A Digital Reality Check Of The Finance Function

Anders Liu-Lindberg and Thomas Schultz

Part 8, the conclusion of the “RPA and AI in Finance series, examines the role that robotic process automation and artificial intelligence can play in finance operations.

Two weeks ago, I attended a Digital Finance Day at PwC in Copenhagen. My intention was to do a digital reality check of the finance function. I wanted to see how far we’d come in terms of artificial intelligence (AI) and robotic process automation (RPA). These two technologies are two of the most touted when it comes to digital finance transformation. On one hand, the day was an enormous success, and on the other, it was quite a disappointment. How can that be?

First, thanks to PwC for an excellent event: All the right topics, interesting companies, and many insightful cases. Job well done, and a success in my book!

On the flip side, I was disappointed with how far we’ve come. It’s one thing to imagine the possibilities of how digital can transform the finance function and another o see what’s really happening.

Let’s dig deeper into that.

Imagination vs. reality

Now, I had a lot of great ideas about what could be done in finance with AI and automation. Those can still happen, of course, but here’s what the reality check shows.

AI in finance

There are two ways I was thinking about using AI in finance. One is for automated forecasting. I’ve seen that used by at least two companies; I was hoping to hear more about that. Second is using chatbots for self-service insights into business and financial developments. I had heard about chatbots being commissioned in finance, so I wanted to see what they could do. I only got to participate in one session about chatbots in finance, and here’s what I saw.

They invited one of the leading authorities in the field from Copenhagen University, who had also started multiple companies and even sold some. I was disappointed with what I saw, though, for three reasons:

  • He showed examples of a chatbot in customer service.
  • All the interactions the robot had with the customers were pre-scripted by humans.
  • These were all simple interactions with little insight shared with the customers.

I had imagined that you could deploy a chatbot in finance and ask it about certain variances observed in your management report. You would ask: “what happened?”, “where did it happen?”, “why did it happen?”, and “what should we do about it?”

I asked this question in the session, and the answer was “Go to PwC, and in two or three months, they’ll come back with an answer.” It’s possible that my question simply wasn’t understood because we’re too far off from reality. But I expected more.

RPA 2.o in finance

RPA, at its core, is like Excel macros: They’ll do the job you program them to do and nothing else. Still, if you combine various kinds of macros or similar technologies, you can start to automate end-to-end processes. This is what I was looking for when I joined the session RPA 2.0.

We were shown a case from Danske Bank. They wanted to automate the handling of closure documents for real estate deals. The input would come in relatively standardized formats, yet each real estate agent would typically put their own unique touch to it. In addition, the documents would include free-text clauses about the individual deals and, of course, handwritten signatures and dates for when buyer and seller had signed the deal. The documents would be sent to the bank in a scanned PDF version. Does it sound like a complex task to automate?

At face value, it might seem simple, but it is quite complicated. You can see from the picture all the different solutions that were employed to extract all the information needed. Even when all information was extracted, a human would still need to read through all the clauses in the document to understand the conditions of the deal.

Let’s just say that while we can do a lot with automation, there are no plug-and-play solutions as we soon as we start to increase the degree of complexity. This solution took Danske Bank a year and a half and many concerted efforts to develop. We might say RPA 2.0, but there’s certainly room for a 3.0 and 4.0 in the future.

Therefore, we need to do reality checks

I spoke to other people at the event who attended other sessions, and they confirmed the same picture I was seeing: They thought we had come further. Therefore, we must do reality checks of our perceptions. It’s easy to read a 30-page consultant report about tech in finance and think that you’re running far behind. But in the real world, you’re likely not far behind and still have time to catch up.

Where are you on the digital finance journey? What solutions have you implemented and how are they working for you? Do you seek inspiration from what other companies are doing are trying to do it all from scratch? Let’s compare notes on the status of digital finance transformation and discuss what we can do to progress it further.

This article originally appeared on LinkedIn and is republished by permission.


Anders Liu-Lindberg

About Anders Liu-Lindberg

Anders Liu-Lindberg is the head of the Global Finance Program Management Office at Maersk and has more than 10 years of experience working with finance at Maersk, both in Denmark and abroad. Anders is also the co-founder of the Business Partnering Institute and owner of the largest group dedicated to finance business partnering on LinkedIn, with close to 5,000 members. His main goal at Maersk is to create a world-class finance function not least when it comes to business partnering. He is the co-author of the book “Skab Værdi Som Finansiel Forretningspartner” and a long-time finance blogger with 20,000+ followers.

Thomas Schultz

About Thomas Schultz

Thomas Schultz is the CCO of Enversion, an AI-experts company based in Denmark. Thomas leads the efforts of Enversion to dramatically change the engine room of the finance function.