Leading Swiss BPO significantly increases efficiency in document capture thanks to AI-based document separation.
Overview – Management Summary
By implementing automatic document separation with Parashift, a major specialist IT service provider in the Swiss market can significantly optimize document digitization.
The service provider is very familiar with the challenges involved in this process step and the associated optimization potential.
With the newly designed process based on AI procedures, the goals set were achieved. Automated document separation with Parashift saves costs and processing time.
Challenges
Every day, the specialist IT service provider processes thousands of documents for its customers. In this case, the documents enter the capture process in paper form. However, incoming digital documents also benefit from this functionality. The documents practically always enter the process in larger quantities (batches/files) and have to be separated.
In this case, incoming mail was separated manually by people on the screen after scanning in document capture software; in many other cases, documents are separated during scanning using barcodes affixed during document preparation (AVOR) or inserted barcode or patchcode separator sheets.
Both variants require a significant amount of manual work. In addition, incorrect handling can result in corrupt documents, and currently available tools for automatic separation have hardly become established due to their lack of reliability.
The solution
Automated document separation was implemented on the basis of GNNs (Graph Neural Networks), a powerful artificial intelligence tool for analyzing and predicting relationships between objects.
- Document awareness
Situational separation based on pages that are much earlier or later in the document.
- Multi-layered AI
Uses layout (textual) and images (visual)
- Contingency decision
Probability “First page” and “Last page” are used for separation suggestion of each page. (*Figure 1)
- High automation, little validation
Automation optimizes validation effort
- Continuous, autonomous learning
Learns continuously based on human feedback (validations and corrections)
- Simple configuration
Set an acceptable error rate (e.g. 2%) and leave the rest to the machine
The results
Saved costs, Optimized process,
More satisfied customers, Continuous further improvement
The implementation of automatic document separation with Parashift has led to significant cost savings for the service provider due to the significantly reduced manual effort.
As a result, throughput times and therefore the overall performance of document capture have been improved. The faster processing of incoming mail in turn led to higher customer satisfaction. Specifically, according to previous surveys, more than half (54%) of the documents have already been separated fully automatically. In this case, this corresponds to 82% of all pages recorded.
However, the remaining documents can also be separated more efficiently and quickly. The separations suggested by the AI can be easily and quickly confirmed or denied by the human validator. A nice side effect of this is that the system learns during validation and further improves the degree of automation through its use.
Interested? Contact us
Peter Zurflueh
Enterprise Sales Development Representative of Parashift
Phone: +41 79 252 88 50
peter.zurflueh@parashift.io
linkedin.com/peterzurflueh/
Related
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