that, finance encounters some additional and unique challenges as there is often a lot of preparatory work needed for processes such as month/quarterly-end closings.

Luckily, RPA is a perfect solution to automate these rule-based processes within the finance departments.

Through our knowledge and experience with RPA in finance teams, we describe some use cases where RPA and Intelligent Automation can immediately provide added value:

Purchase-to-Pay (P2P)

Dealing with suppliers and outgoing invoice payments regularly involves extracting invoice and payment data from multiple systems like the ERP system, CRM tool, vendor management, and logistics tools. Not all of these systems allow easy integration, which results in companies getting creative with extracting information from one system and entering it into another. We often see many of these processes already partially automated, but it's usually done via either expensive and complicated back-end automation tools or through custom-made in-house code that nobody understands anymore after a while. Maintenance then becomes more expensive and time-consuming than the initial band-aid solution. Automating these processes using RPA & IA results in reduced costs, increased accuracy, and effortless maintenance.

Order-to-Cash (O2C)

The same trend continues when we look at O2C processes. Customer Master Data needs to be transferred from systems that manage leads, opportunities and quotations, into order fulfillment, distribution, and invoicing applications. Data quality is often poor and the organization needs to spend great effort to manually correct and maintain their data.

Record-to-Report (R2R)

Finance organizations are typically expected to provide senior management and the rest of the company with various reports and results. One example we see often is the month-end closing. Employees have to collect data from various systems and make sure it arrives in the right common application or spreadsheet. This often involves time-consuming data transformation, because different data sources never output this data in the right common format. In a second step these employees then manually run complicated reports of which the output is then sent to the right departments. This is usually time-consuming and error-prone. RPA solves this simply by taking all the manual work out of the equation: collecting, validating, transforming and entering the data is done in a completely automatic way, as well as running and sending out the necessary reports.

Order Process

Finally, the order process is one last example where we see RPA and Intelligent Automation making a big impact. Everything from payment runs, stock and inventory management, claims processing, order processing and audit management can often be greatly automated using RPA, IA or a combination thereof.

RoboRana shows finance the way 🤖🐸

The RPA technology is constantly evolving; new advancements are released regularly, and the uses cases are only growing in numbers.

Yet, the benefits, impact and further potential can already be seen everywhere in each process, department, and organization.

RPA & Intelligent Automation will deliver a bright future for finance, and RoboRana is excited to show organizations how.

Get in touch with us for more information!

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May 6, 2019

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