How Automation Is Changing Financial Services

Ivona Crnoja and Nik Sacher

New artificial intelligence (AI) technologies are in the process of fundamentally transforming the financial services market. Over the past few years especially, the financial sector has recognized the economic benefits of AI. These include, for example, gaining new insights from existing data to optimize credit decisions and improve financial risk management, automating business processes that previously required manual human intervention, and improving the customer experience through intelligent chatbots. AI is truly disrupting the sector with “frontrunner financial services firms achieving companywide revenue growth of 19% directly attributable to their AI initiatives.” And the transformation has only started.

The role of finance has constantly evolved in recent history. It initially focused very much on labor arbitrage and shared services models, but finance soon recognized that process standardization and centralization are absolutely necessary to increase the efficiency and effectiveness of modern organizations.

In today’s world, the role has again changed fundamentally. CFOs are heavily investing in automation technologies as a next step in the evolution to enable enterprise transformation.

Automation: a key enabler to free up resources

There is no doubt about the necessity to further automate finance processes. Most finance personnel today are busy running the finance organization and taking care of operational activities. Limited time and resources are left to focus on value-adding activities like providing analytical insights. Automation serves as a key enabler to free up those urgently needed resources. Now finance professionals can not only provide real-time insights into the current status of the business but, with advanced predictive algorithms, they can look into the future and proactively steer the business.

Recent studies reveal that world-class organizations, those that are leading with respect to efficiency and effectiveness, are reallocating freed-up resources to value-adding activities. They are putting the finance organization in the driver’s seat of finance and enterprise transformation projects.

Automation has thus become a key competency and objective for many technology providers. These providers were always motivated by the idea that computers and software can do things faster, more accurately, and more reliably than humans. Yet with the emergence of new technologies like chatbots, machine learning, and robotic process automation (RPA), automation has been brought to a whole new level, making things possible that never were before.

Real-life struggles in the accounts payable department

Let’s illustrate with a little story in the accounts payable department of the Acme Corporation.

Please meet Bob.

Bob is an accountant who works eight-hour shifts at Acme Corporation and has to respond to customer and vendor inquiries that rain down on his task list every day. And a single requester can raise issues across multiple channels on more than one related issue.

After struggling to identify the dilemma posed in an inquiry, Bob has to find the related documents in the heterogenous system landscape. He is spending too much time searching for documents as more and more requests enter his inbox.

And then we have Jen.

Jen wrote an email to Acme Corp., trying to find the right person to provide information about her payment status. One day later, Jen received a reply with instructions to call the accounts payable hotline. On the hotline, she was redirected to multiple service-desk employees and waited for 30 minutes before hanging up without solving her inquiry.

At the end of the day, both Jen and Bob are frustrated. 

How can we make Jen a happy customer and Bob a happy employee?

With the help of AI, RPA, and chatbots, we can enrich the standard process and significantly increase the level of automation – even beyond the core process. The AI algorithms learn from historical datasets and the interactions of the accountant with the system, thereby improving the matching rates tremendously. Furthermore, the AI technology allows automatic extraction of unstructured information from documents, such as emails.

As a result, by combining different kinds of innovative solutions, Acme can provide a new level of operational excellence. In the end, the solutions can analyze incoming inquiries automatically and assist Bob or even directly answer the inquiry for him.

Recognizing named entities with machine learning

Machine learning-based document processing services, for example, comprise a powerful technology to detect and highlight any type of named entity in unstructured text and sort it into predefined categories. By doing so, the services help to increase overall document-processing productivity, increase efficiency, and reduce errors. Moreover, thanks to pre-trained models, the services are productive immediately and can be used off the shelf. In fact, the level of automation can be dramatically increased with the help of an adequate document-processing service. The solution not only saves a lot of money and frees up resources, but also helps speed up inquiry processing time and contributes to more timely financial information in the system.

And this is not where it ends! There are various other aspects of inquiry processes that require huge manual effort and cause extra work.

Data may need to be fed from business systems like portal pages, for example. Or how about customer interaction and customer experience data? Interestingly, although experience management is on the top of every CEO’s agenda, and most CEOs believe that their organization delivers a superior experience, only a small percentage of customers would agree.

Relevance from a CFO perspective

Studies show a strong correlation between customer experience and key financial indicators like sales growth and profitability. To put it simply, process automation is about contributing to the bottom line, while customer experience enables your organization to drive the top line.

If we return to the above example and think about how the process could be further transformed by leveraging the latest technology, both process automation and customer experience can be taken to a whole new level.

With Jen’s inquiry arriving in the shared service center and entering the ERP system, the AI functionality is immediately triggered and starts correlating the data extracted from the request, saving historical data in the system.

In a second step, the relevant business objects required for clarifying the request are identified. If further clarification or data is needed, Jen receives an automatic notification and simply logs onto a self-service UI to provide the additional information, guided by a bot. Even her feedback is captured directly in the context of the interaction and fed back into the system.

As illustrated, through seamless integration of the various technical components, both automation levels and customer experience can be improved.

Automation is already reshaping the future of work in many sectors. Finance automation will be an important milestone for future finance and enterprise transformation. Time-consuming and low-value tasks will be replaced, giving employees the chance to spend more time on higher-value-adding activities and thereby enabling employees to become an organization’s most important competitive advantage.

And what about Bob and Jen?

Jen has become a happy and loyal customer, thanks to efficient and fast handling of her requests, which provide her with an enjoyable buying experience. Bob, on the other side, has finally gained some extra time to invest in his professional development and get on track to achieving his personal career goals.

For more information

  • Watch this video about the new SAP Machine Learning business service: Business Entity Recognition (formerly SAP Named Entity Recognition), helping you analyze and extract business objects from incoming requests.
  • Check out the SAP Cloud Platform web page for more information on how to optimize your business processes and embark on your intelligent enterprise journey.

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Ivona Crnoja

About Ivona Crnoja

Ivona drives communications for the machine learning applications team at SAP in Berlin. Together with her team she works on solving tough machine learning challenges and developing new approaches with potential applications across various industries. They aim to identify applications for machine learning and developing algorithms and systems that make SAP solutions more efficient, scalable, and transparent. Ivona dedicates her time to exploring the latest machine learning trends, while writing articles to share the team's latest achievements with the world.

Nik Sacher

About Nik Sacher

Nik Sacher is principal business processes architect at SAP Digital Business Services, focusing on shared services finance and intelligent enterprise topics. Based in Walldorf, he drives innovation projects to enable SAP customers to consume intelligent enterprise solutions. Currently, his main target is to drive a co-innovation project with one of the biggest automotive brands in Germany for a brand-new artificial intelligence machine learning business service.