Automate back-office work with machine learning-based OCR Philippe Jaggi Accounting & Business Services May 18, 2021 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 Start Trial Talk to us Digital Transformation Digitalization Document Capture Document Extraction Document Management Share on Facebook Share on Twitter
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