Designing efficient processes

AI brings old processes up to speed

The zeb approach for efficient processes

The challenge

Becoming efficient, staying fit for the future

In the past, banks and financial services providers were quite successful in optimizing their processes through step-by-step improvements or partly even through radical reorganization measures – this established their reputation as a pioneering industry in the field of IT. But nothing lasts forever: in order to regain momentum and reduce frictional losses in the value chains, banks must optimize their processes in a targeted manner using modern methods. If they don’t, they will fall even further behind their digital competitors. 
Compared to other areas, especially the middle and back office still have significant potential to secure their performance and thus future viability. In this context, they should not only focus on technology, but also on breaking down barriers and preparing the ground for transformation.
To achieve competitive speed and efficiency, organizations need to intelligently automate their processes and make them part of the collective subconscious. Artificial intelligence and data analytics are perfectly suited to getting processes up to speed. An organization’s existing training plan can be intelligently expanded by automating more and more processes and intermediate steps that were previously performed manually.

AI and data analytics: advantages of optimized processes

  1. Shorter lead times: automated processes are faster. Moreover, they work on a 24/7 basis – regardless of the workload. According to our experience, intelligent automation can reduce lead times by 60 to 80 percent.
  2. Reduced costs: robots and AI complete more tasks in less time and thus also save costs. Our projects show that investing into bots pays off within the first year.
  3. Higher process quality: robots and AI methods don’t get distracted. Through permanent focus and without a learning curve, process quality can be increased in the long term.
  4. Greater employee satisfaction: robots and AI free your employees from monotonous and repetitive tasks. This strengthens the organization in the fight against the shortage of skilled workers.

Training stations for process excellence

Five application scenarios for AI methods


Robotic process automation (RPA)

Using conventional methods from business process management, process quality can only be improved to a certain extent. This is due to organizational or systemic barriers in heterogeneous IT landscapes that have grown over time. These barriers can only be circumvented manually. Merging the different systems is either not possible at all or at least not in a cost-effective manner. 
In such cases, efficiency can be increased with the help of robotic process automation (RPA). In addition, RPA reduces process cycle times as well as process errors. The targeted reduction of monotonous, manual tasks provides noticeable employee relief. This not only leads to a focus on value-adding activities, but also to increased employee satisfaction.

Optical character recognition (OCR)

In order to optimize process steps using software-based methods the former must already digitized. OCR technology (Optical Character Recognition) is very helpful in that regard, as it can be used to extract texts from images and documents. Repetitive, manual and paper-heavy business processes involving data extraction from documents are ideal for this. A typical case is the recognition and extraction of information from manually filled forms. This allows analog formats to be made available in digital form. Then, with the help of further AI methods, the efficiency of subsequent process steps can be increased.


For paper-heavy business processes, documents not only have to be transferred from analog to digital formats, but the information provided also has to be further processed based on context. To achieve this, documents must also be categorized. Categorization is another machine vision technology that allows images or data to be classified into categories based on certain features or characteristics. Besides the classic use case in document submission for checking the completeness of documents, this technology can also be used in the area of fraud detection. For example, categorizing transaction data makes it possible to analyze patterns and detect fraudulent activity.

Natural language processing/generation

Machine processing and generation of natural text is a classic AI application. There are many suitable use cases at the customer interface, as well as in the mid- and back-office. While the focus at the customer interface is on direct dialog, the focus in the mid- and back-office is on processing large amounts of text to extract information. These texts may include many different types of documents such as, for example, press releases, financial and business documents, e-mails, contracts or presentations.
A special type of classification is known as sentiment analysis. Such an analysis can give financial services providers valuable insights as to how their product, their company or a third party is perceived. Another application closely related to this is the generation of natural language text from structured data. The challenge in this application is to make the text not only informative, but also emotional. Currently, automated text generation is used where the focus is on quantity and efficiency rather than on particularly high quality.

Process mining

Each cycle of a process generates data on the process itself. These traces allow others to track the steps employees or customers have taken in the application system. Based on this real-world generated data, process mining allows the actual process cycles to be compared to the intended plan – which often differ quite a lot. This reveals where the bottlenecks in the process are. Ideally, in order to pursue a holistic approach, the process data should be merged and evaluated across system boundaries. This requires a comprehensive overview of all processes within the company. Furthermore, the processes should be well-structured and well-modeled, and each individual step should be available to be read out by the IT system. The most decisive success factors in that respect are the quality, variance and volume of the available data. Here, the old saying applies: a little is good, a lot is better.

Our training plan for processes

  1. Test bench: First, we want to get to know your organization and your goals: would you like to try out intelligent automation first or directly deploy it across the board? What pain points do you need to eliminate: high costs, long processing times, low process quality? What are the specific conditions at your institution?
  2. Process selection: Once we have a profound understanding of your situation, we can analyze which of your processes are well suited for automation using AI. To achieve this, we use our process automation map, which was developed specifically for this purpose.
  3. Solution approach: We help you select a suitable solution approach. Are the processes to be reorganized or automated? Which automation software should be used? Do AI models have to be developed in-house or can they be purchased?
  4. Implementation: The selected processes are then optimized. In doing so, we collaborate closely with your staff to achieve a knowledge transfer.
  5. Scaling: Are you satisfied with the outcome and ready to roll out intelligent process automation more widely? That makes two of us. We are happy to provide you with holistic support – from technology and personnel development to organization and governance.

Feel free to contact us

AI is not a question; it is the answer to profound changes in the market for financial services providers. Do you want to make your processes more efficient? We look forward to helping you tackle your challenges!