Back-office work is part of the daily bread of a company. The larger the company, the more time, employees, and costs the back-office work takes up. Especially if this work is done in manual processes. Therefore, back-office tasks are often outsourced to external service providers abroad, with which the company expects optimization in various forms, mainly through lower costs. However, outsourcing back-office work always comes with disadvantages. Why you should automate your back office work with machine learning-based (ML-based) OCR (Optical Character Recognition) instead and what advantages this has compared to outsourcing, you can read in this article.
The reasons for automation in the back-office are manifold
Disadvantages of outsourcing back-office work
Outsourcing can certainly still be a useful tool. However, if there are better alternatives for process optimization in the back-office, it is worth taking a closer look. With the automation of various back-office work through ML-based OCR, these very alternatives are available. Before we go into more detail on use case examples, let’s first take a look at a few disadvantages of outsourcing back-office tasks:
- Risk: Outsourcing data-heavy back-office work overseas always comes with some risk. Data protection and compliance have never been more important, and so the requirements (and handling of breaches) too
- Complexity: The complexity of outsourcing is often underestimated. Seamless integration of back-office work requires an organizational effort that should not be underestimated. Furthermore, there are ongoing controls regarding contract compliance, quality assurance, and others
- Dependency: Outsourcing always brings with it a certain dependency, which naturally comes with outsourcing
- High requirements: The requirements are constantly increasing, companies want to not only save costs with the outsourcing of back-office work but also achieve qualitative improvements and error minimization
- Scalability: Theoretically, back-office work can be scaled in outsourcing. In practice, however, this not only means a large increase in costs due to more employees but is also an illusion of the possibilities of manual versus automated scalability
With intelligent, machine learning-based OCR, the efficiency and productivity of back-office work can be taken to a new level
Why machine learning-based OCR?
Yes, why not make use of traditional OCR? The short answer is: Because back-office documents are far too complex and expensive for traditional OCR. Only intelligent, ML-based OCR can handle the high variation of unstructured documents. The longer answer to why and where ML-based OCR has clear advantages over traditional OCR is explained in more detail here.
Smaller risks and significantly reduced costs – just two of the benefits from automations in the back office
Advantages of automating back-office work
Compared to the disadvantages of outsourcing, the benefits of automation in back-office work using ML-based OCR trump massively:
- Risk: Transparency in processes is significantly higher with the automation of back-office work, which eases the high compliance requirements while mitigating risks
- Complexity: Since ML-based OCR can be easily integrated into existing systems, the complexity is low and the initial effort is comparatively small
- Dependency: Eliminates completely, as back-office work can always be monitored independently
- High requirements: Automations of back-office work know how to meet the high demands by keeping the quality high and the error rate to an absolute minimum
- Scalability: With automation, back-office work can be scaled infinitely, which allows for more efficient processes and enormous increases in productivity
Use cases for automation are desired around data-heavy back-office work – i.e., in most of them
Numerous use cases for the automation of back-office work
The advantages of automation over outsourcing are therefore clear. When it comes to which specific back-office work is a candidate for automation, it’s basically about data-heavy, repetitive tasks that involve entering, capturing, matching, and processing data. Thanks to intelligent, ML-based OCR, this includes complex and unstructured documents. Depending on the industry, use cases can vary, including the following back-office operations:
- Onboarding: Verifying customer data is one of the time-consuming back-office tasks that can be automated in a variety of industries, from insurance to real estate to banking
- Insurance: As a typical part of claims management, capturing claims data is tedious and can be automated
- Finance: Even complex accounting processes such as incoming invoice processing can be automated using intelligent ML-based OCR and its ability to capture and process highly unstructured documents
- Match and verify: The amount of back-office work involved in checking cashback campaigns, for example, is immense and can be automated
